diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
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diff --git a/doc/LectureNotes/_build/.doctrees/exercisesweek37.doctree b/doc/LectureNotes/_build/.doctrees/exercisesweek37.doctree
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index 549aec666..18e8d142d 100644
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diff --git a/doc/LectureNotes/_build/.doctrees/week36.doctree b/doc/LectureNotes/_build/.doctrees/week36.doctree
index c117f6ca1..90607f78d 100644
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diff --git a/doc/LectureNotes/_build/html/_sources/exercisesweek37.ipynb b/doc/LectureNotes/_build/html/_sources/exercisesweek37.ipynb
index c565e7b07..39e531a60 100644
--- a/doc/LectureNotes/_build/html/_sources/exercisesweek37.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/exercisesweek37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cfebae03",
+   "id": "1ff4a4e9",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "353ebb07",
+   "id": "0569f7a9",
    "metadata": {
     "editable": true
    },
@@ -27,25 +27,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c02815dd",
+   "id": "6f34e507",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Overarching aims of the exercises this week\n",
     "\n",
-    "This exercise deals with various mean values and variances in  linear regression method (here it may be useful to look up chapter 3, equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570)). The exercise is also a part of project 1 and can be reused in the theory part of the project.\n",
+    "This exercise deals with various mean values and variances in linear\n",
+    "regression method (here it may be useful to look up chapter 3,\n",
+    "equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome\n",
+    "H. Friedman, The Elements of Statistical Learning,\n",
+    "Springer](https://www.springer.com/gp/book/9780387848570)). The\n",
+    "exercise is also a part of project 1 and can be reused in the theory\n",
+    "part of the project.\n",
     "\n",
-    "For more discussions on Ridge regression and calculation of expectation values, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
+    "For more discussions on Ridge regression and calculation of\n",
+    "expectation values, [Wessel van\n",
+    "Wieringen's](https://arxiv.org/abs/1509.09169) article is highly\n",
+    "recommended.\n",
     "\n",
-    "The assumption we have made is \n",
-    "that there exists a continuous function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0, \\sigma^2)$\n",
-    "which describes our data"
+    "The assumption we have made is that there exists a continuous function\n",
+    "$f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0,\n",
+    "\\sigma^2)$ which describes our data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb152043",
+   "id": "3ff574a4",
    "metadata": {
     "editable": true
    },
@@ -57,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aba7a46",
+   "id": "096a3165",
    "metadata": {
     "editable": true
    },
@@ -68,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be52c097",
+   "id": "cc9fb258",
    "metadata": {
     "editable": true
    },
@@ -80,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c54979e5",
+   "id": "daf4eb5d",
    "metadata": {
     "editable": true
    },
@@ -90,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b1eb811",
+   "id": "3b491679",
    "metadata": {
     "editable": true
    },
@@ -102,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "036a5440",
+   "id": "93a49739",
    "metadata": {
     "editable": true
    },
@@ -114,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7fb8094",
+   "id": "a3fa80db",
    "metadata": {
     "editable": true
    },
@@ -125,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7d92995",
+   "id": "e6e2defd",
    "metadata": {
     "editable": true
    },
@@ -137,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29eb9701",
+   "id": "9aa19afa",
    "metadata": {
     "editable": true
    },
@@ -150,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa106511",
+   "id": "5fa82a47",
    "metadata": {
     "editable": true
    },
@@ -162,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58be1091",
+   "id": "33099abb",
    "metadata": {
     "editable": true
    },
@@ -172,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cb58e41",
+   "id": "5d03f001",
    "metadata": {
     "editable": true
    },
@@ -184,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04df28d",
+   "id": "94bd151d",
    "metadata": {
     "editable": true
    },
@@ -195,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c095c96a",
+   "id": "f0b84941",
    "metadata": {
     "editable": true
    },
@@ -207,20 +216,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d425a79",
+   "id": "60370a63",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
+    "\\mathbb{E} \\big[ \\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
     "\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb89cfab",
+   "id": "af251217",
    "metadata": {
     "editable": true
    },
@@ -233,19 +242,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea8801c",
+   "id": "02e978da",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\mbox{Var}[\\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e885693",
+   "id": "3656a896",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/html/_sources/week36.ipynb b/doc/LectureNotes/_build/html/_sources/week36.ipynb
index bdaee795a..ededd9147 100644
--- a/doc/LectureNotes/_build/html/_sources/week36.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/week36.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7411173e",
+   "id": "2b900fea",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f50db913",
+   "id": "2286e211",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a90e380",
+   "id": "edbcd7f9",
    "metadata": {
     "editable": true
    },
@@ -44,28 +44,34 @@
     "\n",
     "* Material for the lecture on Thursday September 7\n",
     "\n",
-    "  * Linear Regression and links with Statistics, Resampling methods\n",
+    "  * Technicalities related to scaling and other issues with data handling\n",
     "\n",
-    "  * Recommended Reading: Goodfellow et al chapter 3 on probability theory, see URL:\"\"\n",
+    "  * Linear Regression and links with Statistics\n",
     "\n",
-    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)"
+    "  * [Recommended Reading: Goodfellow et al chapter 3 on probability theory](https://www.deeplearningbook.org/)\n",
+    "\n",
+    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)\n",
+    "\n",
+    "  * [Video of lecture](https://youtu.be/Kc20CFK0z7Y)  \n",
+    "\n",
+    "  * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep7.pdf)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "351d36fd",
+   "id": "5dbf3ff5",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Material for the active learning sessions Tuesday and Wednesday\n",
     "\n",
-    "The material here contains a summary from last Week and discussion of SVD, Ridge and Lasso regression with examples"
+    "The material here contains a summary from last week and discussion of SVD, Ridge and Lasso regression with examples"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0f9f4d2f",
+   "id": "2dae8175",
    "metadata": {
     "editable": true
    },
@@ -77,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ec36bdd",
+   "id": "fb3d486d",
    "metadata": {
     "editable": true
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@@ -89,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "614493c6",
+   "id": "5f8492a1",
    "metadata": {
     "editable": true
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@@ -99,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4be423",
+   "id": "a35422b4",
    "metadata": {
     "editable": true
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@@ -111,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b262332c",
+   "id": "aef923ea",
    "metadata": {
     "editable": true
    },
@@ -121,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1829a4",
+   "id": "5da8277b",
    "metadata": {
     "editable": true
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@@ -138,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c82dd9e",
+   "id": "eb34e530",
    "metadata": {
     "editable": true
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@@ -148,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bab8721",
+   "id": "a2a33728",
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     "editable": true
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@@ -160,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72273a4f",
+   "id": "9bea1837",
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     "editable": true
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@@ -170,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4af03ea",
+   "id": "af711505",
    "metadata": {
     "editable": true
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@@ -182,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0d35853",
+   "id": "f02b3738",
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@@ -196,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5ea827",
+   "id": "d3fe4855",
    "metadata": {
     "editable": true
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@@ -208,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6b341ce",
+   "id": "7b5fa376",
    "metadata": {
     "editable": true
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@@ -230,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db723709",
+   "id": "d212be4e",
    "metadata": {
     "editable": true
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@@ -242,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e600268",
+   "id": "eca13576",
    "metadata": {
     "editable": true
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@@ -257,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9452938",
+   "id": "fbf0e2be",
    "metadata": {
     "editable": true
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@@ -269,7 +275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53fea1e6",
+   "id": "acbd86ec",
    "metadata": {
     "editable": true
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@@ -281,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed10baad",
+   "id": "8887e338",
    "metadata": {
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@@ -291,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4d0db96",
+   "id": "e95bcbda",
    "metadata": {
     "editable": true
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@@ -303,7 +309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fae3cadc",
+   "id": "e66200c2",
    "metadata": {
     "editable": true
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@@ -313,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e973c775",
+   "id": "a0c3692c",
    "metadata": {
     "editable": true
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@@ -325,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f88a33f4",
+   "id": "8d13a9ff",
    "metadata": {
     "editable": true
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@@ -335,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bc7214d",
+   "id": "31c3dd91",
    "metadata": {
     "editable": true
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@@ -347,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "561d4c91",
+   "id": "87a6b291",
    "metadata": {
     "editable": true
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@@ -364,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eda2d1e",
+   "id": "3bb9eed3",
    "metadata": {
     "editable": true
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@@ -378,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c95dbc32",
+   "id": "ba123060",
    "metadata": {
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     "editable": true
@@ -392,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77079d25",
+   "id": "46fdfd39",
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@@ -403,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cd0ecc44",
+   "id": "36e47fde",
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@@ -443,7 +449,7 @@
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   {
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-   "id": "41d896dc",
+   "id": "53878874",
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@@ -463,7 +469,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f594852c",
+   "id": "87546753",
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@@ -475,7 +481,7 @@
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   {
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-   "id": "b78264f2",
+   "id": "d8a4669d",
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@@ -486,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d05eff95",
+   "id": "b6523ecb",
    "metadata": {
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@@ -520,7 +526,7 @@
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   {
    "cell_type": "markdown",
-   "id": "2fae32c6",
+   "id": "2fce573f",
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@@ -530,7 +536,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8631011a",
+   "id": "840991d3",
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@@ -543,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e7701de",
+   "id": "4df7aac6",
    "metadata": {
     "editable": true
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@@ -555,7 +561,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea7230b",
+   "id": "3f3acc05",
    "metadata": {
     "editable": true
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@@ -565,7 +571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5029213",
+   "id": "2d1218f6",
    "metadata": {
     "editable": true
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@@ -578,7 +584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "535ad70e",
+   "id": "01624e31",
    "metadata": {
     "editable": true
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@@ -588,7 +594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "159ae526",
+   "id": "616fe159",
    "metadata": {
     "editable": true
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@@ -600,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f25dfa4",
+   "id": "27cf2acd",
    "metadata": {
     "editable": true
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@@ -615,7 +621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d187806",
+   "id": "2ff1e0d2",
    "metadata": {
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@@ -628,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c692db5",
+   "id": "aef1996b",
    "metadata": {
     "editable": true
    },
@@ -642,7 +648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3404e0b4",
+   "id": "75a44cc9",
    "metadata": {
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@@ -654,7 +660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bc49666",
+   "id": "e2ba6037",
    "metadata": {
     "editable": true
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@@ -664,7 +670,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08dfa1",
+   "id": "95517053",
    "metadata": {
     "editable": true
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@@ -677,7 +683,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30bcc0cd",
+   "id": "5f15d4ea",
    "metadata": {
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@@ -689,7 +695,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d311a7d8",
+   "id": "68579bd0",
    "metadata": {
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@@ -701,7 +707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32c46b73",
+   "id": "53887938",
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@@ -713,7 +719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cceda87",
+   "id": "469c8c35",
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@@ -725,7 +731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10a16675",
+   "id": "a7521a38",
    "metadata": {
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@@ -739,7 +745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dba20bf",
+   "id": "cdfddb46",
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@@ -751,7 +757,7 @@
   },
   {
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-   "id": "9325f924",
+   "id": "e87c8606",
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@@ -761,7 +767,7 @@
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   {
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-   "id": "745d61e9",
+   "id": "3e06d524",
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@@ -773,7 +779,7 @@
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@@ -783,7 +789,7 @@
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@@ -795,7 +801,7 @@
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@@ -807,7 +813,7 @@
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   {
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@@ -817,7 +823,7 @@
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   {
    "cell_type": "markdown",
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+   "id": "eaba5f13",
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@@ -828,7 +834,7 @@
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   {
    "cell_type": "markdown",
-   "id": "46270996",
+   "id": "c9cc77c3",
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@@ -840,7 +846,7 @@
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   {
    "cell_type": "markdown",
-   "id": "785aebe9",
+   "id": "4b3e1232",
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@@ -856,7 +862,7 @@
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   {
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-   "id": "443f3a3f",
+   "id": "d59ed944",
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@@ -869,7 +875,7 @@
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   {
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-   "id": "cfb8d4c0",
+   "id": "e81dd0be",
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@@ -881,7 +887,7 @@
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   {
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-   "id": "2be1ca1f",
+   "id": "f26a385c",
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@@ -891,7 +897,7 @@
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   {
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-   "id": "9ed0aec2",
+   "id": "aa00d97a",
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@@ -903,7 +909,7 @@
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+   "id": "17ba8d1c",
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@@ -913,7 +919,7 @@
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   {
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-   "id": "4a8a6bff",
+   "id": "8cf9dc8f",
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@@ -925,7 +931,7 @@
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+   "id": "f0c43d20",
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@@ -937,7 +943,7 @@
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   {
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-   "id": "38e7b497",
+   "id": "5b119d73",
    "metadata": {
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@@ -953,7 +959,7 @@
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   {
    "cell_type": "markdown",
-   "id": "682f90db",
+   "id": "1efba158",
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@@ -965,7 +971,7 @@
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   {
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-   "id": "b5fb7771",
+   "id": "e5943a40",
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@@ -977,7 +983,7 @@
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   {
    "cell_type": "markdown",
-   "id": "0c228525",
+   "id": "167a1a9e",
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@@ -987,7 +993,7 @@
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   {
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-   "id": "08a400af",
+   "id": "924b5c9b",
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@@ -999,7 +1005,7 @@
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   {
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+   "id": "56ac0a65",
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@@ -1009,7 +1015,7 @@
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   {
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-   "id": "92d3d92f",
+   "id": "4323c860",
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@@ -1021,7 +1027,7 @@
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@@ -1038,7 +1044,7 @@
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@@ -1050,7 +1056,7 @@
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@@ -1062,7 +1068,7 @@
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@@ -1072,7 +1078,7 @@
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@@ -1084,7 +1090,7 @@
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@@ -1094,7 +1100,7 @@
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@@ -1106,7 +1112,7 @@
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@@ -1116,7 +1122,7 @@
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@@ -1128,7 +1134,7 @@
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@@ -1138,7 +1144,7 @@
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@@ -1153,7 +1159,7 @@
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@@ -1165,7 +1171,7 @@
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@@ -1175,7 +1181,7 @@
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@@ -1187,7 +1193,7 @@
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@@ -1199,7 +1205,7 @@
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@@ -1211,7 +1217,7 @@
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@@ -1221,7 +1227,7 @@
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@@ -1233,7 +1239,7 @@
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@@ -1245,7 +1251,7 @@
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@@ -1257,7 +1263,7 @@
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@@ -1267,7 +1273,7 @@
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@@ -1279,7 +1285,7 @@
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@@ -1289,7 +1295,7 @@
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@@ -1303,7 +1309,7 @@
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@@ -1313,7 +1319,7 @@
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@@ -1325,7 +1331,7 @@
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@@ -1337,7 +1343,7 @@
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@@ -1347,7 +1353,7 @@
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@@ -1359,7 +1365,7 @@
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@@ -1369,7 +1375,7 @@
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@@ -1381,7 +1387,7 @@
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@@ -1393,7 +1399,7 @@
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@@ -1403,7 +1409,7 @@
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@@ -1415,7 +1421,7 @@
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@@ -1425,7 +1431,7 @@
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@@ -1437,7 +1443,7 @@
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@@ -1449,7 +1455,7 @@
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@@ -1459,7 +1465,7 @@
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+   "id": "87542c6e",
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@@ -1471,7 +1477,7 @@
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+   "id": "13f97260",
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@@ -1484,7 +1490,7 @@
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+   "id": "bc02bc84",
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@@ -1496,7 +1502,7 @@
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+   "id": "9f9d8193",
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@@ -1508,7 +1514,7 @@
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   {
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+   "id": "ba404c5e",
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@@ -1520,7 +1526,7 @@
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+   "id": "9af7054a",
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@@ -1530,7 +1536,7 @@
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@@ -1542,7 +1548,7 @@
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+   "id": "041b60b2",
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@@ -1552,7 +1558,7 @@
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+   "id": "f95dc455",
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@@ -1564,7 +1570,7 @@
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+   "id": "28176b93",
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@@ -1574,7 +1580,7 @@
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@@ -1586,7 +1592,7 @@
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@@ -1596,7 +1602,7 @@
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@@ -1609,7 +1615,7 @@
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@@ -1621,7 +1627,7 @@
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@@ -1633,7 +1639,7 @@
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@@ -1643,7 +1649,7 @@
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@@ -1655,7 +1661,7 @@
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@@ -1672,7 +1678,7 @@
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@@ -1684,7 +1690,7 @@
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@@ -1696,7 +1702,7 @@
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@@ -1706,7 +1712,7 @@
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@@ -1718,7 +1724,7 @@
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@@ -1728,7 +1734,7 @@
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@@ -1740,7 +1746,7 @@
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@@ -1750,7 +1756,7 @@
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@@ -1762,7 +1768,7 @@
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@@ -1772,7 +1778,7 @@
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@@ -1787,7 +1793,7 @@
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-   "id": "92cd1361",
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@@ -1849,7 +1855,7 @@
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@@ -1859,7 +1865,7 @@
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@@ -1870,7 +1876,7 @@
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@@ -1937,7 +1943,7 @@
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@@ -1948,7 +1954,7 @@
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@@ -2047,962 +2053,850 @@
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    "source": [
-    "## Linking the regression analysis with a statistical interpretation\n",
+    "## Important technicalities: More on Rescaling data\n",
     "\n",
-    "We will now couple the discussions of ordinary least squares, Ridge\n",
-    "and Lasso regression with a statistical interpretation, that is we\n",
-    "move from a linear algebra analysis to a statistical analysis. In\n",
-    "particular, we will focus on what the regularization terms can result\n",
-    "in.  We will amongst other things show that the regularization\n",
-    "parameter can reduce considerably the variance of the parameters\n",
-    "$\\beta$.\n",
+    "When you are comparing your own code with for example **Scikit-Learn**'s\n",
+    "library, there are some technicalities to keep in mind.  The examples\n",
+    "here demonstrate some of these aspects with potential pitfalls.\n",
+    "\n",
+    "The discussion here focuses on the role of the intercept, how we can\n",
+    "set up the design matrix, what scaling we should use and other topics\n",
+    "which tend  confuse us.\n",
+    "\n",
+    "The intercept can be interpreted as the expected value of our\n",
+    "target/output variables when all other predictors are set to zero.\n",
+    "Thus, if we cannot assume that the expected outputs/targets are zero\n",
+    "when all predictors are zero (the columns in the design matrix), it\n",
+    "may be a bad idea to implement a model which penalizes the intercept.\n",
+    "Furthermore, in for example Ridge and Lasso regression, the default solutions\n",
+    "from the library **Scikit-Learn** (when not shrinking $\\beta_0$) for the unknown parameters\n",
+    "$\\boldsymbol{\\beta}$, are derived under the assumption that both $\\boldsymbol{y}$ and\n",
+    "$\\boldsymbol{X}$ are zero centered, that is we subtract the mean values.\n",
+    "\n",
+    "If our predictors represent different scales, then it is important to\n",
+    "standardize the design matrix $\\boldsymbol{X}$ by subtracting the mean of each\n",
+    "column from the corresponding column and dividing the column with its\n",
+    "standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,\n",
+    "the results may differ. \n",
     "\n",
     "The\n",
-    "advantage of doing linear regression is that we actually end up with\n",
-    "analytical expressions for several statistical quantities.  \n",
-    "Standard least squares and Ridge regression  allow us to\n",
-    "derive quantities like the variance and other expectation values in a\n",
-    "rather straightforward way.\n",
+    "[Standardscaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
+    "function in **Scikit-Learn** does this for us.  For the data sets we\n",
+    "have been studying in our various examples, the data are in many cases\n",
+    "already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform  a\n",
+    "survey of your data, with a critical assessment of them in case you need to scale the data.\n",
     "\n",
-    "It is assumed that $\\varepsilon_i\n",
-    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
-    "independent, i.e.:"
+    "If you need to scale the data, not doing so will give an *unfair*\n",
+    "penalization of the parameters since their magnitude depends on the\n",
+    "scale of their corresponding predictor.\n",
+    "\n",
+    "Suppose as an example that you \n",
+    "you have an input variable given by the heights of different persons.\n",
+    "Human height might be measured in inches or meters or\n",
+    "kilometers. If measured in kilometers, a standard linear regression\n",
+    "model with this predictor would probably give a much bigger\n",
+    "coefficient term, than if measured in millimeters.\n",
+    "This can clearly lead to problems in evaluating the cost/loss functions.\n",
+    "\n",
+    "Keep in mind that when you transform your data set before training a model, the same transformation needs to be done\n",
+    "on your eventual new data set  before making a prediction. If we translate this into a Python code, it would could be implemented as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e71daa5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "#Model training, we compute the mean value of y and X\n",
+    "y_train_mean = np.mean(y_train)\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "X_train = X_train - X_train_mean\n",
+    "y_train = y_train - y_train_mean\n",
+    "\n",
+    "# The we fit our model with the training data\n",
+    "trained_model = some_model.fit(X_train,y_train)\n",
+    "\n",
+    "\n",
+    "#Model prediction, we need also to transform our data set used for the prediction.\n",
+    "X_test = X_test - X_train_mean #Use mean from training data\n",
+    "y_pred = trained_model(X_test)\n",
+    "y_pred = y_pred + y_train_mean\n",
+    "\"\"\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e50205be",
+   "id": "1f0eb0d0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Let us try to understand what this may imply mathematically when we\n",
+    "subtract the mean values, also known as *zero centering*. For\n",
+    "simplicity, we will focus on  ordinary regression, as done in the above example.\n",
+    "\n",
+    "The cost/loss function  for regression is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ffc4acb",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
-    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
-    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
-    "\\end{align*}\n",
+    "C(\\beta_0, \\beta_1, ... , \\beta_{p-1}) = \\frac{1}{n}\\sum_{i=0}^{n} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij}\\beta_j\\right)^2,.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70eccda9",
+   "id": "2511c028",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The randomness of $\\varepsilon_i$ implies that\n",
-    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
-    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
-    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
-    "non-random scalar. To specify the parameters of the distribution of\n",
-    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "Recall also that we use the squared value. This expression can lead to an\n",
+    "increased penalty for higher differences between predicted and\n",
+    "output/target values.\n",
     "\n",
-    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
-    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
-    "row number $i$ and perform a sum over all values $p$."
+    "What we have done is to single out the $\\beta_0$ term in the\n",
+    "definition of the mean squared error (MSE).  The design matrix $X$\n",
+    "does in this case not contain any intercept column.  When we take the\n",
+    "derivative with respect to $\\beta_0$, we want the derivative to obey"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6bdcbf2",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Assumptions made\n",
-    "\n",
-    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
-    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
-    "which describe our data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d74471b8",
+   "id": "908beb7f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "\\frac{\\partial C}{\\partial \\beta_j} = 0,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f7b178d9",
+   "id": "5fae2a94",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
-    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+    "for all $j$. For $\\beta_0$ we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8ea16c62",
+   "id": "d7729a27",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "\\frac{\\partial C}{\\partial \\beta_0} = -\\frac{2}{n}\\sum_{i=0}^{n-1} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij} \\beta_j\\right).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a8d7bfd",
+   "id": "7fbb2ded",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Expectation value and variance\n",
-    "\n",
-    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+    "Multiplying away the constant $2/n$, we obtain"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5367d943",
+   "id": "8cef22ce",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mathbb{E}(y_i) & =\n",
-    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
-    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
-    "\\end{align*}\n",
+    "\\sum_{i=0}^{n-1} \\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} \\sum_{j=1}^{p-1} X_{ij} \\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7eefd85",
+   "id": "c38ab3a0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "while\n",
-    "its variance is"
+    "Let us specialize first to the case where we have only two parameters $\\beta_0$ and $\\beta_1$.\n",
+    "Our result for $\\beta_0$ simplifies then to"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27ce343a",
+   "id": "c47e7bbe",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
-    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
-    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
-    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
-    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
-    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
-    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
-    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
-    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
-    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
-    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
-    "\\end{align*}\n",
+    "n\\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} X_{i1} \\beta_1.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c67ea20b",
+   "id": "06962abd",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
-    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+    "We obtain then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b4da473a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
-    "\n",
-    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af812d38",
+   "id": "14c0a845",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9d3aaff5",
+   "id": "5f50f223",
    "metadata": {
     "editable": true
    },
    "source": [
-    "This means that the estimator of the regression parameters is unbiased.\n",
-    "\n",
-    "We can also calculate the variance\n",
-    "\n",
-    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+    "If we define"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eebea8b7",
+   "id": "188ed44d",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{eqnarray*}\n",
-    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
-    "\\\\\n",
-    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
-    "\\\\\n",
-    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
-    "% \\\\\n",
-    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
-    "\\\\\n",
-    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
-    "\\end{eqnarray*}\n",
+    "\\mu_{\\boldsymbol{x}_1}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "714f281a",
+   "id": "0b0db7b8",
    "metadata": {
     "editable": true
    },
    "source": [
-    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
-    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
-    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
-    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
-    "variance of the estimate of the $j$-th regression coefficient:\n",
-    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
-    "construct a confidence interval for the estimates.\n",
-    "\n",
-    "In a similar way, we can obtain analytical expressions for say the\n",
-    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
-    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
-    "\n",
-    "It is rather straightforward to show that"
+    "and the mean value of the outputs as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66015ca6",
+   "id": "e7550136",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "\\mu_y=\\frac{1}{n}\\sum_{i=0}^{n-1}y_i,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "267f01c0",
+   "id": "c7cc678d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We see clearly that \n",
-    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
-    "\n",
-    "We can also compute the variance as"
+    "we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "998de4b6",
+   "id": "9c9abcb2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\beta_0 = \\mu_y - \\beta_1\\mu_{\\boldsymbol{x}_1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "06a8d452",
+   "id": "2336bf55",
    "metadata": {
     "editable": true
    },
    "source": [
-    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
-    "\n",
-    "With this, we can compute the difference"
+    "In the general case with more parameters than $\\beta_0$ and $\\beta_1$, we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6cd3ed9b",
+   "id": "e0111655",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\frac{1}{n}\\sum_{i=0}^{n-1}\\sum_{j=1}^{p-1} X_{ij}\\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70f9aa69",
+   "id": "709c3af5",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The difference is non-negative definite since each component of the\n",
-    "matrix product is non-negative definite. \n",
-    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+    "We can rewrite the latter equation as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3bb18388",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Deriving OLS from a probability distribution\n",
-    "\n",
-    "Our basic assumption when we derived the OLS equations was to assume\n",
-    "that our output is determined by a given continuous function\n",
-    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
-    "distribution with zero mean value and an undetermined variance\n",
-    "$\\sigma^2$.\n",
-    "\n",
-    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
-    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
-    "the design matrix are not stochastic variables, we can assume that the\n",
-    "probability distribution of our targets is also a normal distribution\n",
-    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
-    "single output $y_i$ is given by the Gaussian distribution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84b546af",
+   "id": "a9ed060b",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\sum_{j=1}^{p-1} \\mu_{\\boldsymbol{x}_j}\\beta_j,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e90935e",
+   "id": "deb61dec",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Independent and Identically Distrubuted (iid)\n",
-    "\n",
-    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
-    "We define this distribution as"
+    "where we have defined"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b40b33f5",
+   "id": "dc60f20f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "\\mu_{\\boldsymbol{x}_j}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{ij},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "23e0c7ac",
+   "id": "b43a095d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "the mean value for all elements of the column vector $\\boldsymbol{x}_j$.\n",
     "\n",
-    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+    "Replacing $y_i$ with $y_i - y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dcd4ed9c",
+   "id": "050949a2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "C(\\boldsymbol{\\beta}) = (\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta})^T(\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta}).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c37adb5",
+   "id": "7c288c56",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
-    "in case we have a simple one-dimensional input and output case"
+    "If we minimize with respect to $\\boldsymbol{\\beta}$ we have then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27e4eea9",
+   "id": "72d25a93",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X})^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5dd95110",
+   "id": "6e4fe379",
    "metadata": {
     "editable": true
    },
    "source": [
-    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
-    "We can now rewrite the above probability as"
+    "where $\\boldsymbol{\\tilde{y}} = \\boldsymbol{y} - \\overline{\\boldsymbol{y}}$\n",
+    "and $\\tilde{X}_{ij} = X_{ij} - \\frac{1}{n}\\sum_{k=0}^{n-1}X_{kj}$.\n",
+    "\n",
+    "For Ridge regression we need to add $\\lambda \\boldsymbol{\\beta}^T\\boldsymbol{\\beta}$ to the cost function and get then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc7cfda7",
+   "id": "b0f0e6e7",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X} + \\lambda I)^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2bf6dbf4",
+   "id": "6cfb015d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+    "What does this mean? And why do we insist on all this? Let us look at some examples.\n",
+    "\n",
+    "This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (*code example thanks to  Øyvind Sigmundson Schøyen*). Here our scaling of the data is done by subtracting the mean values only.\n",
+    "Note also that we do not split the data into training and test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bfbf5d1d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n",
+    "np.random.seed(2021)\n",
+    "\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "\n",
+    "\n",
+    "def fit_beta(X, y):\n",
+    "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+    "\n",
+    "\n",
+    "true_beta = [2, 0.5, 3.7]\n",
+    "\n",
+    "x = np.linspace(0, 1, 11)\n",
+    "y = np.sum(\n",
+    "    np.asarray([x ** p * b for p, b in enumerate(true_beta)]), axis=0\n",
+    ") + 0.1 * np.random.normal(size=len(x))\n",
+    "\n",
+    "degree = 3\n",
+    "X = np.zeros((len(x), degree))\n",
+    "\n",
+    "# Include the intercept in the design matrix\n",
+    "for p in range(degree):\n",
+    "    X[:, p] = x ** p\n",
+    "\n",
+    "beta = fit_beta(X, y)\n",
+    "\n",
+    "# Intercept is included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=False).fit(X, y)\n",
+    "\n",
+    "print(f\"True beta: {true_beta}\")\n",
+    "print(f\"Fitted beta: {beta}\")\n",
+    "print(f\"Sklearn fitted beta: {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with intercept column\")\n",
+    "print(MSE(y,ypredictOwn))\n",
+    "print(f\"MSE with intercept column from SKL\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x, y, label=\"Data\")\n",
+    "plt.plot(x, X @ beta, label=\"Fit\")\n",
+    "plt.plot(x, skl.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
+    "\n",
+    "\n",
+    "# Do not include the intercept in the design matrix\n",
+    "X = np.zeros((len(x), degree - 1))\n",
+    "\n",
+    "for p in range(degree - 1):\n",
+    "    X[:, p] = x ** (p + 1)\n",
+    "\n",
+    "# Intercept is not included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=True).fit(X, y)\n",
+    "\n",
+    "# Use centered values for X and y when computing coefficients\n",
+    "y_offset = np.average(y, axis=0)\n",
+    "X_offset = np.average(X, axis=0)\n",
+    "\n",
+    "beta = fit_beta(X - X_offset, y - y_offset)\n",
+    "intercept = np.mean(y_offset - X_offset @ beta)\n",
+    "\n",
+    "print(f\"Manual intercept: {intercept}\")\n",
+    "print(f\"Fitted beta (wiothout intercept): {beta}\")\n",
+    "print(f\"Sklearn intercept: {skl.intercept_}\")\n",
+    "print(f\"Sklearn fitted beta (without intercept): {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with Manual intercept\")\n",
+    "print(MSE(y,ypredictOwn+intercept))\n",
+    "print(f\"MSE with Sklearn intercept\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "plt.plot(x, X @ beta + intercept, \"--\", label=\"Fit (manual intercept)\")\n",
+    "plt.plot(x, skl.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8ff9386",
+   "id": "7dc13bd7",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Maximum Likelihood Estimation (MLE)\n",
+    "The intercept is the value of our output/target variable\n",
+    "when all our features are zero and our function crosses the $y$-axis (for a one-dimensional case). \n",
     "\n",
-    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
-    "estimating the parameters of an assumed probability distribution,\n",
-    "given some observed data. This is achieved by maximizing a likelihood\n",
-    "function so that, under the assumed statistical model, the observed\n",
-    "data is the most probable. \n",
+    "Printing the MSE, we see first that both methods give the same MSE, as\n",
+    "they should.  However, when we move to for example Ridge regression,\n",
+    "the way we treat the intercept may give a larger or smaller MSE,\n",
+    "meaning that the MSE can be penalized by the value of the\n",
+    "intercept. Not including the intercept in the fit, means that the\n",
+    "regularization term does not include $\\beta_0$. For different values\n",
+    "of $\\lambda$, this may lead to different MSE values. \n",
     "\n",
-    "We will assume here that our events are given by the above Gaussian\n",
-    "distribution and we will determine the optimal parameters $\\beta$ by\n",
-    "maximizing the above PDF. However, computing the derivatives of a\n",
-    "product function is cumbersome and can easily lead to overflow and/or\n",
-    "underflowproblems, with potentials for loss of numerical precision.\n",
-    "\n",
-    "In practice, it is more convenient to maximize the logarithm of the\n",
-    "PDF because it is a monotonically increasing function of the argument.\n",
-    "Alternatively, and this will be our option, we will minimize the\n",
-    "negative of the logarithm since this is a monotonically decreasing\n",
-    "function.\n",
-    "\n",
-    "Note also that maximization/minimization of the logarithm of the PDF\n",
-    "is equivalent to the maximization/minimization of the function itself."
+    "To remind the reader, the regularization term, with the intercept in Ridge regression, is given by"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cd665e76",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## A new Cost Function\n",
-    "\n",
-    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "144c5c88",
+   "id": "3ce23f71",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=0}^{p-1}\\beta_j^2,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "76c7c5c6",
+   "id": "f34b4a50",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which becomes"
+    "but when we take out the intercept, this equation becomes"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f78405b0",
+   "id": "6c535531",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=1}^{p-1}\\beta_j^2.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd7b69b5",
+   "id": "dc9bd9cc",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+    "For Lasso regression we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a1430a20",
+   "id": "b5911fe2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_1 = \\lambda \\sum_{j=1}^{p-1}\\vert\\beta_j\\vert.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dfcd0c7b",
+   "id": "4d4103f1",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "043d37a6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28fe8edc",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b600d21c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## More basic Statistics and Bayes' theorem\n",
+    "It means that, when scaling the design matrix and the outputs/targets,\n",
+    "by subtracting the mean values, we have an optimization problem which\n",
+    "is not penalized by the intercept. The MSE value can then be smaller\n",
+    "since it focuses only on the remaining quantities. If we however bring\n",
+    "back the intercept, we will get a MSE which then contains the\n",
+    "intercept.\n",
     "\n",
-    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
-    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "Armed with this wisdom, we attempt first to simply set the intercept equal to **False** in our implementation of Ridge regression for our well-known  vanilla data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eecec942",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
     "\n",
-    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
     "\n",
-    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
-    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
     "\n",
-    "**Union of events is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d513cf2c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d18008b5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "**The product rule (aka joint probability) is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22f55ec4",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29e1bff6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(3155)\n",
     "\n",
-    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "333869a5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Marginal Probability\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf8de993",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c5349d1",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Conditional  Probability\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree))\n",
+    "#We include explicitely the intercept column\n",
+    "for degree in range(Maxpolydegree):\n",
+    "    X[:,degree] = x**degree\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "The conditional  probability, if $p(Y) > 0$, is"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbede038",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1334a9c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem\n",
+    "p = Maxpolydegree\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n",
+    "    # Note: we include the intercept column and no scaling\n",
+    "    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    # and then make the prediction\n",
+    "    ytildeOwnRidge = X_train @ OwnRidgeBeta\n",
+    "    ypredictOwnRidge = X_test @ OwnRidgeBeta\n",
+    "    ytildeRidge = RegRidge.predict(X_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta)\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
     "\n",
-    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac8dcf04",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1992e354",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which we can rewrite as"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24e5179f",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42979e5a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86a8cd07",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Interpretations of Bayes' Theorem\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')\n",
     "\n",
-    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
-    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
-    "the parameter space represented by $X$. This function is not\n",
-    "necesseraly normalized and is normally called the likelihood function.\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d67706b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The results here agree when we force **Scikit-Learn**'s Ridge function to include the first column in our design matrix.\n",
+    "We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.\n",
+    "What happens if we do not include the intercept in our fit?\n",
+    "Let us see how we can change this code by zero centering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "598d20bd",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(315)\n",
     "\n",
-    "Let us try to illustrate Bayes' theorem through an example."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9a4f9a9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Example of Usage of Bayes' theorem\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "Let us suppose that you are undergoing a series of mammography scans in\n",
-    "order to rule out possible breast cancer cases.  We define the\n",
-    "sensitivity for a positive event by the variable $X$. It takes binary\n",
-    "values with $X=1$ representing a positive event and $X=0$ being a\n",
-    "negative event. We reserve $Y$ as a classification parameter for\n",
-    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree-1))\n",
     "\n",
-    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "for degree in range(1,Maxpolydegree): #No intercept column\n",
+    "    X[:,degree-1] = x**(degree)\n",
     "\n",
-    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "888c64ec",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=1) =0.8.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5086b046",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
-    "It is however not correct, as the following Bayesian analysis shows."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "609ea2b0",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Doing it correctly\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
-    "Let us assume that the prior probability in the population as a whole is"
+    "#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "#Center by removing mean from each feature\n",
+    "X_train_scaled = X_train - X_train_mean \n",
+    "X_test_scaled = X_test - X_train_mean\n",
+    "#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)\n",
+    "#Remove the intercept from the training data.\n",
+    "y_scaler = np.mean(y_train)           \n",
+    "y_train_scaled = y_train - y_scaler   \n",
+    "\n",
+    "p = Maxpolydegree-1\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)\n",
+    "    intercept_ = y_scaler - X_train_mean@OwnRidgeBeta #The intercept can be shifted so the model can predict on uncentered data\n",
+    "    #Add intercept to prediction\n",
+    "    ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler \n",
+    "    RegRidge = linear_model.Ridge(lmb)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta) #Intercept is given by mean of target variable\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print('Intercept from own implementation:')\n",
+    "    print(intercept_)\n",
+    "    print('Intercept from Scikit-Learn Ridge implementation')\n",
+    "    print(RegRidge.intercept_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
+    "\n",
+    "\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "687bfdf3",
+   "id": "cec80003",
    "metadata": {
     "editable": true
    },
    "source": [
-    "$$\n",
-    "p(Y=1) =0.004.\n",
-    "$$"
+    "We see here, when compared to the code which includes explicitely the\n",
+    "intercept column, that our MSE value is actually smaller. This is\n",
+    "because the regularization term does not include the intercept value\n",
+    "$\\beta_0$ in the fitting.  This applies to Lasso regularization as\n",
+    "well.  It means that our optimization is now done only with the\n",
+    "centered matrix and/or vector that enter the fitting procedure."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c2af1d2d",
+   "id": "16cdcffb",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ec8d93",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=0) =0.1.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48a6fb5b",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba37d4fd",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "824f1789",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07f89fd5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "## Test Function for what happens with OLS, Ridge and Lasso\n",
     "\n",
     "Hitherto we have discussed Ridge and Lasso regression in terms of a\n",
     "linear analysis. This may to many of you feel rather technical and\n",
     "perhaps not that intuitive. The question is whether we can develop a\n",
     "more intuitive way of understanding what Ridge and Lasso express.\n",
     "\n",
-    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fcd73d9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Test Function for what happens with OLS, Ridge and Lasso\n",
+    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit. \n",
     "\n",
     "We will play around with a study of the values for the optimal\n",
     "parameters $\\boldsymbol{\\beta}$ using OLS, Ridge and Lasso regression.  For\n",
@@ -3015,8 +2909,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "4402e113",
+   "execution_count": 11,
+   "id": "e04dce1d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3043,10 +2937,9 @@
     "X = np.zeros((len(x),Maxpolydegree))\n",
     "X[:,0] = 1.0\n",
     "\n",
-    "for polydegree in range(1, Maxpolydegree):\n",
-    "    for degree in range(polydegree):\n",
-    "        X[:,degree] = x**(degree)\n",
     "\n",
+    "for polydegree in range(1,Maxpolydegree):\n",
+    "    X[:,polydegree] = x**(polydegree)\n",
     "\n",
     "# We split the data in test and training data\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
@@ -3090,7 +2983,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b700e45e",
+   "id": "2ae202c3",
    "metadata": {
     "editable": true
    },
@@ -3100,43 +2993,475 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69659d9d",
+   "id": "4186f9f0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Invoking Bayes' theorem\n",
+    "## Linking the regression analysis with a statistical interpretation\n",
     "\n",
-    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "We will now couple the discussions of ordinary least squares, Ridge\n",
+    "and Lasso regression with a statistical interpretation, that is we\n",
+    "move from a linear algebra analysis to a statistical analysis. In\n",
+    "particular, we will focus on what the regularization terms can result\n",
+    "in.  We will amongst other things show that the regularization\n",
+    "parameter can reduce considerably the variance of the parameters\n",
+    "$\\beta$.\n",
     "\n",
-    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+    "The\n",
+    "advantage of doing linear regression is that we actually end up with\n",
+    "analytical expressions for several statistical quantities.  \n",
+    "Standard least squares and Ridge regression  allow us to\n",
+    "derive quantities like the variance and other expectation values in a\n",
+    "rather straightforward way.\n",
+    "\n",
+    "It is assumed that $\\varepsilon_i\n",
+    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
+    "independent, i.e.:"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80b423eb",
+   "id": "7293d2ab",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "\\begin{align*} \n",
+    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
+    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
+    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
+    "\\end{align*}\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de547949",
+   "id": "efee3703",
    "metadata": {
     "editable": true
    },
    "source": [
-    "is given by"
+    "The randomness of $\\varepsilon_i$ implies that\n",
+    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
+    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
+    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
+    "non-random scalar. To specify the parameters of the distribution of\n",
+    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "\n",
+    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
+    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
+    "row number $i$ and perform a sum over all values $p$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "99d9476a",
+   "id": "85d2303d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Assumptions made\n",
+    "\n",
+    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
+    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
+    "which describe our data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1a189c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b63e61f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
+    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14acfe14",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681408c6",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance\n",
+    "\n",
+    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d956e33c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \n",
+    "\\mathbb{E}(y_i) & =\n",
+    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
+    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "992bee5b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "while\n",
+    "its variance is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd75d1af",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
+    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
+    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
+    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
+    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
+    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
+    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
+    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
+    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
+    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
+    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f702411d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
+    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b86e729d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
+    "\n",
+    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5af888a2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d21a65e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This means that the estimator of the regression parameters is unbiased.\n",
+    "\n",
+    "We can also calculate the variance\n",
+    "\n",
+    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d8043ab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{eqnarray*}\n",
+    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
+    "\\\\\n",
+    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
+    "\\\\\n",
+    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
+    "% \\\\\n",
+    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
+    "\\\\\n",
+    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
+    "\\end{eqnarray*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a126ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
+    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
+    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
+    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
+    "variance of the estimate of the $j$-th regression coefficient:\n",
+    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
+    "construct a confidence interval for the estimates.\n",
+    "\n",
+    "In a similar way, we can obtain analytical expressions for say the\n",
+    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
+    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
+    "\n",
+    "It is rather straightforward to show that"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7287992a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2803f793",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We see clearly that \n",
+    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
+    "\n",
+    "We can also compute the variance as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7443c20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d32eb0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
+    "\n",
+    "With this, we can compute the difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbd0143e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6de88a4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The difference is non-negative definite since each component of the\n",
+    "matrix product is non-negative definite. \n",
+    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55630c9f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Deriving OLS from a probability distribution\n",
+    "\n",
+    "Our basic assumption when we derived the OLS equations was to assume\n",
+    "that our output is determined by a given continuous function\n",
+    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
+    "distribution with zero mean value and an undetermined variance\n",
+    "$\\sigma^2$.\n",
+    "\n",
+    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
+    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
+    "the design matrix are not stochastic variables, we can assume that the\n",
+    "probability distribution of our targets is also a normal distribution\n",
+    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
+    "single output $y_i$ is given by the Gaussian distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745e6352",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec8c116",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Independent and Identically Distrubuted (iid)\n",
+    "\n",
+    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
+    "We define this distribution as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f92be14e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edbdd556",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "\n",
+    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a6903ca",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57680896",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
+    "in case we have a simple one-dimensional input and output case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9309640f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67fb3df9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
+    "We can now rewrite the above probability as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "894f0fb7",
    "metadata": {
     "editable": true
    },
@@ -3148,7 +3473,508 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa5613ea",
+   "id": "13f3f705",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf3edfa",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Maximum Likelihood Estimation (MLE)\n",
+    "\n",
+    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
+    "estimating the parameters of an assumed probability distribution,\n",
+    "given some observed data. This is achieved by maximizing a likelihood\n",
+    "function so that, under the assumed statistical model, the observed\n",
+    "data is the most probable. \n",
+    "\n",
+    "We will assume here that our events are given by the above Gaussian\n",
+    "distribution and we will determine the optimal parameters $\\beta$ by\n",
+    "maximizing the above PDF. However, computing the derivatives of a\n",
+    "product function is cumbersome and can easily lead to overflow and/or\n",
+    "underflowproblems, with potentials for loss of numerical precision.\n",
+    "\n",
+    "In practice, it is more convenient to maximize the logarithm of the\n",
+    "PDF because it is a monotonically increasing function of the argument.\n",
+    "Alternatively, and this will be our option, we will minimize the\n",
+    "negative of the logarithm since this is a monotonically decreasing\n",
+    "function.\n",
+    "\n",
+    "Note also that maximization/minimization of the logarithm of the PDF\n",
+    "is equivalent to the maximization/minimization of the function itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be42ac0c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## A new Cost Function\n",
+    "\n",
+    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fbab08",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ef1983",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which becomes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d6504da",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d8775c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "176d1dab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e20e33bf",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f73eae",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d2cf20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c32b8f92",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## More basic Statistics and Bayes' theorem\n",
+    "\n",
+    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
+    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "\n",
+    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "\n",
+    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
+    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
+    "\n",
+    "**Union of events is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3779fd2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e13b18",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**The product rule (aka joint probability) is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8b41f0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b22e26",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "\n",
+    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f6645d2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Marginal Probability\n",
+    "\n",
+    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7349fe65",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6205b4f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Conditional  Probability\n",
+    "\n",
+    "The conditional  probability, if $p(Y) > 0$, is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43cb130d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d8f1813",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem\n",
+    "\n",
+    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2746770",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3659d1d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which we can rewrite as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfd9ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f247ad1",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2615306",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Interpretations of Bayes' Theorem\n",
+    "\n",
+    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
+    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
+    "the parameter space represented by $X$. This function is not\n",
+    "necesseraly normalized and is normally called the likelihood function.\n",
+    "\n",
+    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "\n",
+    "Let us try to illustrate Bayes' theorem through an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91eae9e8",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Example of Usage of Bayes' theorem\n",
+    "\n",
+    "Let us suppose that you are undergoing a series of mammography scans in\n",
+    "order to rule out possible breast cancer cases.  We define the\n",
+    "sensitivity for a positive event by the variable $X$. It takes binary\n",
+    "values with $X=1$ representing a positive event and $X=0$ being a\n",
+    "negative event. We reserve $Y$ as a classification parameter for\n",
+    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "\n",
+    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "\n",
+    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76fe1c46",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=1) =0.8.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397d9768",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
+    "It is however not correct, as the following Bayesian analysis shows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c701f5",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Doing it correctly\n",
+    "\n",
+    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
+    "Let us assume that the prior probability in the population as a whole is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fdb2639",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1) =0.004.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ae9f95",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a681f1dc",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=0) =0.1.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2c6030",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67a1c582",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "969f368b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356a70c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "\n",
+    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "\n",
+    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa653f05",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5dd1081",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "is given by"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "731b21d4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "362ae4e2",
    "metadata": {
     "editable": true
    },
@@ -3158,7 +3984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dea4e11",
+   "id": "8ba56ac5",
    "metadata": {
     "editable": true
    },
@@ -3170,7 +3996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99ab1d57",
+   "id": "658e6364",
    "metadata": {
     "editable": true
    },
@@ -3180,7 +4006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "170ee68b",
+   "id": "28d6553d",
    "metadata": {
     "editable": true
    },
@@ -3192,7 +4018,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3388d6a4",
+   "id": "8f4ec5f0",
    "metadata": {
     "editable": true
    },
@@ -3202,7 +4028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5bdaa16",
+   "id": "e72b34ca",
    "metadata": {
     "editable": true
    },
@@ -3218,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbf8e7e6",
+   "id": "0e656652",
    "metadata": {
     "editable": true
    },
@@ -3230,7 +4056,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52c9e48f",
+   "id": "57606d08",
    "metadata": {
     "editable": true
    },
@@ -3240,7 +4066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ae37a89",
+   "id": "cf40e3a9",
    "metadata": {
     "editable": true
    },
@@ -3252,7 +4078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd8fee27",
+   "id": "65038f20",
    "metadata": {
     "editable": true
    },
@@ -3265,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de603b44",
+   "id": "deb9890c",
    "metadata": {
     "editable": true
    },
@@ -3277,7 +4103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08e4a0",
+   "id": "1a844813",
    "metadata": {
     "editable": true
    },
@@ -3287,7 +4113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b65ee2f0",
+   "id": "a32e4148",
    "metadata": {
     "editable": true
    },
@@ -3299,7 +4125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6526b426",
+   "id": "4050a8ed",
    "metadata": {
     "editable": true
    },
@@ -3309,7 +4135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29190fe5",
+   "id": "ec31e4e2",
    "metadata": {
     "editable": true
    },
@@ -3321,7 +4147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71b1585c",
+   "id": "f872c397",
    "metadata": {
     "editable": true
    },
@@ -3333,7 +4159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e18baaa9",
+   "id": "e8eeb2e5",
    "metadata": {
     "editable": true
    },
@@ -3343,7 +4169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c58b8db",
+   "id": "54d2f39d",
    "metadata": {
     "editable": true
    },
@@ -3355,7 +4181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "238ee8c5",
+   "id": "54cbc3a8",
    "metadata": {
     "editable": true
    },
@@ -3367,7 +4193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46389b82",
+   "id": "7ee728b6",
    "metadata": {
     "editable": true
    },
@@ -3379,7 +4205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ecbb2d2",
+   "id": "00e9a0eb",
    "metadata": {
     "editable": true
    },
@@ -3389,7 +4215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27368dfb",
+   "id": "50f72b22",
    "metadata": {
     "editable": true
    },
@@ -3401,7 +4227,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27082f48",
+   "id": "d53fea2e",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html
index e9f863c97..de203096b 100644
--- a/doc/LectureNotes/_build/html/exercisesweek37.html
+++ b/doc/LectureNotes/_build/html/exercisesweek37.html
@@ -438,11 +438,20 @@ doconce format html exercisesweek37.do.txt  -->
 <p>Date: <strong>Deadline is Sunday September 17 at midnight</strong></p>
 <div class="section" id="overarching-aims-of-the-exercises-this-week">
 <h2>Overarching aims of the exercises this week<a class="headerlink" href="#overarching-aims-of-the-exercises-this-week" title="Permalink to this headline">¶</a></h2>
-<p>This exercise deals with various mean values and variances in  linear regression method (here it may be useful to look up chapter 3, equation (3.8) of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer</a>). The exercise is also a part of project 1 and can be reused in the theory part of the project.</p>
-<p>For more discussions on Ridge regression and calculation of expectation values, <a class="reference external" href="https://arxiv.org/abs/1509.09169">Wessel van Wieringen’s</a> article is highly recommended.</p>
-<p>The assumption we have made is
-that there exists a continuous function <span class="math notranslate nohighlight">\(f(\boldsymbol{x})\)</span> and  a normal distributed error <span class="math notranslate nohighlight">\(\boldsymbol{\varepsilon}\sim N(0, \sigma^2)\)</span>
-which describes our data</p>
+<p>This exercise deals with various mean values and variances in linear
+regression method (here it may be useful to look up chapter 3,
+equation (3.8) of <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">Trevor Hastie, Robert Tibshirani, Jerome
+H. Friedman, The Elements of Statistical Learning,
+Springer</a>). The
+exercise is also a part of project 1 and can be reused in the theory
+part of the project.</p>
+<p>For more discussions on Ridge regression and calculation of
+expectation values, <a class="reference external" href="https://arxiv.org/abs/1509.09169">Wessel van
+Wieringen’s</a> article is highly
+recommended.</p>
+<p>The assumption we have made is that there exists a continuous function
+<span class="math notranslate nohighlight">\(f(\boldsymbol{x})\)</span> and a normal distributed error <span class="math notranslate nohighlight">\(\boldsymbol{\varepsilon}\sim N(0,
+\sigma^2)\)</span> which describes our data</p>
 <div class="math notranslate nohighlight">
 \[
 \boldsymbol{y} = f(\boldsymbol{x})+\boldsymbol{\varepsilon}
@@ -488,7 +497,7 @@ A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span>
 <p>Show that</p>
 <div class="math notranslate nohighlight">
 \[
-\mathbb{E} \big[ \boldsymbol{\beta}^{\mathrm{Ridge}} \big]=(\mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I}_{pp})^{-1} (\mathbf{X}^{\top} \mathbf{X})\
+\mathbb{E} \big[ \hat{\boldsymbol{\beta}}^{\mathrm{Ridge}} \big]=(\mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I}_{pp})^{-1} (\mathbf{X}^{\top} \mathbf{X})\
 \boldsymbol{\beta}^{\mathrm{OLS}}.
 \]</div>
 <p>We see clearly that
@@ -496,7 +505,7 @@ A given parameter <span class="math notranslate nohighlight">\(\beta_j\)</span>
 <p>Show also that the variance is</p>
 <div class="math notranslate nohighlight">
 \[
-\mbox{Var}[\boldsymbol{\beta}^{\mathrm{Ridge}}]=\sigma^2[  \mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I} ]^{-1}  \mathbf{X}^{T}\mathbf{X} \{ [  \mathbf{X}^{\top} \mathbf{X} + \lambda \mathbf{I} ]^{-1}\}^{T},
+\mbox{Var}[\hat{\boldsymbol{\beta}}^{\mathrm{Ridge}}]=\sigma^2[  \mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I} ]^{-1}  \mathbf{X}^{T}\mathbf{X} \{ [  \mathbf{X}^{\top} \mathbf{X} + \lambda \mathbf{I} ]^{-1}\}^{T},
 \]</div>
 <p>and it is easy to see that if the parameter <span class="math notranslate nohighlight">\(\lambda\)</span> goes to infinity then the variance of Ridge parameters <span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span> goes to zero.</p>
 </div>
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index dbe10ecb7..4db0feb6b 100644
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statitsitcal interpretations and Resampling 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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Statistical interpretation of Linear Regression and Resampling techniques","Week 37: Statitsitcal interpretations and Resampling 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\ No newline at end of file
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index b002b3156..11c1e9409 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -1646,7 +1646,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.996535469511469
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9952714635635723
 </pre></div>
 </div>
 </div>
@@ -1663,7 +1663,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.0077750600806588575
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009039197557276654
 </pre></div>
 </div>
 </div>
@@ -1678,23 +1678,31 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.05751737 0.02644748 0.02533184 0.02193035 0.02521356 0.00712899
- 0.00857688 0.0101339  0.01097227 0.01132947 0.01644452 0.01655455
- 0.00771046 0.05177021 0.01374437 0.04208274 0.01667873 0.00228986
- 0.00749579 0.00852965 0.01628823 0.0341899  0.01178253 0.00626339
- 0.00514913 0.00791019 0.00363986 0.00471966 0.00390805 0.00910241
- 0.00232255 0.02670606 0.03224523 0.01637147 0.00918914 0.02705154
- 0.00321694 0.01904583 0.0177181  0.00538287 0.02291121 0.01028255
- 0.04566103 0.00954257 0.00011551 0.0283589  0.00953728 0.03399521
- 0.01205286 0.02251524 0.00353655 0.02344605 0.05288681 0.01950574
- 0.00330907 0.01492855 0.01472766 0.03952093 0.01491299 0.00187154
- 0.02897168 0.00072037 0.00895315 0.02775293 0.01547992 0.04117184
- 0.0116726  0.03330335 0.01515966 0.0070381  0.01317074 0.00654702
- 0.00329264 0.02502101 0.00226569 0.05029894 0.01929643 0.03028379
- 0.053994   0.03413302 0.01854824 0.00393744 0.0600658  0.01855624
- 0.0590702  0.01826743 0.01039549 0.02151219 0.00928016 0.03536062
- 0.00503218 0.08526352 0.00506765 0.02609885 0.04771105 0.0010059
- 0.00659545 0.00143188 0.01489692 0.08237141]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[6.11430166e-02 7.59428035e-03 7.64295978e-03 5.34442654e-02
+ 2.53861506e-02 7.46543658e-02 2.79339933e-02 1.32946226e-02
+ 3.80320028e-03 2.40474117e-02 4.36028477e-02 3.24382528e-02
+ 1.26022420e-02 5.06848116e-03 1.96793569e-02 1.04826413e-04
+ 9.25580178e-03 1.80413812e-02 1.22519792e-02 1.01505087e-02
+ 2.67180442e-02 6.71316703e-02 7.68415876e-03 3.33174939e-02
+ 1.43570786e-02 8.43459933e-03 4.11046407e-03 9.10453697e-04
+ 4.33142087e-03 7.64507671e-02 2.19696497e-02 1.18460525e-02
+ 4.35665906e-02 3.62818572e-02 1.61018347e-03 3.78738205e-02
+ 4.57031517e-03 3.11279984e-02 1.07745809e-02 7.09848429e-03
+ 2.30006341e-03 4.62535124e-02 6.70493970e-02 6.26624267e-03
+ 4.23399553e-02 1.88690267e-02 7.46184816e-03 1.75934097e-02
+ 3.60820168e-02 1.09322328e-02 3.16749353e-02 2.30626515e-02
+ 5.76196895e-04 1.51000420e-02 1.95456434e-03 1.22138768e-02
+ 2.52945186e-03 2.12804129e-03 3.32695433e-03 1.90886410e-02
+ 1.84291049e-02 1.78104004e-02 6.34498455e-02 1.50032818e-02
+ 3.43525003e-02 1.52033394e-03 3.95921014e-02 7.67148008e-02
+ 2.40893740e-02 6.75638425e-02 1.22105472e-02 7.72706640e-03
+ 3.00721135e-02 8.77081499e-02 1.40684484e-02 4.57081718e-02
+ 3.51190686e-02 7.42092776e-02 4.03105744e-02 1.08994018e-02
+ 1.37071826e-02 3.69106251e-02 1.94966662e-03 1.71112140e-02
+ 3.12669602e-03 3.01610677e-02 1.72139847e-02 2.75703556e-02
+ 2.23333773e-02 2.76892410e-03 8.05886306e-02 1.15545946e-01
+ 4.88029792e-02 4.59648068e-02 1.98750794e-02 2.69178261e-03
+ 1.07925220e-02 1.13151043e-03 5.00620642e-03 3.69871367e-03]
 </pre></div>
 </div>
 </div>
@@ -1763,15 +1771,15 @@ but now splitting the data into a training set and a test set.</p>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.95976303  0.48974963  2.82202765  3.79224555 -2.14248744]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.04038959 -0.81343612  7.94304103 -3.83770135  1.70073443]
 Training R2
-0.9947613223847728
+0.9954441212986554
 Training MSE
-0.010596520060222926
+0.008653750612762521
 Test R2
-0.989645675273948
+0.99255249100346
 Test MSE
-0.01889099091713886
+0.016970019460234076
 </pre></div>
 </div>
 </div>
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 3bad39527..d9983eaef 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -519,6 +519,16 @@ const thebe_selector_output = ".output, .cell_output"
    Material for lecture Thursday September 7
   </a>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#important-technicalities-more-on-rescaling-data">
+   Important technicalities: More on Rescaling data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#test-function-for-what-happens-with-ols-ridge-and-lasso">
+   Test Function for what happens with OLS, Ridge and Lasso
+  </a>
+ </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linking-the-regression-analysis-with-a-statistical-interpretation">
    Linking the regression analysis with a statistical interpretation
@@ -602,16 +612,6 @@ const thebe_selector_output = ".output, .cell_output"
    Bayes’ Theorem and Ridge and Lasso Regression
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#test-function-for-what-happens-with-ols-ridge-and-lasso">
-   Test Function for what happens with OLS, Ridge and Lasso
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#invoking-bayes-theorem">
-   Invoking Bayes’ theorem
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ridge-and-bayes">
    Ridge and Bayes
@@ -790,6 +790,16 @@ const thebe_selector_output = ".output, .cell_output"
    Material for lecture Thursday September 7
   </a>
  </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#important-technicalities-more-on-rescaling-data">
+   Important technicalities: More on Rescaling data
+  </a>
+ </li>
+ <li class="toc-h2 nav-item toc-entry">
+  <a class="reference internal nav-link" href="#test-function-for-what-happens-with-ols-ridge-and-lasso">
+   Test Function for what happens with OLS, Ridge and Lasso
+  </a>
+ </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#linking-the-regression-analysis-with-a-statistical-interpretation">
    Linking the regression analysis with a statistical interpretation
@@ -873,16 +883,6 @@ const thebe_selector_output = ".output, .cell_output"
    Bayes’ Theorem and Ridge and Lasso Regression
   </a>
  </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#test-function-for-what-happens-with-ols-ridge-and-lasso">
-   Test Function for what happens with OLS, Ridge and Lasso
-  </a>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#invoking-bayes-theorem">
-   Invoking Bayes’ theorem
-  </a>
- </li>
  <li class="toc-h2 nav-item toc-entry">
   <a class="reference internal nav-link" href="#ridge-and-bayes">
    Ridge and Bayes
@@ -920,16 +920,19 @@ doconce format html week36.do.txt --no_mako -->
 </li>
 <li><p>Material for the lecture on Thursday September 7</p>
 <ul>
-<li><p>Linear Regression and links with Statistics, Resampling methods</p></li>
-<li><p>Recommended Reading: Goodfellow et al chapter 3 on probability theory, see URL:””</p></li>
+<li><p>Technicalities related to scaling and other issues with data handling</p></li>
+<li><p>Linear Regression and links with Statistics</p></li>
+<li><p><a class="reference external" href="https://www.deeplearningbook.org/">Recommended Reading: Goodfellow et al chapter 3 on probability theory</a></p></li>
 <li><p>See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)</p></li>
+<li><p><a class="reference external" href="https://youtu.be/Kc20CFK0z7Y">Video of lecture</a></p></li>
+<li><p><a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep7.pdf">Whiteboard notes</a></p></li>
 </ul>
 </li>
 </ul>
 </div>
 <div class="section" id="material-for-the-active-learning-sessions-tuesday-and-wednesday">
 <h2>Material for the active learning sessions Tuesday and Wednesday<a class="headerlink" href="#material-for-the-active-learning-sessions-tuesday-and-wednesday" title="Permalink to this headline">¶</a></h2>
-<p>The material here contains a summary from last Week and discussion of SVD, Ridge and Lasso regression with examples</p>
+<p>The material here contains a summary from last week and discussion of SVD, Ridge and Lasso regression with examples</p>
 </div>
 <div class="section" id="linear-regression-and-the-svd">
 <h2>Linear Regression and  the SVD<a class="headerlink" href="#linear-regression-and-the-svd" title="Permalink to this headline">¶</a></h2>
@@ -1700,6 +1703,531 @@ C(\boldsymbol{\beta})=(4-2\beta_0)^2+(2-\beta_1)^2+\lambda(\vert\beta_0\vert+\ve
 <div class="section" id="material-for-lecture-thursday-september-7">
 <h2>Material for lecture Thursday September 7<a class="headerlink" href="#material-for-lecture-thursday-september-7" title="Permalink to this headline">¶</a></h2>
 </div>
+<div class="section" id="important-technicalities-more-on-rescaling-data">
+<h2>Important technicalities: More on Rescaling data<a class="headerlink" href="#important-technicalities-more-on-rescaling-data" title="Permalink to this headline">¶</a></h2>
+<p>When you are comparing your own code with for example <strong>Scikit-Learn</strong>’s
+library, there are some technicalities to keep in mind.  The examples
+here demonstrate some of these aspects with potential pitfalls.</p>
+<p>The discussion here focuses on the role of the intercept, how we can
+set up the design matrix, what scaling we should use and other topics
+which tend  confuse us.</p>
+<p>The intercept can be interpreted as the expected value of our
+target/output variables when all other predictors are set to zero.
+Thus, if we cannot assume that the expected outputs/targets are zero
+when all predictors are zero (the columns in the design matrix), it
+may be a bad idea to implement a model which penalizes the intercept.
+Furthermore, in for example Ridge and Lasso regression, the default solutions
+from the library <strong>Scikit-Learn</strong> (when not shrinking <span class="math notranslate nohighlight">\(\beta_0\)</span>) for the unknown parameters
+<span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span>, are derived under the assumption that both <span class="math notranslate nohighlight">\(\boldsymbol{y}\)</span> and
+<span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> are zero centered, that is we subtract the mean values.</p>
+<p>If our predictors represent different scales, then it is important to
+standardize the design matrix <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span> by subtracting the mean of each
+column from the corresponding column and dividing the column with its
+standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,
+the results may differ.</p>
+<p>The
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">Standardscaler</a>
+function in <strong>Scikit-Learn</strong> does this for us.  For the data sets we
+have been studying in our various examples, the data are in many cases
+already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform  a
+survey of your data, with a critical assessment of them in case you need to scale the data.</p>
+<p>If you need to scale the data, not doing so will give an <em>unfair</em>
+penalization of the parameters since their magnitude depends on the
+scale of their corresponding predictor.</p>
+<p>Suppose as an example that you
+you have an input variable given by the heights of different persons.
+Human height might be measured in inches or meters or
+kilometers. If measured in kilometers, a standard linear regression
+model with this predictor would probably give a much bigger
+coefficient term, than if measured in millimeters.
+This can clearly lead to problems in evaluating the cost/loss functions.</p>
+<p>Keep in mind that when you transform your data set before training a model, the same transformation needs to be done
+on your eventual new data set  before making a prediction. If we translate this into a Python code, it would could be implemented as</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">#Model training, we compute the mean value of y and X</span>
+<span class="sd">y_train_mean = np.mean(y_train)</span>
+<span class="sd">X_train_mean = np.mean(X_train,axis=0)</span>
+<span class="sd">X_train = X_train - X_train_mean</span>
+<span class="sd">y_train = y_train - y_train_mean</span>
+
+<span class="sd"># The we fit our model with the training data</span>
+<span class="sd">trained_model = some_model.fit(X_train,y_train)</span>
+
+
+<span class="sd">#Model prediction, we need also to transform our data set used for the prediction.</span>
+<span class="sd">X_test = X_test - X_train_mean #Use mean from training data</span>
+<span class="sd">y_pred = trained_model(X_test)</span>
+<span class="sd">y_pred = y_pred + y_train_mean</span>
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>Let us try to understand what this may imply mathematically when we
+subtract the mean values, also known as <em>zero centering</em>. For
+simplicity, we will focus on  ordinary regression, as done in the above example.</p>
+<p>The cost/loss function  for regression is</p>
+<div class="math notranslate nohighlight">
+\[
+C(\beta_0, \beta_1, ... , \beta_{p-1}) = \frac{1}{n}\sum_{i=0}^{n} \left(y_i - \beta_0 - \sum_{j=1}^{p-1} X_{ij}\beta_j\right)^2,.
+\]</div>
+<p>Recall also that we use the squared value. This expression can lead to an
+increased penalty for higher differences between predicted and
+output/target values.</p>
+<p>What we have done is to single out the <span class="math notranslate nohighlight">\(\beta_0\)</span> term in the
+definition of the mean squared error (MSE).  The design matrix <span class="math notranslate nohighlight">\(X\)</span>
+does in this case not contain any intercept column.  When we take the
+derivative with respect to <span class="math notranslate nohighlight">\(\beta_0\)</span>, we want the derivative to obey</p>
+<div class="math notranslate nohighlight">
+\[
+\frac{\partial C}{\partial \beta_j} = 0,
+\]</div>
+<p>for all <span class="math notranslate nohighlight">\(j\)</span>. For <span class="math notranslate nohighlight">\(\beta_0\)</span> we have</p>
+<div class="math notranslate nohighlight">
+\[
+\frac{\partial C}{\partial \beta_0} = -\frac{2}{n}\sum_{i=0}^{n-1} \left(y_i - \beta_0 - \sum_{j=1}^{p-1} X_{ij} \beta_j\right).
+\]</div>
+<p>Multiplying away the constant <span class="math notranslate nohighlight">\(2/n\)</span>, we obtain</p>
+<div class="math notranslate nohighlight">
+\[
+\sum_{i=0}^{n-1} \beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} \sum_{j=1}^{p-1} X_{ij} \beta_j.
+\]</div>
+<p>Let us specialize first to the case where we have only two parameters <span class="math notranslate nohighlight">\(\beta_0\)</span> and <span class="math notranslate nohighlight">\(\beta_1\)</span>.
+Our result for <span class="math notranslate nohighlight">\(\beta_0\)</span> simplifies then to</p>
+<div class="math notranslate nohighlight">
+\[
+n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1.
+\]</div>
+<p>We obtain then</p>
+<div class="math notranslate nohighlight">
+\[
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} X_{i1}.
+\]</div>
+<p>If we define</p>
+<div class="math notranslate nohighlight">
+\[
+\mu_{\boldsymbol{x}_1}=\frac{1}{n}\sum_{i=0}^{n-1} X_{i1},
+\]</div>
+<p>and the mean value of the outputs as</p>
+<div class="math notranslate nohighlight">
+\[
+\mu_y=\frac{1}{n}\sum_{i=0}^{n-1}y_i,
+\]</div>
+<p>we have</p>
+<div class="math notranslate nohighlight">
+\[
+\beta_0 = \mu_y - \beta_1\mu_{\boldsymbol{x}_1}.
+\]</div>
+<p>In the general case with more parameters than <span class="math notranslate nohighlight">\(\beta_0\)</span> and <span class="math notranslate nohighlight">\(\beta_1\)</span>, we have</p>
+<div class="math notranslate nohighlight">
+\[
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \frac{1}{n}\sum_{i=0}^{n-1}\sum_{j=1}^{p-1} X_{ij}\beta_j.
+\]</div>
+<p>We can rewrite the latter equation as</p>
+<div class="math notranslate nohighlight">
+\[
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \sum_{j=1}^{p-1} \mu_{\boldsymbol{x}_j}\beta_j,
+\]</div>
+<p>where we have defined</p>
+<div class="math notranslate nohighlight">
+\[
+\mu_{\boldsymbol{x}_j}=\frac{1}{n}\sum_{i=0}^{n-1} X_{ij},
+\]</div>
+<p>the mean value for all elements of the column vector <span class="math notranslate nohighlight">\(\boldsymbol{x}_j\)</span>.</p>
+<p>Replacing <span class="math notranslate nohighlight">\(y_i\)</span> with <span class="math notranslate nohighlight">\(y_i - y_i - \overline{\boldsymbol{y}}\)</span> and centering also our design matrix results in a cost function (in vector-matrix disguise)</p>
+<div class="math notranslate nohighlight">
+\[
+C(\boldsymbol{\beta}) = (\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta})^T(\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta}).
+\]</div>
+<p>If we minimize with respect to <span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span> we have then</p>
+<div class="math notranslate nohighlight">
+\[
+\hat{\boldsymbol{\beta}} = (\tilde{X}^T\tilde{X})^{-1}\tilde{X}^T\boldsymbol{\tilde{y}},
+\]</div>
+<p>where <span class="math notranslate nohighlight">\(\boldsymbol{\tilde{y}} = \boldsymbol{y} - \overline{\boldsymbol{y}}\)</span>
+and <span class="math notranslate nohighlight">\(\tilde{X}_{ij} = X_{ij} - \frac{1}{n}\sum_{k=0}^{n-1}X_{kj}\)</span>.</p>
+<p>For Ridge regression we need to add <span class="math notranslate nohighlight">\(\lambda \boldsymbol{\beta}^T\boldsymbol{\beta}\)</span> to the cost function and get then</p>
+<div class="math notranslate nohighlight">
+\[
+\hat{\boldsymbol{\beta}} = (\tilde{X}^T\tilde{X} + \lambda I)^{-1}\tilde{X}^T\boldsymbol{\tilde{y}}.
+\]</div>
+<p>What does this mean? And why do we insist on all this? Let us look at some examples.</p>
+<p>This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (<em>code example thanks to  Øyvind Sigmundson Schøyen</em>). Here our scaling of the data is done by subtracting the mean values only.
+Note also that we do not split the data into training and test.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
+
+
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">2021</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
+
+
+<span class="k">def</span> <span class="nf">fit_beta</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X</span><span class="p">)</span> <span class="o">@</span> <span class="n">X</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y</span>
+
+
+<span class="n">true_beta</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.7</span><span class="p">]</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">x</span> <span class="o">**</span> <span class="n">p</span> <span class="o">*</span> <span class="n">b</span> <span class="k">for</span> <span class="n">p</span><span class="p">,</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">true_beta</span><span class="p">)]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+<span class="p">)</span> <span class="o">+</span> <span class="mf">0.1</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+
+<span class="n">degree</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">degree</span><span class="p">))</span>
+
+<span class="c1"># Include the intercept in the design matrix</span>
+<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">degree</span><span class="p">):</span>
+    <span class="n">X</span><span class="p">[:,</span> <span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="o">**</span> <span class="n">p</span>
+
+<span class="n">beta</span> <span class="o">=</span> <span class="n">fit_beta</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="c1"># Intercept is included in the design matrix</span>
+<span class="n">skl</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;True beta: </span><span class="si">{</span><span class="n">true_beta</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted beta: </span><span class="si">{</span><span class="n">beta</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sklearn fitted beta: </span><span class="si">{</span><span class="n">skl</span><span class="o">.</span><span class="n">coef_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">ypredictOwn</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
+<span class="n">ypredictSKL</span> <span class="o">=</span> <span class="n">skl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;MSE with intercept column&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">ypredictOwn</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;MSE with intercept column from SKL&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">ypredictSKL</span><span class="p">))</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Data&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Fit&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">skl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sklearn (fit_intercept=False)&quot;</span><span class="p">)</span>
+
+
+<span class="c1"># Do not include the intercept in the design matrix</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">degree</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">degree</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+    <span class="n">X</span><span class="p">[:,</span> <span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span> <span class="o">**</span> <span class="p">(</span><span class="n">p</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Intercept is not included in the design matrix</span>
+<span class="n">skl</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">(</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="c1"># Use centered values for X and y when computing coefficients</span>
+<span class="n">y_offset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">X_offset</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">beta</span> <span class="o">=</span> <span class="n">fit_beta</span><span class="p">(</span><span class="n">X</span> <span class="o">-</span> <span class="n">X_offset</span><span class="p">,</span> <span class="n">y</span> <span class="o">-</span> <span class="n">y_offset</span><span class="p">)</span>
+<span class="n">intercept</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_offset</span> <span class="o">-</span> <span class="n">X_offset</span> <span class="o">@</span> <span class="n">beta</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Manual intercept: </span><span class="si">{</span><span class="n">intercept</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Fitted beta (wiothout intercept): </span><span class="si">{</span><span class="n">beta</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sklearn intercept: </span><span class="si">{</span><span class="n">skl</span><span class="o">.</span><span class="n">intercept_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Sklearn fitted beta (without intercept): </span><span class="si">{</span><span class="n">skl</span><span class="o">.</span><span class="n">coef_</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">ypredictOwn</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span>
+<span class="n">ypredictSKL</span> <span class="o">=</span> <span class="n">skl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;MSE with Manual intercept&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">ypredictOwn</span><span class="o">+</span><span class="n">intercept</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;MSE with Sklearn intercept&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">ypredictSKL</span><span class="p">))</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">X</span> <span class="o">@</span> <span class="n">beta</span> <span class="o">+</span> <span class="n">intercept</span><span class="p">,</span> <span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Fit (manual intercept)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">skl</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sklearn (fit_intercept=True)&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The intercept is the value of our output/target variable
+when all our features are zero and our function crosses the <span class="math notranslate nohighlight">\(y\)</span>-axis (for a one-dimensional case).</p>
+<p>Printing the MSE, we see first that both methods give the same MSE, as
+they should.  However, when we move to for example Ridge regression,
+the way we treat the intercept may give a larger or smaller MSE,
+meaning that the MSE can be penalized by the value of the
+intercept. Not including the intercept in the fit, means that the
+regularization term does not include <span class="math notranslate nohighlight">\(\beta_0\)</span>. For different values
+of <span class="math notranslate nohighlight">\(\lambda\)</span>, this may lead to different MSE values.</p>
+<p>To remind the reader, the regularization term, with the intercept in Ridge regression, is given by</p>
+<div class="math notranslate nohighlight">
+\[
+\lambda \vert\vert \boldsymbol{\beta} \vert\vert_2^2 = \lambda \sum_{j=0}^{p-1}\beta_j^2,
+\]</div>
+<p>but when we take out the intercept, this equation becomes</p>
+<div class="math notranslate nohighlight">
+\[
+\lambda \vert\vert \boldsymbol{\beta} \vert\vert_2^2 = \lambda \sum_{j=1}^{p-1}\beta_j^2.
+\]</div>
+<p>For Lasso regression we have</p>
+<div class="math notranslate nohighlight">
+\[
+\lambda \vert\vert \boldsymbol{\beta} \vert\vert_1 = \lambda \sum_{j=1}^{p-1}\vert\beta_j\vert.
+\]</div>
+<p>It means that, when scaling the design matrix and the outputs/targets,
+by subtracting the mean values, we have an optimization problem which
+is not penalized by the intercept. The MSE value can then be smaller
+since it focuses only on the remaining quantities. If we however bring
+back the intercept, we will get a MSE which then contains the
+intercept.</p>
+<p>Armed with this wisdom, we attempt first to simply set the intercept equal to <strong>False</strong> in our implementation of Ridge regression for our well-known  vanilla data set.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
+
+<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
+
+
+<span class="c1"># A seed just to ensure that the random numbers are the same for every run.</span>
+<span class="c1"># Useful for eventual debugging.</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">3155</span><span class="p">)</span>
+
+<span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="n">Maxpolydegree</span> <span class="o">=</span> <span class="mi">20</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span><span class="n">Maxpolydegree</span><span class="p">))</span>
+<span class="c1">#We include explicitely the intercept column</span>
+<span class="k">for</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Maxpolydegree</span><span class="p">):</span>
+    <span class="n">X</span><span class="p">[:,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="n">degree</span>
+<span class="c1"># We split the data in test and training data</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+
+<span class="n">p</span> <span class="o">=</span> <span class="n">Maxpolydegree</span>
+<span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">p</span><span class="p">,</span><span class="n">p</span><span class="p">)</span>
+<span class="c1"># Decide which values of lambda to use</span>
+<span class="n">nlambdas</span> <span class="o">=</span> <span class="mi">6</span>
+<span class="n">MSEOwnRidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+<span class="n">MSERidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+<span class="n">lambdas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">nlambdas</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">):</span>
+    <span class="n">lmb</span> <span class="o">=</span> <span class="n">lambdas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">OwnRidgeBeta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_train</span><span class="o">+</span><span class="n">lmb</span><span class="o">*</span><span class="n">I</span><span class="p">)</span> <span class="o">@</span> <span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y_train</span>
+    <span class="c1"># Note: we include the intercept column and no scaling</span>
+    <span class="n">RegRidge</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Ridge</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">RegRidge</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+    <span class="c1"># and then make the prediction</span>
+    <span class="n">ytildeOwnRidge</span> <span class="o">=</span> <span class="n">X_train</span> <span class="o">@</span> <span class="n">OwnRidgeBeta</span>
+    <span class="n">ypredictOwnRidge</span> <span class="o">=</span> <span class="n">X_test</span> <span class="o">@</span> <span class="n">OwnRidgeBeta</span>
+    <span class="n">ytildeRidge</span> <span class="o">=</span> <span class="n">RegRidge</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
+    <span class="n">ypredictRidge</span> <span class="o">=</span> <span class="n">RegRidge</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+    <span class="n">MSEOwnRidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictOwnRidge</span><span class="p">)</span>
+    <span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictRidge</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Beta values for own Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">OwnRidgeBeta</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Beta values for Scikit-Learn Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">RegRidge</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE values for own Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">MSEOwnRidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE values for Scikit-Learn Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+<span class="c1"># Now plot the results</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSEOwnRidgePredict</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE own Ridge Test&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSERidgePredict</span><span class="p">,</span> <span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE Ridge Test&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;log10(lambda)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;MSE&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>The results here agree when we force <strong>Scikit-Learn</strong>’s Ridge function to include the first column in our design matrix.
+We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.
+What happens if we do not include the intercept in our fit?
+Let us see how we can change this code by zero centering.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
+
+<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
+<span class="c1"># A seed just to ensure that the random numbers are the same for every run.</span>
+<span class="c1"># Useful for eventual debugging.</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">315</span><span class="p">)</span>
+
+<span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+
+<span class="n">Maxpolydegree</span> <span class="o">=</span> <span class="mi">20</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">n</span><span class="p">,</span><span class="n">Maxpolydegree</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">Maxpolydegree</span><span class="p">):</span> <span class="c1">#No intercept column</span>
+    <span class="n">X</span><span class="p">[:,</span><span class="n">degree</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="p">(</span><span class="n">degree</span><span class="p">)</span>
+
+<span class="c1"># We split the data in test and training data</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+
+<span class="c1">#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable</span>
+<span class="n">X_train_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="c1">#Center by removing mean from each feature</span>
+<span class="n">X_train_scaled</span> <span class="o">=</span> <span class="n">X_train</span> <span class="o">-</span> <span class="n">X_train_mean</span> 
+<span class="n">X_test_scaled</span> <span class="o">=</span> <span class="n">X_test</span> <span class="o">-</span> <span class="n">X_train_mean</span>
+<span class="c1">#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)</span>
+<span class="c1">#Remove the intercept from the training data.</span>
+<span class="n">y_scaler</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_train</span><span class="p">)</span>           
+<span class="n">y_train_scaled</span> <span class="o">=</span> <span class="n">y_train</span> <span class="o">-</span> <span class="n">y_scaler</span>   
+
+<span class="n">p</span> <span class="o">=</span> <span class="n">Maxpolydegree</span><span class="o">-</span><span class="mi">1</span>
+<span class="n">I</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">p</span><span class="p">,</span><span class="n">p</span><span class="p">)</span>
+<span class="c1"># Decide which values of lambda to use</span>
+<span class="n">nlambdas</span> <span class="o">=</span> <span class="mi">6</span>
+<span class="n">MSEOwnRidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+<span class="n">MSERidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+
+<span class="n">lambdas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">nlambdas</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">):</span>
+    <span class="n">lmb</span> <span class="o">=</span> <span class="n">lambdas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="n">OwnRidgeBeta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_train_scaled</span><span class="o">+</span><span class="n">lmb</span><span class="o">*</span><span class="n">I</span><span class="p">)</span> <span class="o">@</span> <span class="n">X_train_scaled</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="p">(</span><span class="n">y_train_scaled</span><span class="p">)</span>
+    <span class="n">intercept_</span> <span class="o">=</span> <span class="n">y_scaler</span> <span class="o">-</span> <span class="n">X_train_mean</span><span class="nd">@OwnRidgeBeta</span> <span class="c1">#The intercept can be shifted so the model can predict on uncentered data</span>
+    <span class="c1">#Add intercept to prediction</span>
+    <span class="n">ypredictOwnRidge</span> <span class="o">=</span> <span class="n">X_test_scaled</span> <span class="o">@</span> <span class="n">OwnRidgeBeta</span> <span class="o">+</span> <span class="n">y_scaler</span> 
+    <span class="n">RegRidge</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Ridge</span><span class="p">(</span><span class="n">lmb</span><span class="p">)</span>
+    <span class="n">RegRidge</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+    <span class="n">ypredictRidge</span> <span class="o">=</span> <span class="n">RegRidge</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+    <span class="n">MSEOwnRidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictOwnRidge</span><span class="p">)</span>
+    <span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictRidge</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Beta values for own Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">OwnRidgeBeta</span><span class="p">)</span> <span class="c1">#Intercept is given by mean of target variable</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Beta values for Scikit-Learn Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">RegRidge</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Intercept from own implementation:&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">intercept_</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Intercept from Scikit-Learn Ridge implementation&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">RegRidge</span><span class="o">.</span><span class="n">intercept_</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE values for own Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">MSEOwnRidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;MSE values for Scikit-Learn Ridge implementation&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+
+<span class="c1"># Now plot the results</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSEOwnRidgePredict</span><span class="p">,</span> <span class="s1">&#39;b--&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE own Ridge Test&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSERidgePredict</span><span class="p">,</span> <span class="s1">&#39;g--&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE SL Ridge Test&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;log10(lambda)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;MSE&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>We see here, when compared to the code which includes explicitely the
+intercept column, that our MSE value is actually smaller. This is
+because the regularization term does not include the intercept value
+<span class="math notranslate nohighlight">\(\beta_0\)</span> in the fitting.  This applies to Lasso regularization as
+well.  It means that our optimization is now done only with the
+centered matrix and/or vector that enter the fitting procedure.</p>
+</div>
+<div class="section" id="test-function-for-what-happens-with-ols-ridge-and-lasso">
+<h2>Test Function for what happens with OLS, Ridge and Lasso<a class="headerlink" href="#test-function-for-what-happens-with-ols-ridge-and-lasso" title="Permalink to this headline">¶</a></h2>
+<p>Hitherto we have discussed Ridge and Lasso regression in terms of a
+linear analysis. This may to many of you feel rather technical and
+perhaps not that intuitive. The question is whether we can develop a
+more intuitive way of understanding what Ridge and Lasso express.</p>
+<p>Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit.</p>
+<p>We will play around with a study of the values for the optimal
+parameters <span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span> using OLS, Ridge and Lasso regression.  For
+OLS, you will notice as function of the noise and polynomial degree,
+that the parameters <span class="math notranslate nohighlight">\(\beta\)</span> will fluctuate from order to order in the
+polynomial fit and that for larger and larger polynomial degrees of freedom, the parameters will tend to increase in value for OLS.</p>
+<p>For Ridge and Lasso regression, the higher order parameters will typically be reduced, providing thereby less fluctuations from one order to another one.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
+<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
+
+<span class="k">def</span> <span class="nf">R2</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span> <span class="n">y_model</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">y_model</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_data</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
+
+<span class="c1"># Make data set.</span>
+<span class="n">n</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+<span class="n">Maxpolydegree</span> <span class="o">=</span> <span class="mi">5</span>
+<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="n">Maxpolydegree</span><span class="p">))</span>
+<span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
+
+
+<span class="k">for</span> <span class="n">polydegree</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">Maxpolydegree</span><span class="p">):</span>
+    <span class="n">X</span><span class="p">[:,</span><span class="n">polydegree</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="p">(</span><span class="n">polydegree</span><span class="p">)</span>
+
+<span class="c1"># We split the data in test and training data</span>
+<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+
+<span class="c1"># matrix inversion to find beta</span>
+<span class="n">OLSbeta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_train</span><span class="p">)</span> <span class="o">@</span> <span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y_train</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">OLSbeta</span><span class="p">)</span>
+<span class="n">ypredictOLS</span> <span class="o">=</span> <span class="n">X_test</span> <span class="o">@</span> <span class="n">OLSbeta</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test MSE OLS&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictOLS</span><span class="p">))</span>
+<span class="c1"># Repeat now for Lasso and Ridge regression and various values of the regularization parameter using Scikit-Learn</span>
+<span class="c1"># Decide which values of lambda to use</span>
+<span class="n">nlambdas</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">MSERidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+<span class="n">MSELassoPredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
+<span class="n">lambdas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nlambdas</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">):</span>
+    <span class="n">lmb</span> <span class="o">=</span> <span class="n">lambdas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+    <span class="c1"># Make the fit using Ridge and Lasso</span>
+    <span class="n">RegRidge</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Ridge</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">RegRidge</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+    <span class="n">RegLasso</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Lasso</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">RegLasso</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
+    <span class="c1"># and then make the prediction</span>
+    <span class="n">ypredictRidge</span> <span class="o">=</span> <span class="n">RegRidge</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+    <span class="n">ypredictLasso</span> <span class="o">=</span> <span class="n">RegLasso</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
+    <span class="c1"># Compute the MSE and print it</span>
+    <span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictRidge</span><span class="p">)</span>
+    <span class="n">MSELassoPredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictLasso</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">RegRidge</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">RegLasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
+<span class="c1"># Now plot the results</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSERidgePredict</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE Ridge Test&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSELassoPredict</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE Lasso Test&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;log10(lambda)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;MSE&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</div>
+<p>How can we understand this?</p>
+</div>
 <div class="section" id="linking-the-regression-analysis-with-a-statistical-interpretation">
 <h2>Linking the regression analysis with a statistical interpretation<a class="headerlink" href="#linking-the-regression-analysis-with-a-statistical-interpretation" title="Permalink to this headline">¶</a></h2>
 <p>We will now couple the discussions of ordinary least squares, Ridge
@@ -2036,93 +2564,6 @@ p(Y=1\vert X=1)=\frac{p(X=1\vert Y=1)p(Y=1)}{p(X=1\vert Y=1)p(Y=1)+p(X=1\vert Y=
 </div>
 <div class="section" id="bayes-theorem-and-ridge-and-lasso-regression">
 <h2>Bayes’ Theorem and Ridge and Lasso Regression<a class="headerlink" href="#bayes-theorem-and-ridge-and-lasso-regression" title="Permalink to this headline">¶</a></h2>
-<p>Hitherto we have discussed Ridge and Lasso regression in terms of a
-linear analysis. This may to many of you feel rather technical and
-perhaps not that intuitive. The question is whether we can develop a
-more intuitive way of understanding what Ridge and Lasso express.</p>
-<p>Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit.</p>
-</div>
-<div class="section" id="test-function-for-what-happens-with-ols-ridge-and-lasso">
-<h2>Test Function for what happens with OLS, Ridge and Lasso<a class="headerlink" href="#test-function-for-what-happens-with-ols-ridge-and-lasso" title="Permalink to this headline">¶</a></h2>
-<p>We will play around with a study of the values for the optimal
-parameters <span class="math notranslate nohighlight">\(\boldsymbol{\beta}\)</span> using OLS, Ridge and Lasso regression.  For
-OLS, you will notice as function of the noise and polynomial degree,
-that the parameters <span class="math notranslate nohighlight">\(\beta\)</span> will fluctuate from order to order in the
-polynomial fit and that for larger and larger polynomial degrees of freedom, the parameters will tend to increase in value for OLS.</p>
-<p>For Ridge and Lasso regression, the higher order parameters will typically be reduced, providing thereby less fluctuations from one order to another one.</p>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
-<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">linear_model</span>
-
-<span class="k">def</span> <span class="nf">R2</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span> <span class="n">y_model</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">y_model</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y_data</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">MSE</span><span class="p">(</span><span class="n">y_data</span><span class="p">,</span><span class="n">y_model</span><span class="p">):</span>
-    <span class="n">n</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">y_model</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">y_data</span><span class="o">-</span><span class="n">y_model</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
-
-<span class="c1"># Make data set.</span>
-<span class="n">n</span> <span class="o">=</span> <span class="mi">10000</span>
-<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">x</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-
-<span class="n">Maxpolydegree</span> <span class="o">=</span> <span class="mi">5</span>
-<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="n">Maxpolydegree</span><span class="p">))</span>
-<span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span>
-
-<span class="k">for</span> <span class="n">polydegree</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">Maxpolydegree</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">degree</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">polydegree</span><span class="p">):</span>
-        <span class="n">X</span><span class="p">[:,</span><span class="n">degree</span><span class="p">]</span> <span class="o">=</span> <span class="n">x</span><span class="o">**</span><span class="p">(</span><span class="n">degree</span><span class="p">)</span>
-
-
-<span class="c1"># We split the data in test and training data</span>
-<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-
-<span class="c1"># matrix inversion to find beta</span>
-<span class="n">OLSbeta</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">X_train</span><span class="p">)</span> <span class="o">@</span> <span class="n">X_train</span><span class="o">.</span><span class="n">T</span> <span class="o">@</span> <span class="n">y_train</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">OLSbeta</span><span class="p">)</span>
-<span class="n">ypredictOLS</span> <span class="o">=</span> <span class="n">X_test</span> <span class="o">@</span> <span class="n">OLSbeta</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test MSE OLS&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictOLS</span><span class="p">))</span>
-<span class="c1"># Repeat now for Lasso and Ridge regression and various values of the regularization parameter using Scikit-Learn</span>
-<span class="c1"># Decide which values of lambda to use</span>
-<span class="n">nlambdas</span> <span class="o">=</span> <span class="mi">4</span>
-<span class="n">MSERidgePredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
-<span class="n">MSELassoPredict</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">)</span>
-<span class="n">lambdas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nlambdas</span><span class="p">)</span>
-<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nlambdas</span><span class="p">):</span>
-    <span class="n">lmb</span> <span class="o">=</span> <span class="n">lambdas</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-    <span class="c1"># Make the fit using Ridge and Lasso</span>
-    <span class="n">RegRidge</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Ridge</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">RegRidge</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
-    <span class="n">RegLasso</span> <span class="o">=</span> <span class="n">linear_model</span><span class="o">.</span><span class="n">Lasso</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">fit_intercept</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">RegLasso</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
-    <span class="c1"># and then make the prediction</span>
-    <span class="n">ypredictRidge</span> <span class="o">=</span> <span class="n">RegRidge</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-    <span class="n">ypredictLasso</span> <span class="o">=</span> <span class="n">RegLasso</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
-    <span class="c1"># Compute the MSE and print it</span>
-    <span class="n">MSERidgePredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictRidge</span><span class="p">)</span>
-    <span class="n">MSELassoPredict</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">MSE</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">ypredictLasso</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">RegRidge</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">lmb</span><span class="p">,</span><span class="n">RegLasso</span><span class="o">.</span><span class="n">coef_</span><span class="p">)</span>
-<span class="c1"># Now plot the results</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSERidgePredict</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE Ridge Test&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">lambdas</span><span class="p">),</span> <span class="n">MSELassoPredict</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;MSE Lasso Test&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;log10(lambda)&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;MSE&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-</div>
-</div>
-</div>
-<p>How can we understand this?</p>
-</div>
-<div class="section" id="invoking-bayes-theorem">
-<h2>Invoking Bayes’ theorem<a class="headerlink" href="#invoking-bayes-theorem" title="Permalink to this headline">¶</a></h2>
 <p>Using Bayes’ theorem we can gain a better intuition about Ridge and Lasso regression.</p>
 <p>For ordinary least squares we postulated that the maximum likelihood for the doamin of events <span class="math notranslate nohighlight">\(\boldsymbol{D}\)</span> (one-dimensional case)</p>
 <div class="math notranslate nohighlight">
diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek37.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek37.ipynb
index cc9e06cfc..2efe96e13 100644
--- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek37.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cfebae03",
+   "id": "1ff4a4e9",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "353ebb07",
+   "id": "0569f7a9",
    "metadata": {
     "editable": true
    },
@@ -27,25 +27,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c02815dd",
+   "id": "6f34e507",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Overarching aims of the exercises this week\n",
     "\n",
-    "This exercise deals with various mean values and variances in  linear regression method (here it may be useful to look up chapter 3, equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570)). The exercise is also a part of project 1 and can be reused in the theory part of the project.\n",
+    "This exercise deals with various mean values and variances in linear\n",
+    "regression method (here it may be useful to look up chapter 3,\n",
+    "equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome\n",
+    "H. Friedman, The Elements of Statistical Learning,\n",
+    "Springer](https://www.springer.com/gp/book/9780387848570)). The\n",
+    "exercise is also a part of project 1 and can be reused in the theory\n",
+    "part of the project.\n",
     "\n",
-    "For more discussions on Ridge regression and calculation of expectation values, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
+    "For more discussions on Ridge regression and calculation of\n",
+    "expectation values, [Wessel van\n",
+    "Wieringen's](https://arxiv.org/abs/1509.09169) article is highly\n",
+    "recommended.\n",
     "\n",
-    "The assumption we have made is \n",
-    "that there exists a continuous function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0, \\sigma^2)$\n",
-    "which describes our data"
+    "The assumption we have made is that there exists a continuous function\n",
+    "$f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0,\n",
+    "\\sigma^2)$ which describes our data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb152043",
+   "id": "3ff574a4",
    "metadata": {
     "editable": true
    },
@@ -57,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aba7a46",
+   "id": "096a3165",
    "metadata": {
     "editable": true
    },
@@ -68,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be52c097",
+   "id": "cc9fb258",
    "metadata": {
     "editable": true
    },
@@ -80,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c54979e5",
+   "id": "daf4eb5d",
    "metadata": {
     "editable": true
    },
@@ -90,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b1eb811",
+   "id": "3b491679",
    "metadata": {
     "editable": true
    },
@@ -102,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "036a5440",
+   "id": "93a49739",
    "metadata": {
     "editable": true
    },
@@ -114,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7fb8094",
+   "id": "a3fa80db",
    "metadata": {
     "editable": true
    },
@@ -125,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7d92995",
+   "id": "e6e2defd",
    "metadata": {
     "editable": true
    },
@@ -137,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29eb9701",
+   "id": "9aa19afa",
    "metadata": {
     "editable": true
    },
@@ -150,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa106511",
+   "id": "5fa82a47",
    "metadata": {
     "editable": true
    },
@@ -162,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58be1091",
+   "id": "33099abb",
    "metadata": {
     "editable": true
    },
@@ -172,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cb58e41",
+   "id": "5d03f001",
    "metadata": {
     "editable": true
    },
@@ -184,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04df28d",
+   "id": "94bd151d",
    "metadata": {
     "editable": true
    },
@@ -195,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c095c96a",
+   "id": "f0b84941",
    "metadata": {
     "editable": true
    },
@@ -207,20 +216,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d425a79",
+   "id": "60370a63",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
+    "\\mathbb{E} \\big[ \\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
     "\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb89cfab",
+   "id": "af251217",
    "metadata": {
     "editable": true
    },
@@ -233,19 +242,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea8801c",
+   "id": "02e978da",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\mbox{Var}[\\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e885693",
+   "id": "3656a896",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
index fcd45c57e..71e93c686 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb
@@ -1519,7 +1519,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.996535469511469\n"
+      "0.9952714635635723\n"
      ]
     }
    ],
@@ -1550,7 +1550,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0077750600806588575\n"
+      "0.009039197557276654\n"
      ]
     }
    ],
@@ -1585,23 +1585,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.05751737 0.02644748 0.02533184 0.02193035 0.02521356 0.00712899\n",
-      " 0.00857688 0.0101339  0.01097227 0.01132947 0.01644452 0.01655455\n",
-      " 0.00771046 0.05177021 0.01374437 0.04208274 0.01667873 0.00228986\n",
-      " 0.00749579 0.00852965 0.01628823 0.0341899  0.01178253 0.00626339\n",
-      " 0.00514913 0.00791019 0.00363986 0.00471966 0.00390805 0.00910241\n",
-      " 0.00232255 0.02670606 0.03224523 0.01637147 0.00918914 0.02705154\n",
-      " 0.00321694 0.01904583 0.0177181  0.00538287 0.02291121 0.01028255\n",
-      " 0.04566103 0.00954257 0.00011551 0.0283589  0.00953728 0.03399521\n",
-      " 0.01205286 0.02251524 0.00353655 0.02344605 0.05288681 0.01950574\n",
-      " 0.00330907 0.01492855 0.01472766 0.03952093 0.01491299 0.00187154\n",
-      " 0.02897168 0.00072037 0.00895315 0.02775293 0.01547992 0.04117184\n",
-      " 0.0116726  0.03330335 0.01515966 0.0070381  0.01317074 0.00654702\n",
-      " 0.00329264 0.02502101 0.00226569 0.05029894 0.01929643 0.03028379\n",
-      " 0.053994   0.03413302 0.01854824 0.00393744 0.0600658  0.01855624\n",
-      " 0.0590702  0.01826743 0.01039549 0.02151219 0.00928016 0.03536062\n",
-      " 0.00503218 0.08526352 0.00506765 0.02609885 0.04771105 0.0010059\n",
-      " 0.00659545 0.00143188 0.01489692 0.08237141]\n"
+      "[6.11430166e-02 7.59428035e-03 7.64295978e-03 5.34442654e-02\n",
+      " 2.53861506e-02 7.46543658e-02 2.79339933e-02 1.32946226e-02\n",
+      " 3.80320028e-03 2.40474117e-02 4.36028477e-02 3.24382528e-02\n",
+      " 1.26022420e-02 5.06848116e-03 1.96793569e-02 1.04826413e-04\n",
+      " 9.25580178e-03 1.80413812e-02 1.22519792e-02 1.01505087e-02\n",
+      " 2.67180442e-02 6.71316703e-02 7.68415876e-03 3.33174939e-02\n",
+      " 1.43570786e-02 8.43459933e-03 4.11046407e-03 9.10453697e-04\n",
+      " 4.33142087e-03 7.64507671e-02 2.19696497e-02 1.18460525e-02\n",
+      " 4.35665906e-02 3.62818572e-02 1.61018347e-03 3.78738205e-02\n",
+      " 4.57031517e-03 3.11279984e-02 1.07745809e-02 7.09848429e-03\n",
+      " 2.30006341e-03 4.62535124e-02 6.70493970e-02 6.26624267e-03\n",
+      " 4.23399553e-02 1.88690267e-02 7.46184816e-03 1.75934097e-02\n",
+      " 3.60820168e-02 1.09322328e-02 3.16749353e-02 2.30626515e-02\n",
+      " 5.76196895e-04 1.51000420e-02 1.95456434e-03 1.22138768e-02\n",
+      " 2.52945186e-03 2.12804129e-03 3.32695433e-03 1.90886410e-02\n",
+      " 1.84291049e-02 1.78104004e-02 6.34498455e-02 1.50032818e-02\n",
+      " 3.43525003e-02 1.52033394e-03 3.95921014e-02 7.67148008e-02\n",
+      " 2.40893740e-02 6.75638425e-02 1.22105472e-02 7.72706640e-03\n",
+      " 3.00721135e-02 8.77081499e-02 1.40684484e-02 4.57081718e-02\n",
+      " 3.51190686e-02 7.42092776e-02 4.03105744e-02 1.08994018e-02\n",
+      " 1.37071826e-02 3.69106251e-02 1.94966662e-03 1.71112140e-02\n",
+      " 3.12669602e-03 3.01610677e-02 1.72139847e-02 2.75703556e-02\n",
+      " 2.23333773e-02 2.76892410e-03 8.05886306e-02 1.15545946e-01\n",
+      " 4.88029792e-02 4.59648068e-02 1.98750794e-02 2.69178261e-03\n",
+      " 1.07925220e-02 1.13151043e-03 5.00620642e-03 3.69871367e-03]\n"
      ]
     }
    ],
@@ -1655,15 +1663,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.95976303  0.48974963  2.82202765  3.79224555 -2.14248744]\n",
+      "[ 2.04038959 -0.81343612  7.94304103 -3.83770135  1.70073443]\n",
       "Training R2\n",
-      "0.9947613223847728\n",
+      "0.9954441212986554\n",
       "Training MSE\n",
-      "0.010596520060222926\n",
+      "0.008653750612762521\n",
       "Test R2\n",
-      "0.989645675273948\n",
+      "0.99255249100346\n",
       "Test MSE\n",
-      "0.01889099091713886\n"
+      "0.016970019460234076\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb
index c68516565..d15ac0234 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7411173e",
+   "id": "2b900fea",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f50db913",
+   "id": "2286e211",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a90e380",
+   "id": "edbcd7f9",
    "metadata": {
     "editable": true
    },
@@ -44,28 +44,34 @@
     "\n",
     "* Material for the lecture on Thursday September 7\n",
     "\n",
-    "  * Linear Regression and links with Statistics, Resampling methods\n",
+    "  * Technicalities related to scaling and other issues with data handling\n",
     "\n",
-    "  * Recommended Reading: Goodfellow et al chapter 3 on probability theory, see URL:\"\"\n",
+    "  * Linear Regression and links with Statistics\n",
     "\n",
-    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)"
+    "  * [Recommended Reading: Goodfellow et al chapter 3 on probability theory](https://www.deeplearningbook.org/)\n",
+    "\n",
+    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)\n",
+    "\n",
+    "  * [Video of lecture](https://youtu.be/Kc20CFK0z7Y)  \n",
+    "\n",
+    "  * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep7.pdf)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "351d36fd",
+   "id": "5dbf3ff5",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Material for the active learning sessions Tuesday and Wednesday\n",
     "\n",
-    "The material here contains a summary from last Week and discussion of SVD, Ridge and Lasso regression with examples"
+    "The material here contains a summary from last week and discussion of SVD, Ridge and Lasso regression with examples"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0f9f4d2f",
+   "id": "2dae8175",
    "metadata": {
     "editable": true
    },
@@ -77,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ec36bdd",
+   "id": "fb3d486d",
    "metadata": {
     "editable": true
    },
@@ -89,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "614493c6",
+   "id": "5f8492a1",
    "metadata": {
     "editable": true
    },
@@ -99,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4be423",
+   "id": "a35422b4",
    "metadata": {
     "editable": true
    },
@@ -111,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b262332c",
+   "id": "aef923ea",
    "metadata": {
     "editable": true
    },
@@ -121,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1829a4",
+   "id": "5da8277b",
    "metadata": {
     "editable": true
    },
@@ -138,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c82dd9e",
+   "id": "eb34e530",
    "metadata": {
     "editable": true
    },
@@ -148,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bab8721",
+   "id": "a2a33728",
    "metadata": {
     "editable": true
    },
@@ -160,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72273a4f",
+   "id": "9bea1837",
    "metadata": {
     "editable": true
    },
@@ -170,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4af03ea",
+   "id": "af711505",
    "metadata": {
     "editable": true
    },
@@ -182,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0d35853",
+   "id": "f02b3738",
    "metadata": {
     "editable": true
    },
@@ -196,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5ea827",
+   "id": "d3fe4855",
    "metadata": {
     "editable": true
    },
@@ -208,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6b341ce",
+   "id": "7b5fa376",
    "metadata": {
     "editable": true
    },
@@ -230,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db723709",
+   "id": "d212be4e",
    "metadata": {
     "editable": true
    },
@@ -242,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e600268",
+   "id": "eca13576",
    "metadata": {
     "editable": true
    },
@@ -257,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9452938",
+   "id": "fbf0e2be",
    "metadata": {
     "editable": true
    },
@@ -269,7 +275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53fea1e6",
+   "id": "acbd86ec",
    "metadata": {
     "editable": true
    },
@@ -281,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed10baad",
+   "id": "8887e338",
    "metadata": {
     "editable": true
    },
@@ -291,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4d0db96",
+   "id": "e95bcbda",
    "metadata": {
     "editable": true
    },
@@ -303,7 +309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fae3cadc",
+   "id": "e66200c2",
    "metadata": {
     "editable": true
    },
@@ -313,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e973c775",
+   "id": "a0c3692c",
    "metadata": {
     "editable": true
    },
@@ -325,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f88a33f4",
+   "id": "8d13a9ff",
    "metadata": {
     "editable": true
    },
@@ -335,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bc7214d",
+   "id": "31c3dd91",
    "metadata": {
     "editable": true
    },
@@ -347,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "561d4c91",
+   "id": "87a6b291",
    "metadata": {
     "editable": true
    },
@@ -364,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eda2d1e",
+   "id": "3bb9eed3",
    "metadata": {
     "editable": true
    },
@@ -378,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c95dbc32",
+   "id": "ba123060",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -405,7 +411,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77079d25",
+   "id": "46fdfd39",
    "metadata": {
     "editable": true
    },
@@ -416,7 +422,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cd0ecc44",
+   "id": "36e47fde",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -456,7 +462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41d896dc",
+   "id": "53878874",
    "metadata": {
     "editable": true
    },
@@ -476,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f594852c",
+   "id": "87546753",
    "metadata": {
     "editable": true
    },
@@ -488,7 +494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b78264f2",
+   "id": "d8a4669d",
    "metadata": {
     "editable": true
    },
@@ -499,7 +505,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d05eff95",
+   "id": "b6523ecb",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -533,7 +539,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fae32c6",
+   "id": "2fce573f",
    "metadata": {
     "editable": true
    },
@@ -543,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8631011a",
+   "id": "840991d3",
    "metadata": {
     "editable": true
    },
@@ -556,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e7701de",
+   "id": "4df7aac6",
    "metadata": {
     "editable": true
    },
@@ -568,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea7230b",
+   "id": "3f3acc05",
    "metadata": {
     "editable": true
    },
@@ -578,7 +584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5029213",
+   "id": "2d1218f6",
    "metadata": {
     "editable": true
    },
@@ -591,7 +597,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "535ad70e",
+   "id": "01624e31",
    "metadata": {
     "editable": true
    },
@@ -601,7 +607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "159ae526",
+   "id": "616fe159",
    "metadata": {
     "editable": true
    },
@@ -613,7 +619,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f25dfa4",
+   "id": "27cf2acd",
    "metadata": {
     "editable": true
    },
@@ -628,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d187806",
+   "id": "2ff1e0d2",
    "metadata": {
     "editable": true
    },
@@ -641,7 +647,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c692db5",
+   "id": "aef1996b",
    "metadata": {
     "editable": true
    },
@@ -655,7 +661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3404e0b4",
+   "id": "75a44cc9",
    "metadata": {
     "editable": true
    },
@@ -667,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bc49666",
+   "id": "e2ba6037",
    "metadata": {
     "editable": true
    },
@@ -677,7 +683,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08dfa1",
+   "id": "95517053",
    "metadata": {
     "editable": true
    },
@@ -690,7 +696,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30bcc0cd",
+   "id": "5f15d4ea",
    "metadata": {
     "editable": true
    },
@@ -702,7 +708,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d311a7d8",
+   "id": "68579bd0",
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@@ -714,7 +720,7 @@
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@@ -726,7 +732,7 @@
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@@ -738,7 +744,7 @@
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@@ -752,7 +758,7 @@
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@@ -764,7 +770,7 @@
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@@ -774,7 +780,7 @@
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@@ -786,7 +792,7 @@
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@@ -796,7 +802,7 @@
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@@ -808,7 +814,7 @@
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@@ -820,7 +826,7 @@
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@@ -869,7 +875,7 @@
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@@ -882,7 +888,7 @@
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@@ -904,7 +910,7 @@
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@@ -916,7 +922,7 @@
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@@ -926,7 +932,7 @@
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@@ -966,7 +972,7 @@
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@@ -978,7 +984,7 @@
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@@ -990,7 +996,7 @@
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@@ -1000,7 +1006,7 @@
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@@ -1051,7 +1057,7 @@
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@@ -1258,7 +1264,7 @@
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@@ -1270,7 +1276,7 @@
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@@ -1280,7 +1286,7 @@
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@@ -1292,7 +1298,7 @@
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@@ -1302,7 +1308,7 @@
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@@ -1360,7 +1366,7 @@
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@@ -1372,7 +1378,7 @@
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@@ -1382,7 +1388,7 @@
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@@ -1428,7 +1434,7 @@
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@@ -1438,7 +1444,7 @@
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@@ -1450,7 +1456,7 @@
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@@ -1462,7 +1468,7 @@
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@@ -1472,7 +1478,7 @@
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@@ -1484,7 +1490,7 @@
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@@ -1497,7 +1503,7 @@
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@@ -1509,7 +1515,7 @@
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@@ -1521,7 +1527,7 @@
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@@ -1533,7 +1539,7 @@
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@@ -1543,7 +1549,7 @@
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@@ -1555,7 +1561,7 @@
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@@ -1565,7 +1571,7 @@
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@@ -1577,7 +1583,7 @@
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@@ -1587,7 +1593,7 @@
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@@ -1599,7 +1605,7 @@
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@@ -1609,7 +1615,7 @@
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@@ -1622,7 +1628,7 @@
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@@ -1634,7 +1640,7 @@
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@@ -1646,7 +1652,7 @@
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@@ -1656,7 +1662,7 @@
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@@ -1668,7 +1674,7 @@
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@@ -1685,7 +1691,7 @@
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@@ -1697,7 +1703,7 @@
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@@ -1709,7 +1715,7 @@
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@@ -1719,7 +1725,7 @@
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@@ -1731,7 +1737,7 @@
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@@ -1753,7 +1759,7 @@
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@@ -1763,7 +1769,7 @@
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@@ -1775,7 +1781,7 @@
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@@ -1800,7 +1806,7 @@
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@@ -1862,7 +1868,7 @@
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@@ -1872,7 +1878,7 @@
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@@ -1883,7 +1889,7 @@
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    "source": [
-    "## Linking the regression analysis with a statistical interpretation\n",
+    "## Important technicalities: More on Rescaling data\n",
     "\n",
-    "We will now couple the discussions of ordinary least squares, Ridge\n",
-    "and Lasso regression with a statistical interpretation, that is we\n",
-    "move from a linear algebra analysis to a statistical analysis. In\n",
-    "particular, we will focus on what the regularization terms can result\n",
-    "in.  We will amongst other things show that the regularization\n",
-    "parameter can reduce considerably the variance of the parameters\n",
-    "$\\beta$.\n",
+    "When you are comparing your own code with for example **Scikit-Learn**'s\n",
+    "library, there are some technicalities to keep in mind.  The examples\n",
+    "here demonstrate some of these aspects with potential pitfalls.\n",
+    "\n",
+    "The discussion here focuses on the role of the intercept, how we can\n",
+    "set up the design matrix, what scaling we should use and other topics\n",
+    "which tend  confuse us.\n",
+    "\n",
+    "The intercept can be interpreted as the expected value of our\n",
+    "target/output variables when all other predictors are set to zero.\n",
+    "Thus, if we cannot assume that the expected outputs/targets are zero\n",
+    "when all predictors are zero (the columns in the design matrix), it\n",
+    "may be a bad idea to implement a model which penalizes the intercept.\n",
+    "Furthermore, in for example Ridge and Lasso regression, the default solutions\n",
+    "from the library **Scikit-Learn** (when not shrinking $\\beta_0$) for the unknown parameters\n",
+    "$\\boldsymbol{\\beta}$, are derived under the assumption that both $\\boldsymbol{y}$ and\n",
+    "$\\boldsymbol{X}$ are zero centered, that is we subtract the mean values.\n",
+    "\n",
+    "If our predictors represent different scales, then it is important to\n",
+    "standardize the design matrix $\\boldsymbol{X}$ by subtracting the mean of each\n",
+    "column from the corresponding column and dividing the column with its\n",
+    "standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,\n",
+    "the results may differ. \n",
     "\n",
     "The\n",
-    "advantage of doing linear regression is that we actually end up with\n",
-    "analytical expressions for several statistical quantities.  \n",
-    "Standard least squares and Ridge regression  allow us to\n",
-    "derive quantities like the variance and other expectation values in a\n",
-    "rather straightforward way.\n",
+    "[Standardscaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
+    "function in **Scikit-Learn** does this for us.  For the data sets we\n",
+    "have been studying in our various examples, the data are in many cases\n",
+    "already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform  a\n",
+    "survey of your data, with a critical assessment of them in case you need to scale the data.\n",
     "\n",
-    "It is assumed that $\\varepsilon_i\n",
-    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
-    "independent, i.e.:"
+    "If you need to scale the data, not doing so will give an *unfair*\n",
+    "penalization of the parameters since their magnitude depends on the\n",
+    "scale of their corresponding predictor.\n",
+    "\n",
+    "Suppose as an example that you \n",
+    "you have an input variable given by the heights of different persons.\n",
+    "Human height might be measured in inches or meters or\n",
+    "kilometers. If measured in kilometers, a standard linear regression\n",
+    "model with this predictor would probably give a much bigger\n",
+    "coefficient term, than if measured in millimeters.\n",
+    "This can clearly lead to problems in evaluating the cost/loss functions.\n",
+    "\n",
+    "Keep in mind that when you transform your data set before training a model, the same transformation needs to be done\n",
+    "on your eventual new data set  before making a prediction. If we translate this into a Python code, it would could be implemented as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e71daa5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "#Model training, we compute the mean value of y and X\n",
+    "y_train_mean = np.mean(y_train)\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "X_train = X_train - X_train_mean\n",
+    "y_train = y_train - y_train_mean\n",
+    "\n",
+    "# The we fit our model with the training data\n",
+    "trained_model = some_model.fit(X_train,y_train)\n",
+    "\n",
+    "\n",
+    "#Model prediction, we need also to transform our data set used for the prediction.\n",
+    "X_test = X_test - X_train_mean #Use mean from training data\n",
+    "y_pred = trained_model(X_test)\n",
+    "y_pred = y_pred + y_train_mean\n",
+    "\"\"\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e50205be",
+   "id": "1f0eb0d0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Let us try to understand what this may imply mathematically when we\n",
+    "subtract the mean values, also known as *zero centering*. For\n",
+    "simplicity, we will focus on  ordinary regression, as done in the above example.\n",
+    "\n",
+    "The cost/loss function  for regression is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ffc4acb",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
-    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
-    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
-    "\\end{align*}\n",
+    "C(\\beta_0, \\beta_1, ... , \\beta_{p-1}) = \\frac{1}{n}\\sum_{i=0}^{n} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij}\\beta_j\\right)^2,.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70eccda9",
+   "id": "2511c028",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The randomness of $\\varepsilon_i$ implies that\n",
-    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
-    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
-    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
-    "non-random scalar. To specify the parameters of the distribution of\n",
-    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "Recall also that we use the squared value. This expression can lead to an\n",
+    "increased penalty for higher differences between predicted and\n",
+    "output/target values.\n",
     "\n",
-    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
-    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
-    "row number $i$ and perform a sum over all values $p$."
+    "What we have done is to single out the $\\beta_0$ term in the\n",
+    "definition of the mean squared error (MSE).  The design matrix $X$\n",
+    "does in this case not contain any intercept column.  When we take the\n",
+    "derivative with respect to $\\beta_0$, we want the derivative to obey"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6bdcbf2",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Assumptions made\n",
-    "\n",
-    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
-    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
-    "which describe our data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d74471b8",
+   "id": "908beb7f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "\\frac{\\partial C}{\\partial \\beta_j} = 0,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f7b178d9",
+   "id": "5fae2a94",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
-    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+    "for all $j$. For $\\beta_0$ we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8ea16c62",
+   "id": "d7729a27",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "\\frac{\\partial C}{\\partial \\beta_0} = -\\frac{2}{n}\\sum_{i=0}^{n-1} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij} \\beta_j\\right).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a8d7bfd",
+   "id": "7fbb2ded",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Expectation value and variance\n",
-    "\n",
-    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+    "Multiplying away the constant $2/n$, we obtain"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5367d943",
+   "id": "8cef22ce",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mathbb{E}(y_i) & =\n",
-    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
-    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
-    "\\end{align*}\n",
+    "\\sum_{i=0}^{n-1} \\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} \\sum_{j=1}^{p-1} X_{ij} \\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7eefd85",
+   "id": "c38ab3a0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "while\n",
-    "its variance is"
+    "Let us specialize first to the case where we have only two parameters $\\beta_0$ and $\\beta_1$.\n",
+    "Our result for $\\beta_0$ simplifies then to"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27ce343a",
+   "id": "c47e7bbe",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
-    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
-    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
-    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
-    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
-    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
-    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
-    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
-    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
-    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
-    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
-    "\\end{align*}\n",
+    "n\\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} X_{i1} \\beta_1.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c67ea20b",
+   "id": "06962abd",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
-    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+    "We obtain then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b4da473a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
-    "\n",
-    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af812d38",
+   "id": "14c0a845",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9d3aaff5",
+   "id": "5f50f223",
    "metadata": {
     "editable": true
    },
    "source": [
-    "This means that the estimator of the regression parameters is unbiased.\n",
-    "\n",
-    "We can also calculate the variance\n",
-    "\n",
-    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+    "If we define"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eebea8b7",
+   "id": "188ed44d",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{eqnarray*}\n",
-    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
-    "\\\\\n",
-    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
-    "\\\\\n",
-    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
-    "% \\\\\n",
-    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
-    "\\\\\n",
-    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
-    "\\end{eqnarray*}\n",
+    "\\mu_{\\boldsymbol{x}_1}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "714f281a",
+   "id": "0b0db7b8",
    "metadata": {
     "editable": true
    },
    "source": [
-    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
-    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
-    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
-    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
-    "variance of the estimate of the $j$-th regression coefficient:\n",
-    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
-    "construct a confidence interval for the estimates.\n",
-    "\n",
-    "In a similar way, we can obtain analytical expressions for say the\n",
-    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
-    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
-    "\n",
-    "It is rather straightforward to show that"
+    "and the mean value of the outputs as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66015ca6",
+   "id": "e7550136",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "\\mu_y=\\frac{1}{n}\\sum_{i=0}^{n-1}y_i,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "267f01c0",
+   "id": "c7cc678d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We see clearly that \n",
-    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
-    "\n",
-    "We can also compute the variance as"
+    "we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "998de4b6",
+   "id": "9c9abcb2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\beta_0 = \\mu_y - \\beta_1\\mu_{\\boldsymbol{x}_1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "06a8d452",
+   "id": "2336bf55",
    "metadata": {
     "editable": true
    },
    "source": [
-    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
-    "\n",
-    "With this, we can compute the difference"
+    "In the general case with more parameters than $\\beta_0$ and $\\beta_1$, we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6cd3ed9b",
+   "id": "e0111655",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\frac{1}{n}\\sum_{i=0}^{n-1}\\sum_{j=1}^{p-1} X_{ij}\\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70f9aa69",
+   "id": "709c3af5",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The difference is non-negative definite since each component of the\n",
-    "matrix product is non-negative definite. \n",
-    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+    "We can rewrite the latter equation as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3bb18388",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Deriving OLS from a probability distribution\n",
-    "\n",
-    "Our basic assumption when we derived the OLS equations was to assume\n",
-    "that our output is determined by a given continuous function\n",
-    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
-    "distribution with zero mean value and an undetermined variance\n",
-    "$\\sigma^2$.\n",
-    "\n",
-    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
-    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
-    "the design matrix are not stochastic variables, we can assume that the\n",
-    "probability distribution of our targets is also a normal distribution\n",
-    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
-    "single output $y_i$ is given by the Gaussian distribution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84b546af",
+   "id": "a9ed060b",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\sum_{j=1}^{p-1} \\mu_{\\boldsymbol{x}_j}\\beta_j,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e90935e",
+   "id": "deb61dec",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Independent and Identically Distrubuted (iid)\n",
-    "\n",
-    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
-    "We define this distribution as"
+    "where we have defined"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b40b33f5",
+   "id": "dc60f20f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "\\mu_{\\boldsymbol{x}_j}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{ij},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "23e0c7ac",
+   "id": "b43a095d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "the mean value for all elements of the column vector $\\boldsymbol{x}_j$.\n",
     "\n",
-    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+    "Replacing $y_i$ with $y_i - y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dcd4ed9c",
+   "id": "050949a2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "C(\\boldsymbol{\\beta}) = (\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta})^T(\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta}).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c37adb5",
+   "id": "7c288c56",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
-    "in case we have a simple one-dimensional input and output case"
+    "If we minimize with respect to $\\boldsymbol{\\beta}$ we have then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27e4eea9",
+   "id": "72d25a93",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X})^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5dd95110",
+   "id": "6e4fe379",
    "metadata": {
     "editable": true
    },
    "source": [
-    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
-    "We can now rewrite the above probability as"
+    "where $\\boldsymbol{\\tilde{y}} = \\boldsymbol{y} - \\overline{\\boldsymbol{y}}$\n",
+    "and $\\tilde{X}_{ij} = X_{ij} - \\frac{1}{n}\\sum_{k=0}^{n-1}X_{kj}$.\n",
+    "\n",
+    "For Ridge regression we need to add $\\lambda \\boldsymbol{\\beta}^T\\boldsymbol{\\beta}$ to the cost function and get then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc7cfda7",
+   "id": "b0f0e6e7",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X} + \\lambda I)^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2bf6dbf4",
+   "id": "6cfb015d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+    "What does this mean? And why do we insist on all this? Let us look at some examples.\n",
+    "\n",
+    "This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (*code example thanks to  Øyvind Sigmundson Schøyen*). Here our scaling of the data is done by subtracting the mean values only.\n",
+    "Note also that we do not split the data into training and test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bfbf5d1d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n",
+    "np.random.seed(2021)\n",
+    "\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "\n",
+    "\n",
+    "def fit_beta(X, y):\n",
+    "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+    "\n",
+    "\n",
+    "true_beta = [2, 0.5, 3.7]\n",
+    "\n",
+    "x = np.linspace(0, 1, 11)\n",
+    "y = np.sum(\n",
+    "    np.asarray([x ** p * b for p, b in enumerate(true_beta)]), axis=0\n",
+    ") + 0.1 * np.random.normal(size=len(x))\n",
+    "\n",
+    "degree = 3\n",
+    "X = np.zeros((len(x), degree))\n",
+    "\n",
+    "# Include the intercept in the design matrix\n",
+    "for p in range(degree):\n",
+    "    X[:, p] = x ** p\n",
+    "\n",
+    "beta = fit_beta(X, y)\n",
+    "\n",
+    "# Intercept is included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=False).fit(X, y)\n",
+    "\n",
+    "print(f\"True beta: {true_beta}\")\n",
+    "print(f\"Fitted beta: {beta}\")\n",
+    "print(f\"Sklearn fitted beta: {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with intercept column\")\n",
+    "print(MSE(y,ypredictOwn))\n",
+    "print(f\"MSE with intercept column from SKL\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x, y, label=\"Data\")\n",
+    "plt.plot(x, X @ beta, label=\"Fit\")\n",
+    "plt.plot(x, skl.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
+    "\n",
+    "\n",
+    "# Do not include the intercept in the design matrix\n",
+    "X = np.zeros((len(x), degree - 1))\n",
+    "\n",
+    "for p in range(degree - 1):\n",
+    "    X[:, p] = x ** (p + 1)\n",
+    "\n",
+    "# Intercept is not included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=True).fit(X, y)\n",
+    "\n",
+    "# Use centered values for X and y when computing coefficients\n",
+    "y_offset = np.average(y, axis=0)\n",
+    "X_offset = np.average(X, axis=0)\n",
+    "\n",
+    "beta = fit_beta(X - X_offset, y - y_offset)\n",
+    "intercept = np.mean(y_offset - X_offset @ beta)\n",
+    "\n",
+    "print(f\"Manual intercept: {intercept}\")\n",
+    "print(f\"Fitted beta (wiothout intercept): {beta}\")\n",
+    "print(f\"Sklearn intercept: {skl.intercept_}\")\n",
+    "print(f\"Sklearn fitted beta (without intercept): {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with Manual intercept\")\n",
+    "print(MSE(y,ypredictOwn+intercept))\n",
+    "print(f\"MSE with Sklearn intercept\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "plt.plot(x, X @ beta + intercept, \"--\", label=\"Fit (manual intercept)\")\n",
+    "plt.plot(x, skl.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8ff9386",
+   "id": "7dc13bd7",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Maximum Likelihood Estimation (MLE)\n",
+    "The intercept is the value of our output/target variable\n",
+    "when all our features are zero and our function crosses the $y$-axis (for a one-dimensional case). \n",
     "\n",
-    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
-    "estimating the parameters of an assumed probability distribution,\n",
-    "given some observed data. This is achieved by maximizing a likelihood\n",
-    "function so that, under the assumed statistical model, the observed\n",
-    "data is the most probable. \n",
+    "Printing the MSE, we see first that both methods give the same MSE, as\n",
+    "they should.  However, when we move to for example Ridge regression,\n",
+    "the way we treat the intercept may give a larger or smaller MSE,\n",
+    "meaning that the MSE can be penalized by the value of the\n",
+    "intercept. Not including the intercept in the fit, means that the\n",
+    "regularization term does not include $\\beta_0$. For different values\n",
+    "of $\\lambda$, this may lead to different MSE values. \n",
     "\n",
-    "We will assume here that our events are given by the above Gaussian\n",
-    "distribution and we will determine the optimal parameters $\\beta$ by\n",
-    "maximizing the above PDF. However, computing the derivatives of a\n",
-    "product function is cumbersome and can easily lead to overflow and/or\n",
-    "underflowproblems, with potentials for loss of numerical precision.\n",
-    "\n",
-    "In practice, it is more convenient to maximize the logarithm of the\n",
-    "PDF because it is a monotonically increasing function of the argument.\n",
-    "Alternatively, and this will be our option, we will minimize the\n",
-    "negative of the logarithm since this is a monotonically decreasing\n",
-    "function.\n",
-    "\n",
-    "Note also that maximization/minimization of the logarithm of the PDF\n",
-    "is equivalent to the maximization/minimization of the function itself."
+    "To remind the reader, the regularization term, with the intercept in Ridge regression, is given by"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cd665e76",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## A new Cost Function\n",
-    "\n",
-    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "144c5c88",
+   "id": "3ce23f71",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=0}^{p-1}\\beta_j^2,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "76c7c5c6",
+   "id": "f34b4a50",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which becomes"
+    "but when we take out the intercept, this equation becomes"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f78405b0",
+   "id": "6c535531",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=1}^{p-1}\\beta_j^2.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd7b69b5",
+   "id": "dc9bd9cc",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+    "For Lasso regression we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a1430a20",
+   "id": "b5911fe2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_1 = \\lambda \\sum_{j=1}^{p-1}\\vert\\beta_j\\vert.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dfcd0c7b",
+   "id": "4d4103f1",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "043d37a6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28fe8edc",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b600d21c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## More basic Statistics and Bayes' theorem\n",
+    "It means that, when scaling the design matrix and the outputs/targets,\n",
+    "by subtracting the mean values, we have an optimization problem which\n",
+    "is not penalized by the intercept. The MSE value can then be smaller\n",
+    "since it focuses only on the remaining quantities. If we however bring\n",
+    "back the intercept, we will get a MSE which then contains the\n",
+    "intercept.\n",
     "\n",
-    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
-    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "Armed with this wisdom, we attempt first to simply set the intercept equal to **False** in our implementation of Ridge regression for our well-known  vanilla data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eecec942",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
     "\n",
-    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
     "\n",
-    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
-    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
     "\n",
-    "**Union of events is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d513cf2c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d18008b5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "**The product rule (aka joint probability) is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22f55ec4",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29e1bff6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(3155)\n",
     "\n",
-    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "333869a5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Marginal Probability\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf8de993",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c5349d1",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Conditional  Probability\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree))\n",
+    "#We include explicitely the intercept column\n",
+    "for degree in range(Maxpolydegree):\n",
+    "    X[:,degree] = x**degree\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "The conditional  probability, if $p(Y) > 0$, is"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbede038",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1334a9c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem\n",
+    "p = Maxpolydegree\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n",
+    "    # Note: we include the intercept column and no scaling\n",
+    "    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    # and then make the prediction\n",
+    "    ytildeOwnRidge = X_train @ OwnRidgeBeta\n",
+    "    ypredictOwnRidge = X_test @ OwnRidgeBeta\n",
+    "    ytildeRidge = RegRidge.predict(X_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta)\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
     "\n",
-    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac8dcf04",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1992e354",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which we can rewrite as"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24e5179f",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42979e5a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86a8cd07",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Interpretations of Bayes' Theorem\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')\n",
     "\n",
-    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
-    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
-    "the parameter space represented by $X$. This function is not\n",
-    "necesseraly normalized and is normally called the likelihood function.\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d67706b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The results here agree when we force **Scikit-Learn**'s Ridge function to include the first column in our design matrix.\n",
+    "We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.\n",
+    "What happens if we do not include the intercept in our fit?\n",
+    "Let us see how we can change this code by zero centering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "598d20bd",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(315)\n",
     "\n",
-    "Let us try to illustrate Bayes' theorem through an example."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9a4f9a9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Example of Usage of Bayes' theorem\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "Let us suppose that you are undergoing a series of mammography scans in\n",
-    "order to rule out possible breast cancer cases.  We define the\n",
-    "sensitivity for a positive event by the variable $X$. It takes binary\n",
-    "values with $X=1$ representing a positive event and $X=0$ being a\n",
-    "negative event. We reserve $Y$ as a classification parameter for\n",
-    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree-1))\n",
     "\n",
-    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "for degree in range(1,Maxpolydegree): #No intercept column\n",
+    "    X[:,degree-1] = x**(degree)\n",
     "\n",
-    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "888c64ec",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=1) =0.8.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5086b046",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
-    "It is however not correct, as the following Bayesian analysis shows."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "609ea2b0",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Doing it correctly\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
-    "Let us assume that the prior probability in the population as a whole is"
+    "#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "#Center by removing mean from each feature\n",
+    "X_train_scaled = X_train - X_train_mean \n",
+    "X_test_scaled = X_test - X_train_mean\n",
+    "#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)\n",
+    "#Remove the intercept from the training data.\n",
+    "y_scaler = np.mean(y_train)           \n",
+    "y_train_scaled = y_train - y_scaler   \n",
+    "\n",
+    "p = Maxpolydegree-1\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)\n",
+    "    intercept_ = y_scaler - X_train_mean@OwnRidgeBeta #The intercept can be shifted so the model can predict on uncentered data\n",
+    "    #Add intercept to prediction\n",
+    "    ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler \n",
+    "    RegRidge = linear_model.Ridge(lmb)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta) #Intercept is given by mean of target variable\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print('Intercept from own implementation:')\n",
+    "    print(intercept_)\n",
+    "    print('Intercept from Scikit-Learn Ridge implementation')\n",
+    "    print(RegRidge.intercept_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
+    "\n",
+    "\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "687bfdf3",
+   "id": "cec80003",
    "metadata": {
     "editable": true
    },
    "source": [
-    "$$\n",
-    "p(Y=1) =0.004.\n",
-    "$$"
+    "We see here, when compared to the code which includes explicitely the\n",
+    "intercept column, that our MSE value is actually smaller. This is\n",
+    "because the regularization term does not include the intercept value\n",
+    "$\\beta_0$ in the fitting.  This applies to Lasso regularization as\n",
+    "well.  It means that our optimization is now done only with the\n",
+    "centered matrix and/or vector that enter the fitting procedure."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c2af1d2d",
+   "id": "16cdcffb",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ec8d93",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=0) =0.1.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48a6fb5b",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba37d4fd",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "824f1789",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07f89fd5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "## Test Function for what happens with OLS, Ridge and Lasso\n",
     "\n",
     "Hitherto we have discussed Ridge and Lasso regression in terms of a\n",
     "linear analysis. This may to many of you feel rather technical and\n",
     "perhaps not that intuitive. The question is whether we can develop a\n",
     "more intuitive way of understanding what Ridge and Lasso express.\n",
     "\n",
-    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fcd73d9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Test Function for what happens with OLS, Ridge and Lasso\n",
+    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit. \n",
     "\n",
     "We will play around with a study of the values for the optimal\n",
     "parameters $\\boldsymbol{\\beta}$ using OLS, Ridge and Lasso regression.  For\n",
@@ -3028,8 +2922,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "4402e113",
+   "execution_count": 11,
+   "id": "e04dce1d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3056,10 +2950,9 @@
     "X = np.zeros((len(x),Maxpolydegree))\n",
     "X[:,0] = 1.0\n",
     "\n",
-    "for polydegree in range(1, Maxpolydegree):\n",
-    "    for degree in range(polydegree):\n",
-    "        X[:,degree] = x**(degree)\n",
     "\n",
+    "for polydegree in range(1,Maxpolydegree):\n",
+    "    X[:,polydegree] = x**(polydegree)\n",
     "\n",
     "# We split the data in test and training data\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
@@ -3103,7 +2996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b700e45e",
+   "id": "2ae202c3",
    "metadata": {
     "editable": true
    },
@@ -3113,43 +3006,475 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69659d9d",
+   "id": "4186f9f0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Invoking Bayes' theorem\n",
+    "## Linking the regression analysis with a statistical interpretation\n",
     "\n",
-    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "We will now couple the discussions of ordinary least squares, Ridge\n",
+    "and Lasso regression with a statistical interpretation, that is we\n",
+    "move from a linear algebra analysis to a statistical analysis. In\n",
+    "particular, we will focus on what the regularization terms can result\n",
+    "in.  We will amongst other things show that the regularization\n",
+    "parameter can reduce considerably the variance of the parameters\n",
+    "$\\beta$.\n",
     "\n",
-    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+    "The\n",
+    "advantage of doing linear regression is that we actually end up with\n",
+    "analytical expressions for several statistical quantities.  \n",
+    "Standard least squares and Ridge regression  allow us to\n",
+    "derive quantities like the variance and other expectation values in a\n",
+    "rather straightforward way.\n",
+    "\n",
+    "It is assumed that $\\varepsilon_i\n",
+    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
+    "independent, i.e.:"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80b423eb",
+   "id": "7293d2ab",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "\\begin{align*} \n",
+    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
+    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
+    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
+    "\\end{align*}\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de547949",
+   "id": "efee3703",
    "metadata": {
     "editable": true
    },
    "source": [
-    "is given by"
+    "The randomness of $\\varepsilon_i$ implies that\n",
+    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
+    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
+    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
+    "non-random scalar. To specify the parameters of the distribution of\n",
+    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "\n",
+    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
+    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
+    "row number $i$ and perform a sum over all values $p$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "99d9476a",
+   "id": "85d2303d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Assumptions made\n",
+    "\n",
+    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
+    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
+    "which describe our data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1a189c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b63e61f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
+    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14acfe14",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681408c6",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance\n",
+    "\n",
+    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d956e33c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \n",
+    "\\mathbb{E}(y_i) & =\n",
+    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
+    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "992bee5b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "while\n",
+    "its variance is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd75d1af",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
+    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
+    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
+    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
+    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
+    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
+    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
+    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
+    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
+    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
+    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f702411d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
+    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b86e729d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
+    "\n",
+    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5af888a2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d21a65e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This means that the estimator of the regression parameters is unbiased.\n",
+    "\n",
+    "We can also calculate the variance\n",
+    "\n",
+    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d8043ab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{eqnarray*}\n",
+    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
+    "\\\\\n",
+    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
+    "\\\\\n",
+    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
+    "% \\\\\n",
+    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
+    "\\\\\n",
+    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
+    "\\end{eqnarray*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a126ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
+    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
+    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
+    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
+    "variance of the estimate of the $j$-th regression coefficient:\n",
+    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
+    "construct a confidence interval for the estimates.\n",
+    "\n",
+    "In a similar way, we can obtain analytical expressions for say the\n",
+    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
+    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
+    "\n",
+    "It is rather straightforward to show that"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7287992a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2803f793",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We see clearly that \n",
+    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
+    "\n",
+    "We can also compute the variance as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7443c20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d32eb0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
+    "\n",
+    "With this, we can compute the difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbd0143e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6de88a4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The difference is non-negative definite since each component of the\n",
+    "matrix product is non-negative definite. \n",
+    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55630c9f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Deriving OLS from a probability distribution\n",
+    "\n",
+    "Our basic assumption when we derived the OLS equations was to assume\n",
+    "that our output is determined by a given continuous function\n",
+    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
+    "distribution with zero mean value and an undetermined variance\n",
+    "$\\sigma^2$.\n",
+    "\n",
+    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
+    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
+    "the design matrix are not stochastic variables, we can assume that the\n",
+    "probability distribution of our targets is also a normal distribution\n",
+    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
+    "single output $y_i$ is given by the Gaussian distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745e6352",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec8c116",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Independent and Identically Distrubuted (iid)\n",
+    "\n",
+    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
+    "We define this distribution as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f92be14e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edbdd556",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "\n",
+    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a6903ca",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57680896",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
+    "in case we have a simple one-dimensional input and output case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9309640f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67fb3df9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
+    "We can now rewrite the above probability as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "894f0fb7",
    "metadata": {
     "editable": true
    },
@@ -3161,7 +3486,508 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa5613ea",
+   "id": "13f3f705",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf3edfa",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Maximum Likelihood Estimation (MLE)\n",
+    "\n",
+    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
+    "estimating the parameters of an assumed probability distribution,\n",
+    "given some observed data. This is achieved by maximizing a likelihood\n",
+    "function so that, under the assumed statistical model, the observed\n",
+    "data is the most probable. \n",
+    "\n",
+    "We will assume here that our events are given by the above Gaussian\n",
+    "distribution and we will determine the optimal parameters $\\beta$ by\n",
+    "maximizing the above PDF. However, computing the derivatives of a\n",
+    "product function is cumbersome and can easily lead to overflow and/or\n",
+    "underflowproblems, with potentials for loss of numerical precision.\n",
+    "\n",
+    "In practice, it is more convenient to maximize the logarithm of the\n",
+    "PDF because it is a monotonically increasing function of the argument.\n",
+    "Alternatively, and this will be our option, we will minimize the\n",
+    "negative of the logarithm since this is a monotonically decreasing\n",
+    "function.\n",
+    "\n",
+    "Note also that maximization/minimization of the logarithm of the PDF\n",
+    "is equivalent to the maximization/minimization of the function itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be42ac0c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## A new Cost Function\n",
+    "\n",
+    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fbab08",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ef1983",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which becomes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d6504da",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d8775c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "176d1dab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e20e33bf",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f73eae",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d2cf20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c32b8f92",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## More basic Statistics and Bayes' theorem\n",
+    "\n",
+    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
+    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "\n",
+    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "\n",
+    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
+    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
+    "\n",
+    "**Union of events is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3779fd2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e13b18",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**The product rule (aka joint probability) is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8b41f0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b22e26",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "\n",
+    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f6645d2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Marginal Probability\n",
+    "\n",
+    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7349fe65",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6205b4f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Conditional  Probability\n",
+    "\n",
+    "The conditional  probability, if $p(Y) > 0$, is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43cb130d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d8f1813",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem\n",
+    "\n",
+    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2746770",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3659d1d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which we can rewrite as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfd9ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f247ad1",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2615306",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Interpretations of Bayes' Theorem\n",
+    "\n",
+    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
+    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
+    "the parameter space represented by $X$. This function is not\n",
+    "necesseraly normalized and is normally called the likelihood function.\n",
+    "\n",
+    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "\n",
+    "Let us try to illustrate Bayes' theorem through an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91eae9e8",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Example of Usage of Bayes' theorem\n",
+    "\n",
+    "Let us suppose that you are undergoing a series of mammography scans in\n",
+    "order to rule out possible breast cancer cases.  We define the\n",
+    "sensitivity for a positive event by the variable $X$. It takes binary\n",
+    "values with $X=1$ representing a positive event and $X=0$ being a\n",
+    "negative event. We reserve $Y$ as a classification parameter for\n",
+    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "\n",
+    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "\n",
+    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76fe1c46",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=1) =0.8.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397d9768",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
+    "It is however not correct, as the following Bayesian analysis shows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c701f5",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Doing it correctly\n",
+    "\n",
+    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
+    "Let us assume that the prior probability in the population as a whole is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fdb2639",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1) =0.004.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ae9f95",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a681f1dc",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=0) =0.1.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2c6030",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67a1c582",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "969f368b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356a70c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "\n",
+    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "\n",
+    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa653f05",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5dd1081",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "is given by"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "731b21d4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "362ae4e2",
    "metadata": {
     "editable": true
    },
@@ -3171,7 +3997,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dea4e11",
+   "id": "8ba56ac5",
    "metadata": {
     "editable": true
    },
@@ -3183,7 +4009,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99ab1d57",
+   "id": "658e6364",
    "metadata": {
     "editable": true
    },
@@ -3193,7 +4019,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "170ee68b",
+   "id": "28d6553d",
    "metadata": {
     "editable": true
    },
@@ -3205,7 +4031,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3388d6a4",
+   "id": "8f4ec5f0",
    "metadata": {
     "editable": true
    },
@@ -3215,7 +4041,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5bdaa16",
+   "id": "e72b34ca",
    "metadata": {
     "editable": true
    },
@@ -3231,7 +4057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbf8e7e6",
+   "id": "0e656652",
    "metadata": {
     "editable": true
    },
@@ -3243,7 +4069,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52c9e48f",
+   "id": "57606d08",
    "metadata": {
     "editable": true
    },
@@ -3253,7 +4079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ae37a89",
+   "id": "cf40e3a9",
    "metadata": {
     "editable": true
    },
@@ -3265,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd8fee27",
+   "id": "65038f20",
    "metadata": {
     "editable": true
    },
@@ -3278,7 +4104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de603b44",
+   "id": "deb9890c",
    "metadata": {
     "editable": true
    },
@@ -3290,7 +4116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08e4a0",
+   "id": "1a844813",
    "metadata": {
     "editable": true
    },
@@ -3300,7 +4126,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b65ee2f0",
+   "id": "a32e4148",
    "metadata": {
     "editable": true
    },
@@ -3312,7 +4138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6526b426",
+   "id": "4050a8ed",
    "metadata": {
     "editable": true
    },
@@ -3322,7 +4148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29190fe5",
+   "id": "ec31e4e2",
    "metadata": {
     "editable": true
    },
@@ -3334,7 +4160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71b1585c",
+   "id": "f872c397",
    "metadata": {
     "editable": true
    },
@@ -3346,7 +4172,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e18baaa9",
+   "id": "e8eeb2e5",
    "metadata": {
     "editable": true
    },
@@ -3356,7 +4182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c58b8db",
+   "id": "54d2f39d",
    "metadata": {
     "editable": true
    },
@@ -3368,7 +4194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "238ee8c5",
+   "id": "54cbc3a8",
    "metadata": {
     "editable": true
    },
@@ -3380,7 +4206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46389b82",
+   "id": "7ee728b6",
    "metadata": {
     "editable": true
    },
@@ -3392,7 +4218,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ecbb2d2",
+   "id": "00e9a0eb",
    "metadata": {
     "editable": true
    },
@@ -3402,7 +4228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27368dfb",
+   "id": "50f72b22",
    "metadata": {
     "editable": true
    },
@@ -3414,7 +4240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27082f48",
+   "id": "d53fea2e",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/_build/jupyter_execute/week36.py b/doc/LectureNotes/_build/jupyter_execute/week36.py
index 0ecedfda3..7f96e675c 100644
--- a/doc/LectureNotes/_build/jupyter_execute/week36.py
+++ b/doc/LectureNotes/_build/jupyter_execute/week36.py
@@ -22,15 +22,21 @@
 # 
 # * Material for the lecture on Thursday September 7
 # 
-#   * Linear Regression and links with Statistics, Resampling methods
+#   * Technicalities related to scaling and other issues with data handling
 # 
-#   * Recommended Reading: Goodfellow et al chapter 3 on probability theory, see URL:""
+#   * Linear Regression and links with Statistics
+# 
+#   * [Recommended Reading: Goodfellow et al chapter 3 on probability theory](https://www.deeplearningbook.org/)
 # 
 #   * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)
+# 
+#   * [Video of lecture](https://youtu.be/Kc20CFK0z7Y)  
+# 
+#   * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep7.pdf)
 
 # ## Material for the active learning sessions Tuesday and Wednesday
 # 
-# The material here contains a summary from last Week and discussion of SVD, Ridge and Lasso regression with examples
+# The material here contains a summary from last week and discussion of SVD, Ridge and Lasso regression with examples
 
 # ## Linear Regression and  the SVD
 # 
@@ -844,6 +850,567 @@ plt.show()
 
 # ## Material for lecture Thursday September 7
 
+# ## Important technicalities: More on Rescaling data
+# 
+# When you are comparing your own code with for example **Scikit-Learn**'s
+# library, there are some technicalities to keep in mind.  The examples
+# here demonstrate some of these aspects with potential pitfalls.
+# 
+# The discussion here focuses on the role of the intercept, how we can
+# set up the design matrix, what scaling we should use and other topics
+# which tend  confuse us.
+# 
+# The intercept can be interpreted as the expected value of our
+# target/output variables when all other predictors are set to zero.
+# Thus, if we cannot assume that the expected outputs/targets are zero
+# when all predictors are zero (the columns in the design matrix), it
+# may be a bad idea to implement a model which penalizes the intercept.
+# Furthermore, in for example Ridge and Lasso regression, the default solutions
+# from the library **Scikit-Learn** (when not shrinking $\beta_0$) for the unknown parameters
+# $\boldsymbol{\beta}$, are derived under the assumption that both $\boldsymbol{y}$ and
+# $\boldsymbol{X}$ are zero centered, that is we subtract the mean values.
+# 
+# If our predictors represent different scales, then it is important to
+# standardize the design matrix $\boldsymbol{X}$ by subtracting the mean of each
+# column from the corresponding column and dividing the column with its
+# standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,
+# the results may differ. 
+# 
+# The
+# [Standardscaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)
+# function in **Scikit-Learn** does this for us.  For the data sets we
+# have been studying in our various examples, the data are in many cases
+# already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform  a
+# survey of your data, with a critical assessment of them in case you need to scale the data.
+# 
+# If you need to scale the data, not doing so will give an *unfair*
+# penalization of the parameters since their magnitude depends on the
+# scale of their corresponding predictor.
+# 
+# Suppose as an example that you 
+# you have an input variable given by the heights of different persons.
+# Human height might be measured in inches or meters or
+# kilometers. If measured in kilometers, a standard linear regression
+# model with this predictor would probably give a much bigger
+# coefficient term, than if measured in millimeters.
+# This can clearly lead to problems in evaluating the cost/loss functions.
+# 
+# Keep in mind that when you transform your data set before training a model, the same transformation needs to be done
+# on your eventual new data set  before making a prediction. If we translate this into a Python code, it would could be implemented as
+
+# In[7]:
+
+
+"""
+#Model training, we compute the mean value of y and X
+y_train_mean = np.mean(y_train)
+X_train_mean = np.mean(X_train,axis=0)
+X_train = X_train - X_train_mean
+y_train = y_train - y_train_mean
+
+# The we fit our model with the training data
+trained_model = some_model.fit(X_train,y_train)
+
+
+#Model prediction, we need also to transform our data set used for the prediction.
+X_test = X_test - X_train_mean #Use mean from training data
+y_pred = trained_model(X_test)
+y_pred = y_pred + y_train_mean
+"""
+
+
+# Let us try to understand what this may imply mathematically when we
+# subtract the mean values, also known as *zero centering*. For
+# simplicity, we will focus on  ordinary regression, as done in the above example.
+# 
+# The cost/loss function  for regression is
+
+# $$
+# C(\beta_0, \beta_1, ... , \beta_{p-1}) = \frac{1}{n}\sum_{i=0}^{n} \left(y_i - \beta_0 - \sum_{j=1}^{p-1} X_{ij}\beta_j\right)^2,.
+# $$
+
+# Recall also that we use the squared value. This expression can lead to an
+# increased penalty for higher differences between predicted and
+# output/target values.
+# 
+# What we have done is to single out the $\beta_0$ term in the
+# definition of the mean squared error (MSE).  The design matrix $X$
+# does in this case not contain any intercept column.  When we take the
+# derivative with respect to $\beta_0$, we want the derivative to obey
+
+# $$
+# \frac{\partial C}{\partial \beta_j} = 0,
+# $$
+
+# for all $j$. For $\beta_0$ we have
+
+# $$
+# \frac{\partial C}{\partial \beta_0} = -\frac{2}{n}\sum_{i=0}^{n-1} \left(y_i - \beta_0 - \sum_{j=1}^{p-1} X_{ij} \beta_j\right).
+# $$
+
+# Multiplying away the constant $2/n$, we obtain
+
+# $$
+# \sum_{i=0}^{n-1} \beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} \sum_{j=1}^{p-1} X_{ij} \beta_j.
+# $$
+
+# Let us specialize first to the case where we have only two parameters $\beta_0$ and $\beta_1$.
+# Our result for $\beta_0$ simplifies then to
+
+# $$
+# n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1.
+# $$
+
+# We obtain then
+
+# $$
+# \beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} X_{i1}.
+# $$
+
+# If we define
+
+# $$
+# \mu_{\boldsymbol{x}_1}=\frac{1}{n}\sum_{i=0}^{n-1} X_{i1},
+# $$
+
+# and the mean value of the outputs as
+
+# $$
+# \mu_y=\frac{1}{n}\sum_{i=0}^{n-1}y_i,
+# $$
+
+# we have
+
+# $$
+# \beta_0 = \mu_y - \beta_1\mu_{\boldsymbol{x}_1}.
+# $$
+
+# In the general case with more parameters than $\beta_0$ and $\beta_1$, we have
+
+# $$
+# \beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \frac{1}{n}\sum_{i=0}^{n-1}\sum_{j=1}^{p-1} X_{ij}\beta_j.
+# $$
+
+# We can rewrite the latter equation as
+
+# $$
+# \beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \sum_{j=1}^{p-1} \mu_{\boldsymbol{x}_j}\beta_j,
+# $$
+
+# where we have defined
+
+# $$
+# \mu_{\boldsymbol{x}_j}=\frac{1}{n}\sum_{i=0}^{n-1} X_{ij},
+# $$
+
+# the mean value for all elements of the column vector $\boldsymbol{x}_j$.
+# 
+# Replacing $y_i$ with $y_i - y_i - \overline{\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)
+
+# $$
+# C(\boldsymbol{\beta}) = (\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta})^T(\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta}).
+# $$
+
+# If we minimize with respect to $\boldsymbol{\beta}$ we have then
+
+# $$
+# \hat{\boldsymbol{\beta}} = (\tilde{X}^T\tilde{X})^{-1}\tilde{X}^T\boldsymbol{\tilde{y}},
+# $$
+
+# where $\boldsymbol{\tilde{y}} = \boldsymbol{y} - \overline{\boldsymbol{y}}$
+# and $\tilde{X}_{ij} = X_{ij} - \frac{1}{n}\sum_{k=0}^{n-1}X_{kj}$.
+# 
+# For Ridge regression we need to add $\lambda \boldsymbol{\beta}^T\boldsymbol{\beta}$ to the cost function and get then
+
+# $$
+# \hat{\boldsymbol{\beta}} = (\tilde{X}^T\tilde{X} + \lambda I)^{-1}\tilde{X}^T\boldsymbol{\tilde{y}}.
+# $$
+
+# What does this mean? And why do we insist on all this? Let us look at some examples.
+# 
+# This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (*code example thanks to  Øyvind Sigmundson Schøyen*). Here our scaling of the data is done by subtracting the mean values only.
+# Note also that we do not split the data into training and test.
+
+# In[8]:
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.linear_model import LinearRegression
+
+
+np.random.seed(2021)
+
+def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
+
+
+def fit_beta(X, y):
+    return np.linalg.pinv(X.T @ X) @ X.T @ y
+
+
+true_beta = [2, 0.5, 3.7]
+
+x = np.linspace(0, 1, 11)
+y = np.sum(
+    np.asarray([x ** p * b for p, b in enumerate(true_beta)]), axis=0
+) + 0.1 * np.random.normal(size=len(x))
+
+degree = 3
+X = np.zeros((len(x), degree))
+
+# Include the intercept in the design matrix
+for p in range(degree):
+    X[:, p] = x ** p
+
+beta = fit_beta(X, y)
+
+# Intercept is included in the design matrix
+skl = LinearRegression(fit_intercept=False).fit(X, y)
+
+print(f"True beta: {true_beta}")
+print(f"Fitted beta: {beta}")
+print(f"Sklearn fitted beta: {skl.coef_}")
+ypredictOwn = X @ beta
+ypredictSKL = skl.predict(X)
+print(f"MSE with intercept column")
+print(MSE(y,ypredictOwn))
+print(f"MSE with intercept column from SKL")
+print(MSE(y,ypredictSKL))
+
+
+plt.figure()
+plt.scatter(x, y, label="Data")
+plt.plot(x, X @ beta, label="Fit")
+plt.plot(x, skl.predict(X), label="Sklearn (fit_intercept=False)")
+
+
+# Do not include the intercept in the design matrix
+X = np.zeros((len(x), degree - 1))
+
+for p in range(degree - 1):
+    X[:, p] = x ** (p + 1)
+
+# Intercept is not included in the design matrix
+skl = LinearRegression(fit_intercept=True).fit(X, y)
+
+# Use centered values for X and y when computing coefficients
+y_offset = np.average(y, axis=0)
+X_offset = np.average(X, axis=0)
+
+beta = fit_beta(X - X_offset, y - y_offset)
+intercept = np.mean(y_offset - X_offset @ beta)
+
+print(f"Manual intercept: {intercept}")
+print(f"Fitted beta (wiothout intercept): {beta}")
+print(f"Sklearn intercept: {skl.intercept_}")
+print(f"Sklearn fitted beta (without intercept): {skl.coef_}")
+ypredictOwn = X @ beta
+ypredictSKL = skl.predict(X)
+print(f"MSE with Manual intercept")
+print(MSE(y,ypredictOwn+intercept))
+print(f"MSE with Sklearn intercept")
+print(MSE(y,ypredictSKL))
+
+plt.plot(x, X @ beta + intercept, "--", label="Fit (manual intercept)")
+plt.plot(x, skl.predict(X), "--", label="Sklearn (fit_intercept=True)")
+plt.grid()
+plt.legend()
+
+plt.show()
+
+
+# The intercept is the value of our output/target variable
+# when all our features are zero and our function crosses the $y$-axis (for a one-dimensional case). 
+# 
+# Printing the MSE, we see first that both methods give the same MSE, as
+# they should.  However, when we move to for example Ridge regression,
+# the way we treat the intercept may give a larger or smaller MSE,
+# meaning that the MSE can be penalized by the value of the
+# intercept. Not including the intercept in the fit, means that the
+# regularization term does not include $\beta_0$. For different values
+# of $\lambda$, this may lead to different MSE values. 
+# 
+# To remind the reader, the regularization term, with the intercept in Ridge regression, is given by
+
+# $$
+# \lambda \vert\vert \boldsymbol{\beta} \vert\vert_2^2 = \lambda \sum_{j=0}^{p-1}\beta_j^2,
+# $$
+
+# but when we take out the intercept, this equation becomes
+
+# $$
+# \lambda \vert\vert \boldsymbol{\beta} \vert\vert_2^2 = \lambda \sum_{j=1}^{p-1}\beta_j^2.
+# $$
+
+# For Lasso regression we have
+
+# $$
+# \lambda \vert\vert \boldsymbol{\beta} \vert\vert_1 = \lambda \sum_{j=1}^{p-1}\vert\beta_j\vert.
+# $$
+
+# It means that, when scaling the design matrix and the outputs/targets,
+# by subtracting the mean values, we have an optimization problem which
+# is not penalized by the intercept. The MSE value can then be smaller
+# since it focuses only on the remaining quantities. If we however bring
+# back the intercept, we will get a MSE which then contains the
+# intercept.
+# 
+# Armed with this wisdom, we attempt first to simply set the intercept equal to **False** in our implementation of Ridge regression for our well-known  vanilla data set.
+
+# In[9]:
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn import linear_model
+
+def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
+
+
+# A seed just to ensure that the random numbers are the same for every run.
+# Useful for eventual debugging.
+np.random.seed(3155)
+
+n = 100
+x = np.random.rand(n)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)
+
+Maxpolydegree = 20
+X = np.zeros((n,Maxpolydegree))
+#We include explicitely the intercept column
+for degree in range(Maxpolydegree):
+    X[:,degree] = x**degree
+# We split the data in test and training data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+p = Maxpolydegree
+I = np.eye(p,p)
+# Decide which values of lambda to use
+nlambdas = 6
+MSEOwnRidgePredict = np.zeros(nlambdas)
+MSERidgePredict = np.zeros(nlambdas)
+lambdas = np.logspace(-4, 2, nlambdas)
+for i in range(nlambdas):
+    lmb = lambdas[i]
+    OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train
+    # Note: we include the intercept column and no scaling
+    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)
+    RegRidge.fit(X_train,y_train)
+    # and then make the prediction
+    ytildeOwnRidge = X_train @ OwnRidgeBeta
+    ypredictOwnRidge = X_test @ OwnRidgeBeta
+    ytildeRidge = RegRidge.predict(X_train)
+    ypredictRidge = RegRidge.predict(X_test)
+    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)
+    MSERidgePredict[i] = MSE(y_test,ypredictRidge)
+    print("Beta values for own Ridge implementation")
+    print(OwnRidgeBeta)
+    print("Beta values for Scikit-Learn Ridge implementation")
+    print(RegRidge.coef_)
+    print("MSE values for own Ridge implementation")
+    print(MSEOwnRidgePredict[i])
+    print("MSE values for Scikit-Learn Ridge implementation")
+    print(MSERidgePredict[i])
+
+# Now plot the results
+plt.figure()
+plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE own Ridge Test')
+plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')
+
+plt.xlabel('log10(lambda)')
+plt.ylabel('MSE')
+plt.legend()
+plt.show()
+
+
+# The results here agree when we force **Scikit-Learn**'s Ridge function to include the first column in our design matrix.
+# We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.
+# What happens if we do not include the intercept in our fit?
+# Let us see how we can change this code by zero centering.
+
+# In[10]:
+
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn import linear_model
+from sklearn.preprocessing import StandardScaler
+
+def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
+# A seed just to ensure that the random numbers are the same for every run.
+# Useful for eventual debugging.
+np.random.seed(315)
+
+n = 100
+x = np.random.rand(n)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)
+
+Maxpolydegree = 20
+X = np.zeros((n,Maxpolydegree-1))
+
+for degree in range(1,Maxpolydegree): #No intercept column
+    X[:,degree-1] = x**(degree)
+
+# We split the data in test and training data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable
+X_train_mean = np.mean(X_train,axis=0)
+#Center by removing mean from each feature
+X_train_scaled = X_train - X_train_mean 
+X_test_scaled = X_test - X_train_mean
+#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)
+#Remove the intercept from the training data.
+y_scaler = np.mean(y_train)           
+y_train_scaled = y_train - y_scaler   
+
+p = Maxpolydegree-1
+I = np.eye(p,p)
+# Decide which values of lambda to use
+nlambdas = 6
+MSEOwnRidgePredict = np.zeros(nlambdas)
+MSERidgePredict = np.zeros(nlambdas)
+
+lambdas = np.logspace(-4, 2, nlambdas)
+for i in range(nlambdas):
+    lmb = lambdas[i]
+    OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)
+    intercept_ = y_scaler - X_train_mean@OwnRidgeBeta #The intercept can be shifted so the model can predict on uncentered data
+    #Add intercept to prediction
+    ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler 
+    RegRidge = linear_model.Ridge(lmb)
+    RegRidge.fit(X_train,y_train)
+    ypredictRidge = RegRidge.predict(X_test)
+    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)
+    MSERidgePredict[i] = MSE(y_test,ypredictRidge)
+    print("Beta values for own Ridge implementation")
+    print(OwnRidgeBeta) #Intercept is given by mean of target variable
+    print("Beta values for Scikit-Learn Ridge implementation")
+    print(RegRidge.coef_)
+    print('Intercept from own implementation:')
+    print(intercept_)
+    print('Intercept from Scikit-Learn Ridge implementation')
+    print(RegRidge.intercept_)
+    print("MSE values for own Ridge implementation")
+    print(MSEOwnRidgePredict[i])
+    print("MSE values for Scikit-Learn Ridge implementation")
+    print(MSERidgePredict[i])
+
+
+# Now plot the results
+plt.figure()
+plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test')
+plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')
+plt.xlabel('log10(lambda)')
+plt.ylabel('MSE')
+plt.legend()
+plt.show()
+
+
+# We see here, when compared to the code which includes explicitely the
+# intercept column, that our MSE value is actually smaller. This is
+# because the regularization term does not include the intercept value
+# $\beta_0$ in the fitting.  This applies to Lasso regularization as
+# well.  It means that our optimization is now done only with the
+# centered matrix and/or vector that enter the fitting procedure.
+
+# ## Test Function for what happens with OLS, Ridge and Lasso
+# 
+# Hitherto we have discussed Ridge and Lasso regression in terms of a
+# linear analysis. This may to many of you feel rather technical and
+# perhaps not that intuitive. The question is whether we can develop a
+# more intuitive way of understanding what Ridge and Lasso express.
+# 
+# Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit. 
+# 
+# We will play around with a study of the values for the optimal
+# parameters $\boldsymbol{\beta}$ using OLS, Ridge and Lasso regression.  For
+# OLS, you will notice as function of the noise and polynomial degree,
+# that the parameters $\beta$ will fluctuate from order to order in the
+# polynomial fit and that for larger and larger polynomial degrees of freedom, the parameters will tend to increase in value for OLS.
+# 
+# For Ridge and Lasso regression, the higher order parameters will typically be reduced, providing thereby less fluctuations from one order to another one.
+
+# In[11]:
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn import linear_model
+
+def R2(y_data, y_model):
+    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
+def MSE(y_data,y_model):
+    n = np.size(y_model)
+    return np.sum((y_data-y_model)**2)/n
+
+# Make data set.
+n = 10000
+x = np.random.rand(n)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)
+
+Maxpolydegree = 5
+X = np.zeros((len(x),Maxpolydegree))
+X[:,0] = 1.0
+
+
+for polydegree in range(1,Maxpolydegree):
+    X[:,polydegree] = x**(polydegree)
+
+# We split the data in test and training data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# matrix inversion to find beta
+OLSbeta = np.linalg.pinv(X_train.T @ X_train) @ X_train.T @ y_train
+print(OLSbeta)
+ypredictOLS = X_test @ OLSbeta
+print("Test MSE OLS")
+print(MSE(y_test,ypredictOLS))
+# Repeat now for Lasso and Ridge regression and various values of the regularization parameter using Scikit-Learn
+# Decide which values of lambda to use
+nlambdas = 4
+MSERidgePredict = np.zeros(nlambdas)
+MSELassoPredict = np.zeros(nlambdas)
+lambdas = np.logspace(-3, 1, nlambdas)
+for i in range(nlambdas):
+    lmb = lambdas[i]
+    # Make the fit using Ridge and Lasso
+    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)
+    RegRidge.fit(X_train,y_train)
+    RegLasso = linear_model.Lasso(lmb,fit_intercept=False)
+    RegLasso.fit(X_train,y_train)
+    # and then make the prediction
+    ypredictRidge = RegRidge.predict(X_test)
+    ypredictLasso = RegLasso.predict(X_test)
+    # Compute the MSE and print it
+    MSERidgePredict[i] = MSE(y_test,ypredictRidge)
+    MSELassoPredict[i] = MSE(y_test,ypredictLasso)
+    print(lmb,RegRidge.coef_)
+    print(lmb,RegLasso.coef_)
+# Now plot the results
+plt.figure()
+plt.plot(np.log10(lambdas), MSERidgePredict, 'b', label = 'MSE Ridge Test')
+plt.plot(np.log10(lambdas), MSELassoPredict, 'r', label = 'MSE Lasso Test')
+plt.xlabel('log10(lambda)')
+plt.ylabel('MSE')
+plt.legend()
+plt.show()
+
+
+# How can we understand this?
+
 # ## Linking the regression analysis with a statistical interpretation
 # 
 # We will now couple the discussions of ordinary least squares, Ridge
@@ -1227,95 +1794,6 @@ plt.show()
 
 # ## Bayes' Theorem and Ridge and Lasso Regression
 # 
-# Hitherto we have discussed Ridge and Lasso regression in terms of a
-# linear analysis. This may to many of you feel rather technical and
-# perhaps not that intuitive. The question is whether we can develop a
-# more intuitive way of understanding what Ridge and Lasso express.
-# 
-# Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit.
-
-# ## Test Function for what happens with OLS, Ridge and Lasso
-# 
-# We will play around with a study of the values for the optimal
-# parameters $\boldsymbol{\beta}$ using OLS, Ridge and Lasso regression.  For
-# OLS, you will notice as function of the noise and polynomial degree,
-# that the parameters $\beta$ will fluctuate from order to order in the
-# polynomial fit and that for larger and larger polynomial degrees of freedom, the parameters will tend to increase in value for OLS.
-# 
-# For Ridge and Lasso regression, the higher order parameters will typically be reduced, providing thereby less fluctuations from one order to another one.
-
-# In[7]:
-
-
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.model_selection import train_test_split
-from sklearn import linear_model
-
-def R2(y_data, y_model):
-    return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)
-def MSE(y_data,y_model):
-    n = np.size(y_model)
-    return np.sum((y_data-y_model)**2)/n
-
-# Make data set.
-n = 10000
-x = np.random.rand(n)
-y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)
-
-Maxpolydegree = 5
-X = np.zeros((len(x),Maxpolydegree))
-X[:,0] = 1.0
-
-for polydegree in range(1, Maxpolydegree):
-    for degree in range(polydegree):
-        X[:,degree] = x**(degree)
-
-
-# We split the data in test and training data
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-
-# matrix inversion to find beta
-OLSbeta = np.linalg.pinv(X_train.T @ X_train) @ X_train.T @ y_train
-print(OLSbeta)
-ypredictOLS = X_test @ OLSbeta
-print("Test MSE OLS")
-print(MSE(y_test,ypredictOLS))
-# Repeat now for Lasso and Ridge regression and various values of the regularization parameter using Scikit-Learn
-# Decide which values of lambda to use
-nlambdas = 4
-MSERidgePredict = np.zeros(nlambdas)
-MSELassoPredict = np.zeros(nlambdas)
-lambdas = np.logspace(-3, 1, nlambdas)
-for i in range(nlambdas):
-    lmb = lambdas[i]
-    # Make the fit using Ridge and Lasso
-    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)
-    RegRidge.fit(X_train,y_train)
-    RegLasso = linear_model.Lasso(lmb,fit_intercept=False)
-    RegLasso.fit(X_train,y_train)
-    # and then make the prediction
-    ypredictRidge = RegRidge.predict(X_test)
-    ypredictLasso = RegLasso.predict(X_test)
-    # Compute the MSE and print it
-    MSERidgePredict[i] = MSE(y_test,ypredictRidge)
-    MSELassoPredict[i] = MSE(y_test,ypredictLasso)
-    print(lmb,RegRidge.coef_)
-    print(lmb,RegLasso.coef_)
-# Now plot the results
-plt.figure()
-plt.plot(np.log10(lambdas), MSERidgePredict, 'b', label = 'MSE Ridge Test')
-plt.plot(np.log10(lambdas), MSELassoPredict, 'r', label = 'MSE Lasso Test')
-plt.xlabel('log10(lambda)')
-plt.ylabel('MSE')
-plt.legend()
-plt.show()
-
-
-# How can we understand this?
-
-# ## Invoking Bayes' theorem
-# 
 # Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. 
 # 
 # For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\boldsymbol{D}$ (one-dimensional case)
diff --git a/doc/LectureNotes/exercisesweek37.ipynb b/doc/LectureNotes/exercisesweek37.ipynb
index c565e7b07..39e531a60 100644
--- a/doc/LectureNotes/exercisesweek37.ipynb
+++ b/doc/LectureNotes/exercisesweek37.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cfebae03",
+   "id": "1ff4a4e9",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "353ebb07",
+   "id": "0569f7a9",
    "metadata": {
     "editable": true
    },
@@ -27,25 +27,34 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c02815dd",
+   "id": "6f34e507",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Overarching aims of the exercises this week\n",
     "\n",
-    "This exercise deals with various mean values and variances in  linear regression method (here it may be useful to look up chapter 3, equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer](https://www.springer.com/gp/book/9780387848570)). The exercise is also a part of project 1 and can be reused in the theory part of the project.\n",
+    "This exercise deals with various mean values and variances in linear\n",
+    "regression method (here it may be useful to look up chapter 3,\n",
+    "equation (3.8) of [Trevor Hastie, Robert Tibshirani, Jerome\n",
+    "H. Friedman, The Elements of Statistical Learning,\n",
+    "Springer](https://www.springer.com/gp/book/9780387848570)). The\n",
+    "exercise is also a part of project 1 and can be reused in the theory\n",
+    "part of the project.\n",
     "\n",
-    "For more discussions on Ridge regression and calculation of expectation values, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
+    "For more discussions on Ridge regression and calculation of\n",
+    "expectation values, [Wessel van\n",
+    "Wieringen's](https://arxiv.org/abs/1509.09169) article is highly\n",
+    "recommended.\n",
     "\n",
-    "The assumption we have made is \n",
-    "that there exists a continuous function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0, \\sigma^2)$\n",
-    "which describes our data"
+    "The assumption we have made is that there exists a continuous function\n",
+    "$f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim N(0,\n",
+    "\\sigma^2)$ which describes our data"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb152043",
+   "id": "3ff574a4",
    "metadata": {
     "editable": true
    },
@@ -57,7 +66,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aba7a46",
+   "id": "096a3165",
    "metadata": {
     "editable": true
    },
@@ -68,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be52c097",
+   "id": "cc9fb258",
    "metadata": {
     "editable": true
    },
@@ -80,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c54979e5",
+   "id": "daf4eb5d",
    "metadata": {
     "editable": true
    },
@@ -90,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b1eb811",
+   "id": "3b491679",
    "metadata": {
     "editable": true
    },
@@ -102,7 +111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "036a5440",
+   "id": "93a49739",
    "metadata": {
     "editable": true
    },
@@ -114,7 +123,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7fb8094",
+   "id": "a3fa80db",
    "metadata": {
     "editable": true
    },
@@ -125,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7d92995",
+   "id": "e6e2defd",
    "metadata": {
     "editable": true
    },
@@ -137,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29eb9701",
+   "id": "9aa19afa",
    "metadata": {
     "editable": true
    },
@@ -150,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa106511",
+   "id": "5fa82a47",
    "metadata": {
     "editable": true
    },
@@ -162,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58be1091",
+   "id": "33099abb",
    "metadata": {
     "editable": true
    },
@@ -172,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cb58e41",
+   "id": "5d03f001",
    "metadata": {
     "editable": true
    },
@@ -184,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a04df28d",
+   "id": "94bd151d",
    "metadata": {
     "editable": true
    },
@@ -195,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c095c96a",
+   "id": "f0b84941",
    "metadata": {
     "editable": true
    },
@@ -207,20 +216,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d425a79",
+   "id": "60370a63",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
+    "\\mathbb{E} \\big[ \\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\\n",
     "\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "fb89cfab",
+   "id": "af251217",
    "metadata": {
     "editable": true
    },
@@ -233,19 +242,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea8801c",
+   "id": "02e978da",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\mbox{Var}[\\hat{\\boldsymbol{\\beta}}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T}\\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e885693",
+   "id": "3656a896",
    "metadata": {
     "editable": true
    },
diff --git a/doc/LectureNotes/week36.ipynb b/doc/LectureNotes/week36.ipynb
index bdaee795a..ededd9147 100644
--- a/doc/LectureNotes/week36.ipynb
+++ b/doc/LectureNotes/week36.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7411173e",
+   "id": "2b900fea",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f50db913",
+   "id": "2286e211",
    "metadata": {
     "editable": true
    },
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a90e380",
+   "id": "edbcd7f9",
    "metadata": {
     "editable": true
    },
@@ -44,28 +44,34 @@
     "\n",
     "* Material for the lecture on Thursday September 7\n",
     "\n",
-    "  * Linear Regression and links with Statistics, Resampling methods\n",
+    "  * Technicalities related to scaling and other issues with data handling\n",
     "\n",
-    "  * Recommended Reading: Goodfellow et al chapter 3 on probability theory, see URL:\"\"\n",
+    "  * Linear Regression and links with Statistics\n",
     "\n",
-    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)"
+    "  * [Recommended Reading: Goodfellow et al chapter 3 on probability theory](https://www.deeplearningbook.org/)\n",
+    "\n",
+    "  * See also Murphy, sections 2.4 (Gaussian distributions) and 3.2 (Bayesian Statistics, basis)\n",
+    "\n",
+    "  * [Video of lecture](https://youtu.be/Kc20CFK0z7Y)  \n",
+    "\n",
+    "  * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesSep7.pdf)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "351d36fd",
+   "id": "5dbf3ff5",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Material for the active learning sessions Tuesday and Wednesday\n",
     "\n",
-    "The material here contains a summary from last Week and discussion of SVD, Ridge and Lasso regression with examples"
+    "The material here contains a summary from last week and discussion of SVD, Ridge and Lasso regression with examples"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0f9f4d2f",
+   "id": "2dae8175",
    "metadata": {
     "editable": true
    },
@@ -77,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ec36bdd",
+   "id": "fb3d486d",
    "metadata": {
     "editable": true
    },
@@ -89,7 +95,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "614493c6",
+   "id": "5f8492a1",
    "metadata": {
     "editable": true
    },
@@ -99,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc4be423",
+   "id": "a35422b4",
    "metadata": {
     "editable": true
    },
@@ -111,7 +117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b262332c",
+   "id": "aef923ea",
    "metadata": {
     "editable": true
    },
@@ -121,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ab1829a4",
+   "id": "5da8277b",
    "metadata": {
     "editable": true
    },
@@ -138,7 +144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c82dd9e",
+   "id": "eb34e530",
    "metadata": {
     "editable": true
    },
@@ -148,7 +154,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bab8721",
+   "id": "a2a33728",
    "metadata": {
     "editable": true
    },
@@ -160,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72273a4f",
+   "id": "9bea1837",
    "metadata": {
     "editable": true
    },
@@ -170,7 +176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4af03ea",
+   "id": "af711505",
    "metadata": {
     "editable": true
    },
@@ -182,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0d35853",
+   "id": "f02b3738",
    "metadata": {
     "editable": true
    },
@@ -196,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db5ea827",
+   "id": "d3fe4855",
    "metadata": {
     "editable": true
    },
@@ -208,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6b341ce",
+   "id": "7b5fa376",
    "metadata": {
     "editable": true
    },
@@ -230,7 +236,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db723709",
+   "id": "d212be4e",
    "metadata": {
     "editable": true
    },
@@ -242,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e600268",
+   "id": "eca13576",
    "metadata": {
     "editable": true
    },
@@ -257,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9452938",
+   "id": "fbf0e2be",
    "metadata": {
     "editable": true
    },
@@ -269,7 +275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53fea1e6",
+   "id": "acbd86ec",
    "metadata": {
     "editable": true
    },
@@ -281,7 +287,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed10baad",
+   "id": "8887e338",
    "metadata": {
     "editable": true
    },
@@ -291,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4d0db96",
+   "id": "e95bcbda",
    "metadata": {
     "editable": true
    },
@@ -303,7 +309,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fae3cadc",
+   "id": "e66200c2",
    "metadata": {
     "editable": true
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@@ -313,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e973c775",
+   "id": "a0c3692c",
    "metadata": {
     "editable": true
    },
@@ -325,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f88a33f4",
+   "id": "8d13a9ff",
    "metadata": {
     "editable": true
    },
@@ -335,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bc7214d",
+   "id": "31c3dd91",
    "metadata": {
     "editable": true
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@@ -347,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "561d4c91",
+   "id": "87a6b291",
    "metadata": {
     "editable": true
    },
@@ -364,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0eda2d1e",
+   "id": "3bb9eed3",
    "metadata": {
     "editable": true
    },
@@ -378,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c95dbc32",
+   "id": "ba123060",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -392,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77079d25",
+   "id": "46fdfd39",
    "metadata": {
     "editable": true
    },
@@ -403,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cd0ecc44",
+   "id": "36e47fde",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -443,7 +449,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41d896dc",
+   "id": "53878874",
    "metadata": {
     "editable": true
    },
@@ -463,7 +469,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f594852c",
+   "id": "87546753",
    "metadata": {
     "editable": true
    },
@@ -475,7 +481,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b78264f2",
+   "id": "d8a4669d",
    "metadata": {
     "editable": true
    },
@@ -486,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d05eff95",
+   "id": "b6523ecb",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -520,7 +526,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fae32c6",
+   "id": "2fce573f",
    "metadata": {
     "editable": true
    },
@@ -530,7 +536,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8631011a",
+   "id": "840991d3",
    "metadata": {
     "editable": true
    },
@@ -543,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e7701de",
+   "id": "4df7aac6",
    "metadata": {
     "editable": true
    },
@@ -555,7 +561,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aea7230b",
+   "id": "3f3acc05",
    "metadata": {
     "editable": true
    },
@@ -565,7 +571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5029213",
+   "id": "2d1218f6",
    "metadata": {
     "editable": true
    },
@@ -578,7 +584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "535ad70e",
+   "id": "01624e31",
    "metadata": {
     "editable": true
    },
@@ -588,7 +594,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "159ae526",
+   "id": "616fe159",
    "metadata": {
     "editable": true
    },
@@ -600,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f25dfa4",
+   "id": "27cf2acd",
    "metadata": {
     "editable": true
    },
@@ -615,7 +621,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d187806",
+   "id": "2ff1e0d2",
    "metadata": {
     "editable": true
    },
@@ -628,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c692db5",
+   "id": "aef1996b",
    "metadata": {
     "editable": true
    },
@@ -642,7 +648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3404e0b4",
+   "id": "75a44cc9",
    "metadata": {
     "editable": true
    },
@@ -654,7 +660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bc49666",
+   "id": "e2ba6037",
    "metadata": {
     "editable": true
    },
@@ -664,7 +670,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08dfa1",
+   "id": "95517053",
    "metadata": {
     "editable": true
    },
@@ -677,7 +683,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30bcc0cd",
+   "id": "5f15d4ea",
    "metadata": {
     "editable": true
    },
@@ -689,7 +695,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d311a7d8",
+   "id": "68579bd0",
    "metadata": {
     "editable": true
    },
@@ -701,7 +707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32c46b73",
+   "id": "53887938",
    "metadata": {
     "editable": true
    },
@@ -713,7 +719,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cceda87",
+   "id": "469c8c35",
    "metadata": {
     "editable": true
    },
@@ -725,7 +731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10a16675",
+   "id": "a7521a38",
    "metadata": {
     "editable": true
    },
@@ -739,7 +745,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6dba20bf",
+   "id": "cdfddb46",
    "metadata": {
     "editable": true
    },
@@ -751,7 +757,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9325f924",
+   "id": "e87c8606",
    "metadata": {
     "editable": true
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@@ -761,7 +767,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "745d61e9",
+   "id": "3e06d524",
    "metadata": {
     "editable": true
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@@ -773,7 +779,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3866af64",
+   "id": "798d77a9",
    "metadata": {
     "editable": true
    },
@@ -783,7 +789,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "710de288",
+   "id": "8808de88",
    "metadata": {
     "editable": true
    },
@@ -795,7 +801,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5893fba0",
+   "id": "c3825a07",
    "metadata": {
     "editable": true
    },
@@ -807,7 +813,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ef8c59f",
+   "id": "868b6a19",
    "metadata": {
     "editable": true
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@@ -817,7 +823,7 @@
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   {
    "cell_type": "markdown",
-   "id": "79b257ae",
+   "id": "eaba5f13",
    "metadata": {
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@@ -828,7 +834,7 @@
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   {
    "cell_type": "markdown",
-   "id": "46270996",
+   "id": "c9cc77c3",
    "metadata": {
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@@ -840,7 +846,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "785aebe9",
+   "id": "4b3e1232",
    "metadata": {
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@@ -856,7 +862,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "443f3a3f",
+   "id": "d59ed944",
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@@ -869,7 +875,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cfb8d4c0",
+   "id": "e81dd0be",
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@@ -881,7 +887,7 @@
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   {
    "cell_type": "markdown",
-   "id": "2be1ca1f",
+   "id": "f26a385c",
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@@ -891,7 +897,7 @@
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   {
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-   "id": "9ed0aec2",
+   "id": "aa00d97a",
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@@ -903,7 +909,7 @@
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-   "id": "943b1cd5",
+   "id": "17ba8d1c",
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@@ -913,7 +919,7 @@
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   {
    "cell_type": "markdown",
-   "id": "4a8a6bff",
+   "id": "8cf9dc8f",
    "metadata": {
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@@ -925,7 +931,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0371965",
+   "id": "f0c43d20",
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@@ -937,7 +943,7 @@
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   {
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-   "id": "38e7b497",
+   "id": "5b119d73",
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@@ -953,7 +959,7 @@
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   {
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-   "id": "682f90db",
+   "id": "1efba158",
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@@ -965,7 +971,7 @@
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-   "id": "b5fb7771",
+   "id": "e5943a40",
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@@ -977,7 +983,7 @@
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   {
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-   "id": "0c228525",
+   "id": "167a1a9e",
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@@ -987,7 +993,7 @@
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-   "id": "08a400af",
+   "id": "924b5c9b",
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@@ -999,7 +1005,7 @@
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+   "id": "56ac0a65",
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@@ -1009,7 +1015,7 @@
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@@ -1021,7 +1027,7 @@
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+   "id": "12ef13b2",
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@@ -1038,7 +1044,7 @@
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+   "id": "d33c685e",
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@@ -1050,7 +1056,7 @@
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+   "id": "b30d436d",
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@@ -1062,7 +1068,7 @@
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@@ -1072,7 +1078,7 @@
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@@ -1084,7 +1090,7 @@
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+   "id": "b14d10ff",
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@@ -1094,7 +1100,7 @@
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+   "id": "672b64b5",
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@@ -1106,7 +1112,7 @@
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+   "id": "ef321df6",
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@@ -1116,7 +1122,7 @@
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+   "id": "e67e683d",
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@@ -1128,7 +1134,7 @@
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+   "id": "b9bd1d3f",
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@@ -1138,7 +1144,7 @@
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+   "id": "b705fb0b",
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@@ -1153,7 +1159,7 @@
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+   "id": "03679605",
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@@ -1165,7 +1171,7 @@
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+   "id": "b58e9ee7",
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@@ -1175,7 +1181,7 @@
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+   "id": "42d8e1b3",
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@@ -1187,7 +1193,7 @@
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   {
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-   "id": "bf6316a2",
+   "id": "db624348",
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@@ -1199,7 +1205,7 @@
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@@ -1211,7 +1217,7 @@
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@@ -1221,7 +1227,7 @@
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@@ -1233,7 +1239,7 @@
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   {
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@@ -1245,7 +1251,7 @@
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@@ -1257,7 +1263,7 @@
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@@ -1267,7 +1273,7 @@
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@@ -1279,7 +1285,7 @@
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@@ -1289,7 +1295,7 @@
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@@ -1449,7 +1455,7 @@
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@@ -1459,7 +1465,7 @@
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@@ -1471,7 +1477,7 @@
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@@ -1484,7 +1490,7 @@
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@@ -1508,7 +1514,7 @@
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@@ -1520,7 +1526,7 @@
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@@ -1530,7 +1536,7 @@
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@@ -1542,7 +1548,7 @@
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@@ -1564,7 +1570,7 @@
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@@ -1574,7 +1580,7 @@
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@@ -1655,7 +1661,7 @@
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@@ -1706,7 +1712,7 @@
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@@ -1718,7 +1724,7 @@
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@@ -1728,7 +1734,7 @@
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@@ -1740,7 +1746,7 @@
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@@ -1750,7 +1756,7 @@
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@@ -1762,7 +1768,7 @@
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@@ -1787,7 +1793,7 @@
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-   "id": "92cd1361",
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@@ -1849,7 +1855,7 @@
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@@ -1859,7 +1865,7 @@
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@@ -1870,7 +1876,7 @@
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@@ -1937,7 +1943,7 @@
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@@ -1948,7 +1954,7 @@
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@@ -2037,7 +2043,7 @@
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@@ -2047,962 +2053,850 @@
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   {
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-   "id": "1232037e",
+   "id": "242933c8",
    "metadata": {
     "editable": true
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    "source": [
-    "## Linking the regression analysis with a statistical interpretation\n",
+    "## Important technicalities: More on Rescaling data\n",
     "\n",
-    "We will now couple the discussions of ordinary least squares, Ridge\n",
-    "and Lasso regression with a statistical interpretation, that is we\n",
-    "move from a linear algebra analysis to a statistical analysis. In\n",
-    "particular, we will focus on what the regularization terms can result\n",
-    "in.  We will amongst other things show that the regularization\n",
-    "parameter can reduce considerably the variance of the parameters\n",
-    "$\\beta$.\n",
+    "When you are comparing your own code with for example **Scikit-Learn**'s\n",
+    "library, there are some technicalities to keep in mind.  The examples\n",
+    "here demonstrate some of these aspects with potential pitfalls.\n",
+    "\n",
+    "The discussion here focuses on the role of the intercept, how we can\n",
+    "set up the design matrix, what scaling we should use and other topics\n",
+    "which tend  confuse us.\n",
+    "\n",
+    "The intercept can be interpreted as the expected value of our\n",
+    "target/output variables when all other predictors are set to zero.\n",
+    "Thus, if we cannot assume that the expected outputs/targets are zero\n",
+    "when all predictors are zero (the columns in the design matrix), it\n",
+    "may be a bad idea to implement a model which penalizes the intercept.\n",
+    "Furthermore, in for example Ridge and Lasso regression, the default solutions\n",
+    "from the library **Scikit-Learn** (when not shrinking $\\beta_0$) for the unknown parameters\n",
+    "$\\boldsymbol{\\beta}$, are derived under the assumption that both $\\boldsymbol{y}$ and\n",
+    "$\\boldsymbol{X}$ are zero centered, that is we subtract the mean values.\n",
+    "\n",
+    "If our predictors represent different scales, then it is important to\n",
+    "standardize the design matrix $\\boldsymbol{X}$ by subtracting the mean of each\n",
+    "column from the corresponding column and dividing the column with its\n",
+    "standard deviation. Most machine learning libraries do this as a default. This means that if you compare your code with the results from a given library,\n",
+    "the results may differ. \n",
     "\n",
     "The\n",
-    "advantage of doing linear regression is that we actually end up with\n",
-    "analytical expressions for several statistical quantities.  \n",
-    "Standard least squares and Ridge regression  allow us to\n",
-    "derive quantities like the variance and other expectation values in a\n",
-    "rather straightforward way.\n",
+    "[Standardscaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html)\n",
+    "function in **Scikit-Learn** does this for us.  For the data sets we\n",
+    "have been studying in our various examples, the data are in many cases\n",
+    "already scaled and there is no need to scale them. You as a user of different machine learning algorithms, should always perform  a\n",
+    "survey of your data, with a critical assessment of them in case you need to scale the data.\n",
     "\n",
-    "It is assumed that $\\varepsilon_i\n",
-    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
-    "independent, i.e.:"
+    "If you need to scale the data, not doing so will give an *unfair*\n",
+    "penalization of the parameters since their magnitude depends on the\n",
+    "scale of their corresponding predictor.\n",
+    "\n",
+    "Suppose as an example that you \n",
+    "you have an input variable given by the heights of different persons.\n",
+    "Human height might be measured in inches or meters or\n",
+    "kilometers. If measured in kilometers, a standard linear regression\n",
+    "model with this predictor would probably give a much bigger\n",
+    "coefficient term, than if measured in millimeters.\n",
+    "This can clearly lead to problems in evaluating the cost/loss functions.\n",
+    "\n",
+    "Keep in mind that when you transform your data set before training a model, the same transformation needs to be done\n",
+    "on your eventual new data set  before making a prediction. If we translate this into a Python code, it would could be implemented as"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0e71daa5",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "#Model training, we compute the mean value of y and X\n",
+    "y_train_mean = np.mean(y_train)\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "X_train = X_train - X_train_mean\n",
+    "y_train = y_train - y_train_mean\n",
+    "\n",
+    "# The we fit our model with the training data\n",
+    "trained_model = some_model.fit(X_train,y_train)\n",
+    "\n",
+    "\n",
+    "#Model prediction, we need also to transform our data set used for the prediction.\n",
+    "X_test = X_test - X_train_mean #Use mean from training data\n",
+    "y_pred = trained_model(X_test)\n",
+    "y_pred = y_pred + y_train_mean\n",
+    "\"\"\""
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e50205be",
+   "id": "1f0eb0d0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Let us try to understand what this may imply mathematically when we\n",
+    "subtract the mean values, also known as *zero centering*. For\n",
+    "simplicity, we will focus on  ordinary regression, as done in the above example.\n",
+    "\n",
+    "The cost/loss function  for regression is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ffc4acb",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
-    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
-    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
-    "\\end{align*}\n",
+    "C(\\beta_0, \\beta_1, ... , \\beta_{p-1}) = \\frac{1}{n}\\sum_{i=0}^{n} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij}\\beta_j\\right)^2,.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70eccda9",
+   "id": "2511c028",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The randomness of $\\varepsilon_i$ implies that\n",
-    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
-    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
-    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
-    "non-random scalar. To specify the parameters of the distribution of\n",
-    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "Recall also that we use the squared value. This expression can lead to an\n",
+    "increased penalty for higher differences between predicted and\n",
+    "output/target values.\n",
     "\n",
-    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
-    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
-    "row number $i$ and perform a sum over all values $p$."
+    "What we have done is to single out the $\\beta_0$ term in the\n",
+    "definition of the mean squared error (MSE).  The design matrix $X$\n",
+    "does in this case not contain any intercept column.  When we take the\n",
+    "derivative with respect to $\\beta_0$, we want the derivative to obey"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e6bdcbf2",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Assumptions made\n",
-    "\n",
-    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
-    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
-    "which describe our data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d74471b8",
+   "id": "908beb7f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "\\frac{\\partial C}{\\partial \\beta_j} = 0,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f7b178d9",
+   "id": "5fae2a94",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
-    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+    "for all $j$. For $\\beta_0$ we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "8ea16c62",
+   "id": "d7729a27",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "\\frac{\\partial C}{\\partial \\beta_0} = -\\frac{2}{n}\\sum_{i=0}^{n-1} \\left(y_i - \\beta_0 - \\sum_{j=1}^{p-1} X_{ij} \\beta_j\\right).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4a8d7bfd",
+   "id": "7fbb2ded",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Expectation value and variance\n",
-    "\n",
-    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+    "Multiplying away the constant $2/n$, we obtain"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5367d943",
+   "id": "8cef22ce",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \n",
-    "\\mathbb{E}(y_i) & =\n",
-    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
-    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
-    "\\end{align*}\n",
+    "\\sum_{i=0}^{n-1} \\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} \\sum_{j=1}^{p-1} X_{ij} \\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c7eefd85",
+   "id": "c38ab3a0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "while\n",
-    "its variance is"
+    "Let us specialize first to the case where we have only two parameters $\\beta_0$ and $\\beta_1$.\n",
+    "Our result for $\\beta_0$ simplifies then to"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27ce343a",
+   "id": "c47e7bbe",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
-    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
-    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
-    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
-    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
-    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
-    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
-    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
-    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
-    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
-    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
-    "\\end{align*}\n",
+    "n\\beta_0 = \\sum_{i=0}^{n-1}y_i - \\sum_{i=0}^{n-1} X_{i1} \\beta_1.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c67ea20b",
+   "id": "06962abd",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
-    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+    "We obtain then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b4da473a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
-    "\n",
-    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af812d38",
+   "id": "14c0a845",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "9d3aaff5",
+   "id": "5f50f223",
    "metadata": {
     "editable": true
    },
    "source": [
-    "This means that the estimator of the regression parameters is unbiased.\n",
-    "\n",
-    "We can also calculate the variance\n",
-    "\n",
-    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+    "If we define"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eebea8b7",
+   "id": "188ed44d",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\begin{eqnarray*}\n",
-    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
-    "\\\\\n",
-    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
-    "\\\\\n",
-    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\\\\n",
-    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "% \\\\\n",
-    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
-    "% \\\\\n",
-    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
-    "\\\\\n",
-    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
-    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
-    "\\end{eqnarray*}\n",
+    "\\mu_{\\boldsymbol{x}_1}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{i1},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "714f281a",
+   "id": "0b0db7b8",
    "metadata": {
     "editable": true
    },
    "source": [
-    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
-    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
-    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
-    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
-    "variance of the estimate of the $j$-th regression coefficient:\n",
-    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
-    "construct a confidence interval for the estimates.\n",
-    "\n",
-    "In a similar way, we can obtain analytical expressions for say the\n",
-    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
-    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
-    "\n",
-    "It is rather straightforward to show that"
+    "and the mean value of the outputs as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "66015ca6",
+   "id": "e7550136",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "\\mu_y=\\frac{1}{n}\\sum_{i=0}^{n-1}y_i,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "267f01c0",
+   "id": "c7cc678d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We see clearly that \n",
-    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
-    "\n",
-    "We can also compute the variance as"
+    "we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "998de4b6",
+   "id": "9c9abcb2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "\\beta_0 = \\mu_y - \\beta_1\\mu_{\\boldsymbol{x}_1}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "06a8d452",
+   "id": "2336bf55",
    "metadata": {
     "editable": true
    },
    "source": [
-    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
-    "\n",
-    "With this, we can compute the difference"
+    "In the general case with more parameters than $\\beta_0$ and $\\beta_1$, we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6cd3ed9b",
+   "id": "e0111655",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\frac{1}{n}\\sum_{i=0}^{n-1}\\sum_{j=1}^{p-1} X_{ij}\\beta_j.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "70f9aa69",
+   "id": "709c3af5",
    "metadata": {
     "editable": true
    },
    "source": [
-    "The difference is non-negative definite since each component of the\n",
-    "matrix product is non-negative definite. \n",
-    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+    "We can rewrite the latter equation as"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "3bb18388",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Deriving OLS from a probability distribution\n",
-    "\n",
-    "Our basic assumption when we derived the OLS equations was to assume\n",
-    "that our output is determined by a given continuous function\n",
-    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
-    "distribution with zero mean value and an undetermined variance\n",
-    "$\\sigma^2$.\n",
-    "\n",
-    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
-    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
-    "the design matrix are not stochastic variables, we can assume that the\n",
-    "probability distribution of our targets is also a normal distribution\n",
-    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
-    "single output $y_i$ is given by the Gaussian distribution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84b546af",
+   "id": "a9ed060b",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\sum_{j=1}^{p-1} \\mu_{\\boldsymbol{x}_j}\\beta_j,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e90935e",
+   "id": "deb61dec",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Independent and Identically Distrubuted (iid)\n",
-    "\n",
-    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
-    "We define this distribution as"
+    "where we have defined"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b40b33f5",
+   "id": "dc60f20f",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "\\mu_{\\boldsymbol{x}_j}=\\frac{1}{n}\\sum_{i=0}^{n-1} X_{ij},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "23e0c7ac",
+   "id": "b43a095d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "the mean value for all elements of the column vector $\\boldsymbol{x}_j$.\n",
     "\n",
-    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+    "Replacing $y_i$ with $y_i - y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dcd4ed9c",
+   "id": "050949a2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "C(\\boldsymbol{\\beta}) = (\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta})^T(\\boldsymbol{\\tilde{y}} - \\tilde{X}\\boldsymbol{\\beta}).\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5c37adb5",
+   "id": "7c288c56",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
-    "in case we have a simple one-dimensional input and output case"
+    "If we minimize with respect to $\\boldsymbol{\\beta}$ we have then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27e4eea9",
+   "id": "72d25a93",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X})^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}},\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5dd95110",
+   "id": "6e4fe379",
    "metadata": {
     "editable": true
    },
    "source": [
-    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
-    "We can now rewrite the above probability as"
+    "where $\\boldsymbol{\\tilde{y}} = \\boldsymbol{y} - \\overline{\\boldsymbol{y}}$\n",
+    "and $\\tilde{X}_{ij} = X_{ij} - \\frac{1}{n}\\sum_{k=0}^{n-1}X_{kj}$.\n",
+    "\n",
+    "For Ridge regression we need to add $\\lambda \\boldsymbol{\\beta}^T\\boldsymbol{\\beta}$ to the cost function and get then"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "bc7cfda7",
+   "id": "b0f0e6e7",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "\\hat{\\boldsymbol{\\beta}} = (\\tilde{X}^T\\tilde{X} + \\lambda I)^{-1}\\tilde{X}^T\\boldsymbol{\\tilde{y}}.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2bf6dbf4",
+   "id": "6cfb015d",
    "metadata": {
     "editable": true
    },
    "source": [
-    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+    "What does this mean? And why do we insist on all this? Let us look at some examples.\n",
+    "\n",
+    "This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (*code example thanks to  Øyvind Sigmundson Schøyen*). Here our scaling of the data is done by subtracting the mean values only.\n",
+    "Note also that we do not split the data into training and test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "bfbf5d1d",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n",
+    "np.random.seed(2021)\n",
+    "\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "\n",
+    "\n",
+    "def fit_beta(X, y):\n",
+    "    return np.linalg.pinv(X.T @ X) @ X.T @ y\n",
+    "\n",
+    "\n",
+    "true_beta = [2, 0.5, 3.7]\n",
+    "\n",
+    "x = np.linspace(0, 1, 11)\n",
+    "y = np.sum(\n",
+    "    np.asarray([x ** p * b for p, b in enumerate(true_beta)]), axis=0\n",
+    ") + 0.1 * np.random.normal(size=len(x))\n",
+    "\n",
+    "degree = 3\n",
+    "X = np.zeros((len(x), degree))\n",
+    "\n",
+    "# Include the intercept in the design matrix\n",
+    "for p in range(degree):\n",
+    "    X[:, p] = x ** p\n",
+    "\n",
+    "beta = fit_beta(X, y)\n",
+    "\n",
+    "# Intercept is included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=False).fit(X, y)\n",
+    "\n",
+    "print(f\"True beta: {true_beta}\")\n",
+    "print(f\"Fitted beta: {beta}\")\n",
+    "print(f\"Sklearn fitted beta: {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with intercept column\")\n",
+    "print(MSE(y,ypredictOwn))\n",
+    "print(f\"MSE with intercept column from SKL\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.scatter(x, y, label=\"Data\")\n",
+    "plt.plot(x, X @ beta, label=\"Fit\")\n",
+    "plt.plot(x, skl.predict(X), label=\"Sklearn (fit_intercept=False)\")\n",
+    "\n",
+    "\n",
+    "# Do not include the intercept in the design matrix\n",
+    "X = np.zeros((len(x), degree - 1))\n",
+    "\n",
+    "for p in range(degree - 1):\n",
+    "    X[:, p] = x ** (p + 1)\n",
+    "\n",
+    "# Intercept is not included in the design matrix\n",
+    "skl = LinearRegression(fit_intercept=True).fit(X, y)\n",
+    "\n",
+    "# Use centered values for X and y when computing coefficients\n",
+    "y_offset = np.average(y, axis=0)\n",
+    "X_offset = np.average(X, axis=0)\n",
+    "\n",
+    "beta = fit_beta(X - X_offset, y - y_offset)\n",
+    "intercept = np.mean(y_offset - X_offset @ beta)\n",
+    "\n",
+    "print(f\"Manual intercept: {intercept}\")\n",
+    "print(f\"Fitted beta (wiothout intercept): {beta}\")\n",
+    "print(f\"Sklearn intercept: {skl.intercept_}\")\n",
+    "print(f\"Sklearn fitted beta (without intercept): {skl.coef_}\")\n",
+    "ypredictOwn = X @ beta\n",
+    "ypredictSKL = skl.predict(X)\n",
+    "print(f\"MSE with Manual intercept\")\n",
+    "print(MSE(y,ypredictOwn+intercept))\n",
+    "print(f\"MSE with Sklearn intercept\")\n",
+    "print(MSE(y,ypredictSKL))\n",
+    "\n",
+    "plt.plot(x, X @ beta + intercept, \"--\", label=\"Fit (manual intercept)\")\n",
+    "plt.plot(x, skl.predict(X), \"--\", label=\"Sklearn (fit_intercept=True)\")\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "e8ff9386",
+   "id": "7dc13bd7",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Maximum Likelihood Estimation (MLE)\n",
+    "The intercept is the value of our output/target variable\n",
+    "when all our features are zero and our function crosses the $y$-axis (for a one-dimensional case). \n",
     "\n",
-    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
-    "estimating the parameters of an assumed probability distribution,\n",
-    "given some observed data. This is achieved by maximizing a likelihood\n",
-    "function so that, under the assumed statistical model, the observed\n",
-    "data is the most probable. \n",
+    "Printing the MSE, we see first that both methods give the same MSE, as\n",
+    "they should.  However, when we move to for example Ridge regression,\n",
+    "the way we treat the intercept may give a larger or smaller MSE,\n",
+    "meaning that the MSE can be penalized by the value of the\n",
+    "intercept. Not including the intercept in the fit, means that the\n",
+    "regularization term does not include $\\beta_0$. For different values\n",
+    "of $\\lambda$, this may lead to different MSE values. \n",
     "\n",
-    "We will assume here that our events are given by the above Gaussian\n",
-    "distribution and we will determine the optimal parameters $\\beta$ by\n",
-    "maximizing the above PDF. However, computing the derivatives of a\n",
-    "product function is cumbersome and can easily lead to overflow and/or\n",
-    "underflowproblems, with potentials for loss of numerical precision.\n",
-    "\n",
-    "In practice, it is more convenient to maximize the logarithm of the\n",
-    "PDF because it is a monotonically increasing function of the argument.\n",
-    "Alternatively, and this will be our option, we will minimize the\n",
-    "negative of the logarithm since this is a monotonically decreasing\n",
-    "function.\n",
-    "\n",
-    "Note also that maximization/minimization of the logarithm of the PDF\n",
-    "is equivalent to the maximization/minimization of the function itself."
+    "To remind the reader, the regularization term, with the intercept in Ridge regression, is given by"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cd665e76",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## A new Cost Function\n",
-    "\n",
-    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "144c5c88",
+   "id": "3ce23f71",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=0}^{p-1}\\beta_j^2,\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "76c7c5c6",
+   "id": "f34b4a50",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which becomes"
+    "but when we take out the intercept, this equation becomes"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f78405b0",
+   "id": "6c535531",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_2^2 = \\lambda \\sum_{j=1}^{p-1}\\beta_j^2.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd7b69b5",
+   "id": "dc9bd9cc",
    "metadata": {
     "editable": true
    },
    "source": [
-    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+    "For Lasso regression we have"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a1430a20",
+   "id": "b5911fe2",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "\\lambda \\vert\\vert \\boldsymbol{\\beta} \\vert\\vert_1 = \\lambda \\sum_{j=1}^{p-1}\\vert\\beta_j\\vert.\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dfcd0c7b",
+   "id": "4d4103f1",
    "metadata": {
     "editable": true
    },
    "source": [
-    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "043d37a6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28fe8edc",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b600d21c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## More basic Statistics and Bayes' theorem\n",
+    "It means that, when scaling the design matrix and the outputs/targets,\n",
+    "by subtracting the mean values, we have an optimization problem which\n",
+    "is not penalized by the intercept. The MSE value can then be smaller\n",
+    "since it focuses only on the remaining quantities. If we however bring\n",
+    "back the intercept, we will get a MSE which then contains the\n",
+    "intercept.\n",
     "\n",
-    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
-    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "Armed with this wisdom, we attempt first to simply set the intercept equal to **False** in our implementation of Ridge regression for our well-known  vanilla data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eecec942",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
     "\n",
-    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
     "\n",
-    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
-    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
     "\n",
-    "**Union of events is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d513cf2c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d18008b5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "**The product rule (aka joint probability) is given by.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22f55ec4",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "29e1bff6",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(3155)\n",
     "\n",
-    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "333869a5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Marginal Probability\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf8de993",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c5349d1",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Conditional  Probability\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree))\n",
+    "#We include explicitely the intercept column\n",
+    "for degree in range(Maxpolydegree):\n",
+    "    X[:,degree] = x**degree\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "The conditional  probability, if $p(Y) > 0$, is"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbede038",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1334a9c",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem\n",
+    "p = Maxpolydegree\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train.T @ X_train+lmb*I) @ X_train.T @ y_train\n",
+    "    # Note: we include the intercept column and no scaling\n",
+    "    RegRidge = linear_model.Ridge(lmb,fit_intercept=False)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    # and then make the prediction\n",
+    "    ytildeOwnRidge = X_train @ OwnRidgeBeta\n",
+    "    ypredictOwnRidge = X_test @ OwnRidgeBeta\n",
+    "    ytildeRidge = RegRidge.predict(X_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta)\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
     "\n",
-    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac8dcf04",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1992e354",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which we can rewrite as"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24e5179f",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42979e5a",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "86a8cd07",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Interpretations of Bayes' Theorem\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'r', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g', label = 'MSE Ridge Test')\n",
     "\n",
-    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
-    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
-    "the parameter space represented by $X$. This function is not\n",
-    "necesseraly normalized and is normally called the likelihood function.\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d67706b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The results here agree when we force **Scikit-Learn**'s Ridge function to include the first column in our design matrix.\n",
+    "We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix.\n",
+    "What happens if we do not include the intercept in our fit?\n",
+    "Let us see how we can change this code by zero centering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "598d20bd",
+   "metadata": {
+    "collapsed": false,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "\n",
-    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "def MSE(y_data,y_model):\n",
+    "    n = np.size(y_model)\n",
+    "    return np.sum((y_data-y_model)**2)/n\n",
+    "# A seed just to ensure that the random numbers are the same for every run.\n",
+    "# Useful for eventual debugging.\n",
+    "np.random.seed(315)\n",
     "\n",
-    "Let us try to illustrate Bayes' theorem through an example."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9a4f9a9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Example of Usage of Bayes' theorem\n",
+    "n = 100\n",
+    "x = np.random.rand(n)\n",
+    "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)\n",
     "\n",
-    "Let us suppose that you are undergoing a series of mammography scans in\n",
-    "order to rule out possible breast cancer cases.  We define the\n",
-    "sensitivity for a positive event by the variable $X$. It takes binary\n",
-    "values with $X=1$ representing a positive event and $X=0$ being a\n",
-    "negative event. We reserve $Y$ as a classification parameter for\n",
-    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "Maxpolydegree = 20\n",
+    "X = np.zeros((n,Maxpolydegree-1))\n",
     "\n",
-    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "for degree in range(1,Maxpolydegree): #No intercept column\n",
+    "    X[:,degree-1] = x**(degree)\n",
     "\n",
-    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "888c64ec",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=1) =0.8.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5086b046",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
-    "It is however not correct, as the following Bayesian analysis shows."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "609ea2b0",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Doing it correctly\n",
+    "# We split the data in test and training data\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "\n",
-    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
-    "Let us assume that the prior probability in the population as a whole is"
+    "#For our own implementation, we will need to deal with the intercept by centering the design matrix and the target variable\n",
+    "X_train_mean = np.mean(X_train,axis=0)\n",
+    "#Center by removing mean from each feature\n",
+    "X_train_scaled = X_train - X_train_mean \n",
+    "X_test_scaled = X_test - X_train_mean\n",
+    "#The model intercept (called y_scaler) is given by the mean of the target variable (IF X is centered)\n",
+    "#Remove the intercept from the training data.\n",
+    "y_scaler = np.mean(y_train)           \n",
+    "y_train_scaled = y_train - y_scaler   \n",
+    "\n",
+    "p = Maxpolydegree-1\n",
+    "I = np.eye(p,p)\n",
+    "# Decide which values of lambda to use\n",
+    "nlambdas = 6\n",
+    "MSEOwnRidgePredict = np.zeros(nlambdas)\n",
+    "MSERidgePredict = np.zeros(nlambdas)\n",
+    "\n",
+    "lambdas = np.logspace(-4, 2, nlambdas)\n",
+    "for i in range(nlambdas):\n",
+    "    lmb = lambdas[i]\n",
+    "    OwnRidgeBeta = np.linalg.pinv(X_train_scaled.T @ X_train_scaled+lmb*I) @ X_train_scaled.T @ (y_train_scaled)\n",
+    "    intercept_ = y_scaler - X_train_mean@OwnRidgeBeta #The intercept can be shifted so the model can predict on uncentered data\n",
+    "    #Add intercept to prediction\n",
+    "    ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler \n",
+    "    RegRidge = linear_model.Ridge(lmb)\n",
+    "    RegRidge.fit(X_train,y_train)\n",
+    "    ypredictRidge = RegRidge.predict(X_test)\n",
+    "    MSEOwnRidgePredict[i] = MSE(y_test,ypredictOwnRidge)\n",
+    "    MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
+    "    print(\"Beta values for own Ridge implementation\")\n",
+    "    print(OwnRidgeBeta) #Intercept is given by mean of target variable\n",
+    "    print(\"Beta values for Scikit-Learn Ridge implementation\")\n",
+    "    print(RegRidge.coef_)\n",
+    "    print('Intercept from own implementation:')\n",
+    "    print(intercept_)\n",
+    "    print('Intercept from Scikit-Learn Ridge implementation')\n",
+    "    print(RegRidge.intercept_)\n",
+    "    print(\"MSE values for own Ridge implementation\")\n",
+    "    print(MSEOwnRidgePredict[i])\n",
+    "    print(\"MSE values for Scikit-Learn Ridge implementation\")\n",
+    "    print(MSERidgePredict[i])\n",
+    "\n",
+    "\n",
+    "# Now plot the results\n",
+    "plt.figure()\n",
+    "plt.plot(np.log10(lambdas), MSEOwnRidgePredict, 'b--', label = 'MSE own Ridge Test')\n",
+    "plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
+    "plt.xlabel('log10(lambda)')\n",
+    "plt.ylabel('MSE')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "687bfdf3",
+   "id": "cec80003",
    "metadata": {
     "editable": true
    },
    "source": [
-    "$$\n",
-    "p(Y=1) =0.004.\n",
-    "$$"
+    "We see here, when compared to the code which includes explicitely the\n",
+    "intercept column, that our MSE value is actually smaller. This is\n",
+    "because the regularization term does not include the intercept value\n",
+    "$\\beta_0$ in the fitting.  This applies to Lasso regularization as\n",
+    "well.  It means that our optimization is now done only with the\n",
+    "centered matrix and/or vector that enter the fitting procedure."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c2af1d2d",
+   "id": "16cdcffb",
    "metadata": {
     "editable": true
    },
    "source": [
-    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ec8d93",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(X=1\\vert Y=0) =0.1.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48a6fb5b",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba37d4fd",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "$$\n",
-    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "824f1789",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07f89fd5",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "## Test Function for what happens with OLS, Ridge and Lasso\n",
     "\n",
     "Hitherto we have discussed Ridge and Lasso regression in terms of a\n",
     "linear analysis. This may to many of you feel rather technical and\n",
     "perhaps not that intuitive. The question is whether we can develop a\n",
     "more intuitive way of understanding what Ridge and Lasso express.\n",
     "\n",
-    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8fcd73d9",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Test Function for what happens with OLS, Ridge and Lasso\n",
+    "Before we proceed let us perform a Ridge, Lasso  and OLS analysis of a polynomial fit. \n",
     "\n",
     "We will play around with a study of the values for the optimal\n",
     "parameters $\\boldsymbol{\\beta}$ using OLS, Ridge and Lasso regression.  For\n",
@@ -3015,8 +2909,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "4402e113",
+   "execution_count": 11,
+   "id": "e04dce1d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3043,10 +2937,9 @@
     "X = np.zeros((len(x),Maxpolydegree))\n",
     "X[:,0] = 1.0\n",
     "\n",
-    "for polydegree in range(1, Maxpolydegree):\n",
-    "    for degree in range(polydegree):\n",
-    "        X[:,degree] = x**(degree)\n",
     "\n",
+    "for polydegree in range(1,Maxpolydegree):\n",
+    "    X[:,polydegree] = x**(polydegree)\n",
     "\n",
     "# We split the data in test and training data\n",
     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
@@ -3090,7 +2983,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b700e45e",
+   "id": "2ae202c3",
    "metadata": {
     "editable": true
    },
@@ -3100,43 +2993,475 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69659d9d",
+   "id": "4186f9f0",
    "metadata": {
     "editable": true
    },
    "source": [
-    "## Invoking Bayes' theorem\n",
+    "## Linking the regression analysis with a statistical interpretation\n",
     "\n",
-    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "We will now couple the discussions of ordinary least squares, Ridge\n",
+    "and Lasso regression with a statistical interpretation, that is we\n",
+    "move from a linear algebra analysis to a statistical analysis. In\n",
+    "particular, we will focus on what the regularization terms can result\n",
+    "in.  We will amongst other things show that the regularization\n",
+    "parameter can reduce considerably the variance of the parameters\n",
+    "$\\beta$.\n",
     "\n",
-    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+    "The\n",
+    "advantage of doing linear regression is that we actually end up with\n",
+    "analytical expressions for several statistical quantities.  \n",
+    "Standard least squares and Ridge regression  allow us to\n",
+    "derive quantities like the variance and other expectation values in a\n",
+    "rather straightforward way.\n",
+    "\n",
+    "It is assumed that $\\varepsilon_i\n",
+    "\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
+    "independent, i.e.:"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "80b423eb",
+   "id": "7293d2ab",
    "metadata": {
     "editable": true
    },
    "source": [
     "$$\n",
-    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "\\begin{align*} \n",
+    "\\mbox{Cov}(\\varepsilon_{i_1},\n",
+    "\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
+    "& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2.  \\end{array} \\right.\n",
+    "\\end{align*}\n",
     "$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "de547949",
+   "id": "efee3703",
    "metadata": {
     "editable": true
    },
    "source": [
-    "is given by"
+    "The randomness of $\\varepsilon_i$ implies that\n",
+    "$\\mathbf{y}_i$ is also a random variable. In particular,\n",
+    "$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
+    "\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
+    "non-random scalar. To specify the parameters of the distribution of\n",
+    "$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
+    "\n",
+    "Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
+    "notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
+    "row number $i$ and perform a sum over all values $p$."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "99d9476a",
+   "id": "85d2303d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Assumptions made\n",
+    "\n",
+    "The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
+    "that there exists a function $f(\\boldsymbol{x})$ and  a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
+    "which describe our data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1a189c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b63e61f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We approximate this function with our model from the solution of the linear regression equations, that is our\n",
+    "function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14acfe14",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681408c6",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance\n",
+    "\n",
+    "We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d956e33c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \n",
+    "\\mathbb{E}(y_i) & =\n",
+    "\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
+    "\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "992bee5b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "while\n",
+    "its variance is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd75d1af",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
+    "- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
+    "[\\mathbb{E}(y_i)]^2  \\\\  & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
+    "\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
+    "= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
+    "\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
+    "\\ast} \\, \\beta)^2 \\\\  & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
+    "\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
+    "\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
+    "\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
+    "\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2.  \n",
+    "\\end{align*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f702411d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
+    "mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b86e729d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
+    "\n",
+    "With the OLS expressions for the optimal parameters $\\boldsymbol{\\hat{\\beta}}$ we can evaluate the expectation value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5af888a2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E}(\\boldsymbol{\\hat{\\beta}}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d21a65e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This means that the estimator of the regression parameters is unbiased.\n",
+    "\n",
+    "We can also calculate the variance\n",
+    "\n",
+    "The variance of the optimal value $\\boldsymbol{\\hat{\\beta}}$ is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d8043ab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\begin{eqnarray*}\n",
+    "\\mbox{Var}(\\boldsymbol{\\hat{\\beta}}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
+    "\\\\\n",
+    "& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
+    "\\\\\n",
+    "% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}  \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\\\\n",
+    "& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\,  \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "% \\\\\n",
+    "% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\,  \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
+    "% \\\\\n",
+    "% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T  \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
+    "\\\\\n",
+    "& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}  + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
+    "\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
+    "\\end{eqnarray*}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a126ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we have used  that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
+    "\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
+    "\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
+    "\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
+    "variance of the estimate of the $j$-th regression coefficient:\n",
+    "$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 [(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} $. This may be used to\n",
+    "construct a confidence interval for the estimates.\n",
+    "\n",
+    "In a similar way, we can obtain analytical expressions for say the\n",
+    "expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
+    "when we employ Ridge regression, allowing us again to define a confidence interval. \n",
+    "\n",
+    "It is rather straightforward to show that"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7287992a",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2803f793",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We see clearly that \n",
+    "$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
+    "\n",
+    "We can also compute the variance as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7443c20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}  \\mathbf{X}^{T} \\mathbf{X} \\{ [  \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0d32eb0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
+    "\n",
+    "With this, we can compute the difference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbd0143e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [  \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6de88a4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "The difference is non-negative definite since each component of the\n",
+    "matrix product is non-negative definite. \n",
+    "This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55630c9f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Deriving OLS from a probability distribution\n",
+    "\n",
+    "Our basic assumption when we derived the OLS equations was to assume\n",
+    "that our output is determined by a given continuous function\n",
+    "$f(\\boldsymbol{x})$ and a random noise $\\boldsymbol{\\epsilon}$ given by the normal\n",
+    "distribution with zero mean value and an undetermined variance\n",
+    "$\\sigma^2$.\n",
+    "\n",
+    "We found above that the outputs $\\boldsymbol{y}$ have a mean value given by\n",
+    "$\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$ and variance $\\sigma^2$. Since the entries to\n",
+    "the design matrix are not stochastic variables, we can assume that the\n",
+    "probability distribution of our targets is also a normal distribution\n",
+    "but now with mean value $\\boldsymbol{X}\\hat{\\boldsymbol{\\beta}}$. This means that a\n",
+    "single output $y_i$ is given by the Gaussian distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "745e6352",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "y_i\\sim \\mathcal{N}(\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta}, \\sigma^2)=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec8c116",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Independent and Identically Distrubuted (iid)\n",
+    "\n",
+    "We assume now that the various $y_i$ values are stochastically distributed according to the above Gaussian distribution. \n",
+    "We define this distribution as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f92be14e",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(y_i, \\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edbdd556",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which reads as finding the likelihood of an event $y_i$ with the input variables $\\boldsymbol{X}$ given the parameters (to be determined) $\\boldsymbol{\\beta}$.\n",
+    "\n",
+    "Since these events are assumed to be independent and identicall distributed we can build the probability distribution function (PDF) for all possible event $\\boldsymbol{y}$ as the product of the single events, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a6903ca",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{y},\\boldsymbol{X}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}=\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta}).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57680896",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We will write this in a more compact form reserving $\\boldsymbol{D}$ for the domain of events, including the ouputs (targets) and the inputs. That is\n",
+    "in case we have a simple one-dimensional input and output case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9309640f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})].\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67fb3df9",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "In the more general case the various inputs should be replaced by the possible features represented by the input data set $\\boldsymbol{X}$. \n",
+    "We can now rewrite the above probability as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "894f0fb7",
    "metadata": {
     "editable": true
    },
@@ -3148,7 +3473,508 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aa5613ea",
+   "id": "13f3f705",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "It is a conditional probability (see below) and reads as the likelihood of a domain of events $\\boldsymbol{D}$ given a set of parameters $\\boldsymbol{\\beta}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bf3edfa",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Maximum Likelihood Estimation (MLE)\n",
+    "\n",
+    "In statistics, maximum likelihood estimation (MLE) is a method of\n",
+    "estimating the parameters of an assumed probability distribution,\n",
+    "given some observed data. This is achieved by maximizing a likelihood\n",
+    "function so that, under the assumed statistical model, the observed\n",
+    "data is the most probable. \n",
+    "\n",
+    "We will assume here that our events are given by the above Gaussian\n",
+    "distribution and we will determine the optimal parameters $\\beta$ by\n",
+    "maximizing the above PDF. However, computing the derivatives of a\n",
+    "product function is cumbersome and can easily lead to overflow and/or\n",
+    "underflowproblems, with potentials for loss of numerical precision.\n",
+    "\n",
+    "In practice, it is more convenient to maximize the logarithm of the\n",
+    "PDF because it is a monotonically increasing function of the argument.\n",
+    "Alternatively, and this will be our option, we will minimize the\n",
+    "negative of the logarithm since this is a monotonically decreasing\n",
+    "function.\n",
+    "\n",
+    "Note also that maximization/minimization of the logarithm of the PDF\n",
+    "is equivalent to the maximization/minimization of the function itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be42ac0c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## A new Cost Function\n",
+    "\n",
+    "We could now define a new cost function to minimize, namely the negative logarithm of the above PDF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6fbab08",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=-\\log{\\prod_{i=0}^{n-1}p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})}=-\\sum_{i=0}^{n-1}\\log{p(y_i,\\boldsymbol{X}\\vert\\boldsymbol{\\beta})},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33ef1983",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which becomes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d6504da",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "C(\\boldsymbol{\\beta}=\\frac{n}{2}\\log{2\\pi\\sigma^2}+\\frac{\\vert\\vert (\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\vert\\vert_2^2}{2\\sigma^2}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03d8775c",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Taking the derivative of the *new* cost function with respect to the parameters $\\beta$ we recognize our familiar OLS equation, namely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "176d1dab",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right) =0,\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e20e33bf",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which leads to the well-known OLS equation for the optimal paramters $\\beta$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f73eae",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\hat{\\boldsymbol{\\beta}}^{\\mathrm{OLS}}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}!\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5d2cf20",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Before we make a similar analysis for Ridge and Lasso regression, we need a short reminder on statistics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c32b8f92",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## More basic Statistics and Bayes' theorem\n",
+    "\n",
+    "A central theorem in statistics is Bayes' theorem. This theorem plays a similar role as the good old Pythagoras' theorem in geometry.\n",
+    "Bayes' theorem is extremely simple to derive. But to do so we need some basic axioms from statistics.\n",
+    "\n",
+    "Assume we have two domains of events $X=[x_0,x_1,\\dots,x_{n-1}]$ and $Y=[y_0,y_1,\\dots,y_{n-1}]$.\n",
+    "\n",
+    "We define also the likelihood for $X$ and $Y$ as $p(X)$ and $p(Y)$ respectively.\n",
+    "The likelihood of a specific event $x_i$ (or $y_i$) is then written as $p(X=x_i)$ or just $p(x_i)=p_i$. \n",
+    "\n",
+    "**Union of events is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3779fd2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X)+p(Y)-p(X \\cap Y).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e13b18",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "**The product rule (aka joint probability) is given by.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd8b41f0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X \\cup Y)= p(X,Y)= p(X\\vert Y)p(Y)=p(Y\\vert X)p(X),\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b22e26",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "where we read $p(X\\vert Y)$ as the likelihood of obtaining $X$ given $Y$.\n",
+    "\n",
+    "If we have independent events then $p(X,Y)=p(X)p(Y)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f6645d2",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Marginal Probability\n",
+    "\n",
+    "The marginal probability is defined in terms of only one of the set of variables $X,Y$. For a discrete probability we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7349fe65",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X)=\\sum_{i=0}^{n-1}p(X,Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert Y=y_i)p(Y=y_i)=\\sum_{i=0}^{n-1}p(X\\vert y_i)p(y_i).\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6205b4f",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Conditional  Probability\n",
+    "\n",
+    "The conditional  probability, if $p(Y) > 0$, is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43cb130d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)}=\\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d8f1813",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem\n",
+    "\n",
+    "If we combine the conditional probability with the marginal probability and the standard product rule, we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2746770",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{p(Y)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3659d1d",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which we can rewrite as"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8dfd9ec7",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X\\vert Y)= \\frac{p(X,Y)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)}=\\frac{p(Y\\vert X)p(X)}{\\sum_{i=0}^{n-1}p(Y\\vert X=x_i)p(x_i)},\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f247ad1",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "which is Bayes' theorem. It allows us to evaluate the uncertainty in in $X$ after we have observed $Y$. We can easily interchange $X$ with $Y$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2615306",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Interpretations of Bayes' Theorem\n",
+    "\n",
+    "The quantity $p(Y\\vert X)$ on the right-hand side of the theorem is\n",
+    "evaluated for the observed data $Y$ and can be viewed as a function of\n",
+    "the parameter space represented by $X$. This function is not\n",
+    "necesseraly normalized and is normally called the likelihood function.\n",
+    "\n",
+    "The function $p(X)$ on the right hand side is called the prior while the function on the left hand side is the called the posterior probability. The denominator on the right hand side serves as a normalization factor for the posterior distribution.\n",
+    "\n",
+    "Let us try to illustrate Bayes' theorem through an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91eae9e8",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Example of Usage of Bayes' theorem\n",
+    "\n",
+    "Let us suppose that you are undergoing a series of mammography scans in\n",
+    "order to rule out possible breast cancer cases.  We define the\n",
+    "sensitivity for a positive event by the variable $X$. It takes binary\n",
+    "values with $X=1$ representing a positive event and $X=0$ being a\n",
+    "negative event. We reserve $Y$ as a classification parameter for\n",
+    "either a negative or a positive breast cancer confirmation. (Short note on wordings: positive here means having breast cancer, although none of us would consider this being a  positive thing).\n",
+    "\n",
+    "We let $Y=1$ represent the the case of having breast cancer and $Y=0$ as not.\n",
+    "\n",
+    "Let us assume that if you have breast cancer, the test will be positive with a probability of $0.8$, that is we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76fe1c46",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=1) =0.8.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "397d9768",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "This obviously sounds  scary since many would conclude that if the test is positive, there is a likelihood of $80\\%$ for having cancer.\n",
+    "It is however not correct, as the following Bayesian analysis shows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2c701f5",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Doing it correctly\n",
+    "\n",
+    "If we look at various national surveys on breast cancer, the general likelihood of developing breast cancer is a very small number.\n",
+    "Let us assume that the prior probability in the population as a whole is"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fdb2639",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1) =0.004.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ae9f95",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "We need also to account for the fact that the test may produce a false positive result (false alarm). Let us here assume that we have"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a681f1dc",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(X=1\\vert Y=0) =0.1.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d2c6030",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "Using Bayes' theorem we can then find the posterior probability that the person has breast cancer in case of a positive test, that is we can compute"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67a1c582",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(Y=1\\vert X=1)=\\frac{p(X=1\\vert Y=1)p(Y=1)}{p(X=1\\vert Y=1)p(Y=1)+p(X=1\\vert Y=0)p(Y=0)}=\\frac{0.8\\times 0.004}{0.8\\times 0.004+0.1\\times 0.996}=0.031.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "969f368b",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "That is, in case of a positive test, there is only a $3\\%$ chance of having breast cancer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "356a70c0",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "## Bayes' Theorem and Ridge and Lasso Regression\n",
+    "\n",
+    "Using Bayes' theorem we can gain a better intuition about Ridge and Lasso regression. \n",
+    "\n",
+    "For ordinary least squares we postulated that the maximum likelihood for the doamin of events $\\boldsymbol{D}$ (one-dimensional case)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa653f05",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "\\boldsymbol{D}=[(x_0,y_0), (x_1,y_1),\\dots, (x_{n-1},y_{n-1})],\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5dd1081",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "is given by"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "731b21d4",
+   "metadata": {
+    "editable": true
+   },
+   "source": [
+    "$$\n",
+    "p(\\boldsymbol{D}\\vert\\boldsymbol{\\beta})=\\prod_{i=0}^{n-1}\\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\exp{\\left[-\\frac{(y_i-\\boldsymbol{X}_{i,*}\\boldsymbol{\\beta})^2}{2\\sigma^2}\\right]}.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "362ae4e2",
    "metadata": {
     "editable": true
    },
@@ -3158,7 +3984,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dea4e11",
+   "id": "8ba56ac5",
    "metadata": {
     "editable": true
    },
@@ -3170,7 +3996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99ab1d57",
+   "id": "658e6364",
    "metadata": {
     "editable": true
    },
@@ -3180,7 +4006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "170ee68b",
+   "id": "28d6553d",
    "metadata": {
     "editable": true
    },
@@ -3192,7 +4018,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3388d6a4",
+   "id": "8f4ec5f0",
    "metadata": {
     "editable": true
    },
@@ -3202,7 +4028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5bdaa16",
+   "id": "e72b34ca",
    "metadata": {
     "editable": true
    },
@@ -3218,7 +4044,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cbf8e7e6",
+   "id": "0e656652",
    "metadata": {
     "editable": true
    },
@@ -3230,7 +4056,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52c9e48f",
+   "id": "57606d08",
    "metadata": {
     "editable": true
    },
@@ -3240,7 +4066,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ae37a89",
+   "id": "cf40e3a9",
    "metadata": {
     "editable": true
    },
@@ -3252,7 +4078,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd8fee27",
+   "id": "65038f20",
    "metadata": {
     "editable": true
    },
@@ -3265,7 +4091,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "de603b44",
+   "id": "deb9890c",
    "metadata": {
     "editable": true
    },
@@ -3277,7 +4103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f08e4a0",
+   "id": "1a844813",
    "metadata": {
     "editable": true
    },
@@ -3287,7 +4113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b65ee2f0",
+   "id": "a32e4148",
    "metadata": {
     "editable": true
    },
@@ -3299,7 +4125,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6526b426",
+   "id": "4050a8ed",
    "metadata": {
     "editable": true
    },
@@ -3309,7 +4135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29190fe5",
+   "id": "ec31e4e2",
    "metadata": {
     "editable": true
    },
@@ -3321,7 +4147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71b1585c",
+   "id": "f872c397",
    "metadata": {
     "editable": true
    },
@@ -3333,7 +4159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e18baaa9",
+   "id": "e8eeb2e5",
    "metadata": {
     "editable": true
    },
@@ -3343,7 +4169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c58b8db",
+   "id": "54d2f39d",
    "metadata": {
     "editable": true
    },
@@ -3355,7 +4181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "238ee8c5",
+   "id": "54cbc3a8",
    "metadata": {
     "editable": true
    },
@@ -3367,7 +4193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46389b82",
+   "id": "7ee728b6",
    "metadata": {
     "editable": true
    },
@@ -3379,7 +4205,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ecbb2d2",
+   "id": "00e9a0eb",
    "metadata": {
     "editable": true
    },
@@ -3389,7 +4215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27368dfb",
+   "id": "50f72b22",
    "metadata": {
     "editable": true
    },
@@ -3401,7 +4227,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27082f48",
+   "id": "d53fea2e",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week37/exercisesweek37.do.txt b/doc/src/week37/exercisesweek37.do.txt
index f6c803e74..556182276 100644
--- a/doc/src/week37/exercisesweek37.do.txt
+++ b/doc/src/week37/exercisesweek37.do.txt
@@ -6,14 +6,24 @@ DATE: Deadline is Sunday September 17 at midnight
 =====  Overarching aims of the exercises this week =====
 
 
-This exercise deals with various mean values and variances in  linear regression method (here it may be useful to look up chapter 3, equation (3.8) of "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"). The exercise is also a part of project 1 and can be reused in the theory part of the project.
+This exercise deals with various mean values and variances in linear
+regression method (here it may be useful to look up chapter 3,
+equation (3.8) of "Trevor Hastie, Robert Tibshirani, Jerome
+H. Friedman, The Elements of Statistical Learning,
+Springer":"https://www.springer.com/gp/book/9780387848570"). The
+exercise is also a part of project 1 and can be reused in the theory
+part of the project.
 
-For more discussions on Ridge regression and calculation of expectation values, "Wessel van Wieringen's":"https://arxiv.org/abs/1509.09169" article is highly recommended.
+For more discussions on Ridge regression and calculation of
+expectation values, "Wessel van
+Wieringen's":"https://arxiv.org/abs/1509.09169" article is highly
+recommended.
 
 
-The assumption we have made is 
-that there exists a continuous function $f(\bm{x})$ and  a normal distributed error $\bm{\varepsilon}\sim N(0, \sigma^2)$
-which describes our data
+The assumption we have made is that there exists a continuous function
+$f(\bm{x})$ and a normal distributed error $\bm{\varepsilon}\sim N(0,
+\sigma^2)$ which describes our data
+
 !bt
 \[
 \bm{y} = f(\bm{x})+\bm{\varepsilon}
@@ -70,7 +80,7 @@ A given parameter $\beta_j$ is given by the diagonal matrix element of the above
 Show that
 !bt
 \[
-\mathbb{E} \big[ \bm{\beta}^{\mathrm{Ridge}} \big]=(\mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I}_{pp})^{-1} (\mathbf{X}^{\top} \mathbf{X})\
+\mathbb{E} \big[ \hat{\bm{\beta}}^{\mathrm{Ridge}} \big]=(\mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I}_{pp})^{-1} (\mathbf{X}^{\top} \mathbf{X})\
 \bm{\beta}^{\mathrm{OLS}}.
 \]
 !et
@@ -82,7 +92,7 @@ Show also that the variance is
 
 !bt
 \[
-\mbox{Var}[\bm{\beta}^{\mathrm{Ridge}}]=\sigma^2[  \mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I} ]^{-1}  \mathbf{X}^{T}\mathbf{X} \{ [  \mathbf{X}^{\top} \mathbf{X} + \lambda \mathbf{I} ]^{-1}\}^{T},
+\mbox{Var}[\hat{\bm{\beta}}^{\mathrm{Ridge}}]=\sigma^2[  \mathbf{X}^{T} \mathbf{X} + \lambda \mathbf{I} ]^{-1}  \mathbf{X}^{T}\mathbf{X} \{ [  \mathbf{X}^{\top} \mathbf{X} + \lambda \mathbf{I} ]^{-1}\}^{T},
 \]
 !et
 and it is easy to see that if the parameter $\lambda$ goes to infinity then the variance of Ridge parameters $\bm{\beta}$ goes to zero.