diff --git a/doc/pub/week35/html/._week35-bs001.html b/doc/pub/week35/html/._week35-bs001.html
index 9ae1cf0b7..79e75b530 100644
--- a/doc/pub/week35/html/._week35-bs001.html
+++ b/doc/pub/week35/html/._week35-bs001.html
@@ -389,7 +389,7 @@ MathJax.Hub.Config({
Plans for week 35
-- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises
+- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.
- Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition
- Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
diff --git a/doc/pub/week35/html/._week35-bs069.html b/doc/pub/week35/html/._week35-bs069.html
index 951591146..a329534df 100644
--- a/doc/pub/week35/html/._week35-bs069.html
+++ b/doc/pub/week35/html/._week35-bs069.html
@@ -500,7 +500,7 @@ y = 2.0+5*
Write your own code (following the examples under the regression notes) for computing the parametrization of the data set fitting a second-order polynomial.
- Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code.
+ Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to FALSE, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data.
Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as
$$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
diff --git a/doc/pub/week35/html/week35-reveal.html b/doc/pub/week35/html/week35-reveal.html
index 8cd43c5d6..13ef5ff7d 100644
--- a/doc/pub/week35/html/week35-reveal.html
+++ b/doc/pub/week35/html/week35-reveal.html
@@ -198,7 +198,7 @@ MathJax.Hub.Config({
Plans for week 35
-- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises
+- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.
- Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition
- Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
@@ -3610,8 +3610,7 @@ y = 2.0+5
Write your own code (following the examples under the regression notes) for computing the parametrization of the data set fitting a second-order polynomial.
- Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code.
-
+ Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to FALSE, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data.
Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as
diff --git a/doc/pub/week35/html/week35-solarized.html b/doc/pub/week35/html/week35-solarized.html
index 8373962c0..3e3deed64 100644
--- a/doc/pub/week35/html/week35-solarized.html
+++ b/doc/pub/week35/html/week35-solarized.html
@@ -338,7 +338,7 @@ MathJax.Hub.Config({
Plans for week 35
-- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises
+- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.
- Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition
- Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
@@ -3443,7 +3443,7 @@ y = 2.0+5
Write your own code (following the examples under the regression notes) for computing the parametrization of the data set fitting a second-order polynomial.
- Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code.
+ Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to FALSE, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data.
Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as
$$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
diff --git a/doc/pub/week35/html/week35.html b/doc/pub/week35/html/week35.html
index dd23adbae..6b0f8dd15 100644
--- a/doc/pub/week35/html/week35.html
+++ b/doc/pub/week35/html/week35.html
@@ -415,7 +415,7 @@ MathJax.Hub.Config({
Plans for week 35
-- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises
+- Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.
- Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition
- Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
@@ -3520,7 +3520,7 @@ y = 2.0+5*
Write your own code (following the examples under the regression notes) for computing the parametrization of the data set fitting a second-order polynomial.
- Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code.
+ Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to FALSE, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data.
Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as
$$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
diff --git a/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz b/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz
index 84261de1f..f62ea32e6 100644
Binary files a/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz and b/doc/pub/week35/ipynb/ipynb-week35-src.tar.gz differ
diff --git a/doc/pub/week35/ipynb/week35.ipynb b/doc/pub/week35/ipynb/week35.ipynb
index 5ab096820..3e7b2feab 100644
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"## Plans for week 35\n",
"\n",
- "* Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises\n",
+ "* Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises. \n",
"\n",
"* Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition\n",
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@@ -185,7 +185,7 @@
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@@ -197,7 +197,7 @@
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@@ -212,7 +212,7 @@
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@@ -225,7 +225,7 @@
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@@ -237,7 +237,7 @@
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@@ -247,7 +247,7 @@
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@@ -258,7 +258,7 @@
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@@ -287,7 +287,7 @@
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@@ -299,7 +299,7 @@
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@@ -309,7 +309,7 @@
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@@ -321,7 +321,7 @@
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@@ -331,7 +331,7 @@
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@@ -343,7 +343,7 @@
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@@ -353,7 +353,7 @@
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@@ -372,7 +372,7 @@
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@@ -382,7 +382,7 @@
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@@ -394,7 +394,7 @@
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@@ -404,7 +404,7 @@
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@@ -420,7 +420,7 @@
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@@ -440,7 +440,7 @@
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@@ -450,7 +450,7 @@
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@@ -461,7 +461,7 @@
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@@ -480,7 +480,7 @@
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@@ -1981,7 +1981,7 @@
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@@ -2008,7 +2008,7 @@
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@@ -2901,7 +2901,7 @@
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@@ -5380,21 +5380,21 @@
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"source": [
"1. Write your own code (following the examples under the [regression notes](https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html)) for computing the parametrization of the data set fitting a second-order polynomial. \n",
"\n",
- "2. Use thereafter **scikit-learn** (see again the examples in the regression slides) and compare with your own code. \n",
+ "2. Use thereafter **scikit-learn** (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to **FALSE**, see . This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in .\n",
"\n",
"3. Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as"
]
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@@ -5962,7 +5962,7 @@
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@@ -5984,7 +5984,7 @@
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@@ -6009,7 +6009,7 @@
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@@ -6019,7 +6019,7 @@
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@@ -6031,7 +6031,7 @@
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{
"cell_type": "markdown",
- "id": "24481dd4",
+ "id": "cca595e8",
"metadata": {
"editable": true
},
@@ -6041,7 +6041,7 @@
},
{
"cell_type": "markdown",
- "id": "49b7aa71",
+ "id": "7927ee52",
"metadata": {
"editable": true
},
@@ -6053,7 +6053,7 @@
},
{
"cell_type": "markdown",
- "id": "6900d4ad",
+ "id": "110237d1",
"metadata": {
"editable": true
},
@@ -6063,7 +6063,7 @@
},
{
"cell_type": "markdown",
- "id": "50e6bb13",
+ "id": "b995bf7b",
"metadata": {
"editable": true
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diff --git a/doc/src/week35/week35.do.txt b/doc/src/week35/week35.do.txt
index 9d6903cfe..5720003da 100644
--- a/doc/src/week35/week35.do.txt
+++ b/doc/src/week35/week35.do.txt
@@ -6,7 +6,7 @@ DATE: today
!split
===== Plans for week 35 =====
-* Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises
+* Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.
* Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition
* Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
@@ -2598,7 +2598,7 @@ y = 2.0+5*x*x+0.1*np.random.randn(100,1)
!ec
o Write your own code (following the examples under the "regression notes":"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html") for computing the parametrization of the data set fitting a second-order polynomial.
-o Use thereafter _scikit-learn_ (see again the examples in the regression slides) and compare with your own code.
+o Use thereafter _scikit-learn_ (see again the examples in the regression slides) and compare with your own code. When compairing with _scikit_learn_, make sure you set the option for the intercept to _FALSE_, see URL:"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html". This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data".
o Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as
!bt
\[ MSE(\bm{y},\bm{\tilde{y}}) = \frac{1}{n}