update

2022-08-29 10:50:11 +02:00
parent 7e770fbda5
commit 42f9b82bd3
8 changed files with 428 additions and 429 deletions
@@ -389,7 +389,7 @@ MathJax.Hub.Config({
 <h2 id="plans-for-week-35" class="anchor">Plans for week 35 </h2>

 <ul>
-<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises</li>
+<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.</li> 
 <li> Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition</li>
 <li> Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
 </ul>
@@ -500,7 +500,7 @@ y <span style="color: #666666">=</span> <span style="color: #666666">2.0+5*</spa

 <ol>
 <li> Write your own code (following the examples under the <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html" target="_self">regression notes</a>) for computing the parametrization of the data set fitting a second-order polynomial.</li> 
-<li> Use thereafter <b>scikit-learn</b> (see again the examples in the regression slides) and compare with your own code.</li>   
+<li> Use thereafter <b>scikit-learn</b> (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 <b>FALSE</b>, see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" target="_self"><tt>https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html</tt></a>. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data</tt></a>.</li>
 <li> Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as</li>
 </ol>
 $$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
@@ -198,7 +198,7 @@ MathJax.Hub.Config({
 <h2 id="plans-for-week-35">Plans for week 35 </h2>

 <ul>
-<p><li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises</li>
+<p><li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.</li> 
 <p><li> Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition</li>
 <p><li> Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
 </ul>
@@ -3610,8 +3610,7 @@ y = <span style="color: #B452CD">2.0</span>+<span style="color: #B452CD">5</span

 <ol>
 <p><li> Write your own code (following the examples under the <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html" target="_blank">regression notes</a>) for computing the parametrization of the data set fitting a second-order polynomial.</li> 
-<p><li> Use thereafter <b>scikit-learn</b> (see again the examples in the regression slides) and compare with your own code.</li>
-
+<p><li> Use thereafter <b>scikit-learn</b> (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 <b>FALSE</b>, see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" target="_blank"><tt>https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html</tt></a>. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data</tt></a>.</li>
 <p><li> Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as</li>
 </ol>
 <p>
@@ -338,7 +338,7 @@ MathJax.Hub.Config({
 <h2 id="plans-for-week-35">Plans for week 35 </h2>

 <ul>
-<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises</li>
+<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.</li> 
 <li> Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition</li>
 <li> Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
 </ul>
@@ -3443,7 +3443,7 @@ y = <span style="color: #B452CD">2.0</span>+<span style="color: #B452CD">5</span

 <ol>
 <li> Write your own code (following the examples under the <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html" target="_blank">regression notes</a>) for computing the parametrization of the data set fitting a second-order polynomial.</li> 
-<li> Use thereafter <b>scikit-learn</b> (see again the examples in the regression slides) and compare with your own code.</li>   
+<li> Use thereafter <b>scikit-learn</b> (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 <b>FALSE</b>, see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" target="_blank"><tt>https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html</tt></a>. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data</tt></a>.</li>
 <li> Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as</li>
 </ol>
 $$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
@@ -415,7 +415,7 @@ MathJax.Hub.Config({
 <h2 id="plans-for-week-35">Plans for week 35 </h2>

 <ul>
-<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises</li>
+<li> Lab Wednesday: Work on exercises 1-5 for week 35, see end of these slides for the exercises.</li> 
 <li> Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition</li>
 <li> Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</li>
 </ul>
@@ -3520,7 +3520,7 @@ y <span style="color: #666666">=</span> <span style="color: #666666">2.0+5*</spa

 <ol>
 <li> Write your own code (following the examples under the <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter1.html" target="_blank">regression notes</a>) for computing the parametrization of the data set fitting a second-order polynomial.</li> 
-<li> Use thereafter <b>scikit-learn</b> (see again the examples in the regression slides) and compare with your own code.</li>   
+<li> Use thereafter <b>scikit-learn</b> (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 <b>FALSE</b>, see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html" target="_blank"><tt>https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html</tt></a>. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data</tt></a>.</li>
 <li> Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as</li>
 </ol>
 $$ MSE(\boldsymbol{y},\boldsymbol{\tilde{y}}) = \frac{1}{n}
@@ -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}