updated readme

README.md

@@ -409,6 +409,7 @@ Recommended prereading: Chapters 1-2 (linear algebra) and chapter 3 (statistics)
 - Lecture Thursday: Support Vector Machines 
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureNovember25.mp4?vrtx=view-as-webpage
 - Lecture Friday:  Support Vector Machines and Summary of Course
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember26.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Geron's chapter 5.

doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d0a34092",
+   "id": "c475c107",
    "metadata": {},
    "source": [
     "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4d8f404",
+   "id": "424b8b49",
    "metadata": {},
    "source": [
     "# Clustering Analysis\n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae95f76d",
+   "id": "0530d5a8",
    "metadata": {},
    "source": [
     "## Basic Idea of the K-means Clustering Algorithm\n",
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a919a12d",
+   "id": "032b0982",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1afc1372",
+   "id": "7741996c",
    "metadata": {},
    "source": [
     "which we wish to group into $K < n$ clusters. For our dissimilarity measure we\n",
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4332ba1",
+   "id": "9fa36cba",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c6eca50",
+   "id": "67c0c2fb",
    "metadata": {},
    "source": [
     "Next we define the so called *within-cluster point scatter* which gives us a\n",
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d762d24f",
+   "id": "9e06af7f",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "058e6d60",
+   "id": "617b1871",
    "metadata": {},
    "source": [
     "where $\\boldsymbol{\\overline{x_k}}$ is the mean vector associated with the $k$-th\n",
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "191959b0",
+   "id": "56283dc8",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c170bd21",
+   "id": "6995cce8",
    "metadata": {},
    "source": [
     "Which is a quantity that is conserved throughout the $k$-means algorithm. It can\n",
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d495c32e",
+   "id": "2767eaea",
    "metadata": {},
    "source": [
     "<!-- Equation labels as ordinary links -->\n",
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5835ab97",
+   "id": "fe5dd65d",
    "metadata": {},
    "source": [
     "Now we have all the pieces necessary to formally revisit the k-means algorithm.\n",
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5804711e",
+   "id": "3d051e1f",
    "metadata": {},
    "source": [
     "## The K-means Clustering Algorithm\n",
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bda4544a",
+   "id": "a8d5180d",
    "metadata": {},
    "source": [
     "## Writing Our Own Code\n",
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0229351b",
+   "id": "396b648e",
    "metadata": {},
    "source": [
     "### Basic Python\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4d4cb587",
+   "id": "d1266795",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -294,7 +294,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "adbf5dfb",
+   "id": "04e0ae48",
    "metadata": {},
    "source": [
     "Next we define functions, for ease of use later, to generate Gaussians and to\n",
@@ -304,7 +304,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "432afa10",
+   "id": "c54add58",
    "metadata": {},
    "outputs": [
     {
@@ -377,7 +377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5da6553d",
+   "id": "359f17b2",
    "metadata": {},
    "source": [
     "Now that we are our, albeit very simple, dataset we are ready to start\n",
@@ -387,7 +387,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "50fdce3a",
+   "id": "07b9b0f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a192c1a",
+   "id": "a4f06c0d",
    "metadata": {},
    "source": [
     "Let's plot and see"
@@ -437,7 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "e3bd3ed5",
+   "id": "75654aaf",
    "metadata": {},
    "outputs": [
     {
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca5cdbba",
+   "id": "b600aeb7",
    "metadata": {},
    "source": [
     "So what do we have so far? We have 'picked' $k$ centroids at random from our\n",
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "683d9e56",
+   "id": "44faac12",
    "metadata": {},
    "outputs": [
     {
@@ -500,7 +500,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.23237395286560059 seconds\n"
+      "Runtime: 0.23453593254089355 seconds\n"
      ]
     }
    ],
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77d7b6c9",
+   "id": "e9ad9ec8",
    "metadata": {},
    "source": [
     "And thats it! We now have an extremely barebones, un-optimized k-means\n",
@@ -567,7 +567,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "de21744c",
+   "id": "8d3e29d6",
    "metadata": {},
    "outputs": [
     {
@@ -604,7 +604,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d3be7e4",
+   "id": "adf41666",
    "metadata": {},
    "source": [
     "Now there are a few glaring improvements to be done here. First of all is\n",
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d4b64fd",
+   "id": "0bc703d2",
    "metadata": {},
    "source": [
     "## Towards a More Numpythonic Code"
@@ -626,7 +626,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4249aa95",
+   "id": "667d9000",
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +634,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.19273090362548828 seconds\n"
+      "Runtime: 0.19642972946166992 seconds\n"
      ]
     }
    ],
@@ -748,7 +748,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdbad890",
+   "id": "ab038b5c",
    "metadata": {},
    "source": [
     "**Note**: the start of the timing is after the random initialization, and first\n",
@@ -769,7 +769,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "0ffe5738",
+   "id": "b610c6ef",
    "metadata": {},
    "outputs": [
     {
@@ -777,7 +777,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.386091947555542 seconds\n",
+      "Runtime: 0.39145517349243164 seconds\n",
       " "
      ]
     }
@@ -789,7 +789,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "649e0d75",
+   "id": "c3581c3a",
    "metadata": {},
    "source": [
     "Here we can see the reason for profiling. We now know for certain a lot can be\n",
@@ -801,7 +801,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f5b94362",
+   "id": "56cc3361",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -892,7 +892,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65816e1e",
+   "id": "0214a338",
    "metadata": {},
    "source": [
     "When working towards becoming a data scientist using Python this last step is\n",
@@ -905,7 +905,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "dab77d43",
+   "id": "1d86e363",
    "metadata": {},
    "outputs": [
     {
@@ -913,7 +913,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.002377033233642578 seconds\n"
+      "Runtime: 0.0024149417877197266 seconds\n"
      ]
     }
    ],
@@ -924,7 +924,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6ac85377",
+   "id": "5846cfe7",
    "metadata": {},
    "outputs": [],
    "source": []

doc/LectureNotes/schedule.md

@@ -175,6 +175,7 @@ For the reading assignments we use the following abbreviations:
 - Lecture Thursday: Support Vector Machines
   - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureNovember25.mp4?vrtx=view-as-webpage
 - Lecture Friday:  Support Vector Machines and Summary of course
+  - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureNovember26.mp4?vrtx=view-as-webpage
 - Reading recommendations:
   - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - Geron's chapter 5.