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Converged at iteration 5
-Runtime: 0.4831998348236084 seconds
+Runtime: 0.4887218475341797 seconds
@@ -840,7 +840,7 @@ two improvements.

Converged at iteration: 5
-Runtime: 0.42905116081237793 seconds
+Runtime: 0.432811975479126 seconds
@@ -866,7 +866,7 @@ i.e. the loop over all the samples. Nonetheless, let us do some profiling!

Converged at iteration: 11
-Runtime: 0.8538670539855957 seconds
+Runtime: 0.8653810024261475 seconds
@@ -977,7 +977,7 @@ fruits of our labor.

Converged at iteration: 5
-Runtime: 0.004904031753540039 seconds
+Runtime: 0.0036406517028808594 seconds
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b/doc/LectureNotes/_build/html/_images/statistics_178_0.png differ diff --git a/doc/LectureNotes/_build/html/_images/statistics_184_1.png b/doc/LectureNotes/_build/html/_images/statistics_184_1.png index e6b459fa4..9f993f3ab 100644 Binary files a/doc/LectureNotes/_build/html/_images/statistics_184_1.png and b/doc/LectureNotes/_build/html/_images/statistics_184_1.png differ diff --git a/doc/LectureNotes/_build/html/_sources/chapter1.ipynb b/doc/LectureNotes/_build/html/_sources/chapter1.ipynb index 9dab26cdc..d2273f8f7 100644 --- a/doc/LectureNotes/_build/html/_sources/chapter1.ipynb +++ b/doc/LectureNotes/_build/html/_sources/chapter1.ipynb @@ -3411,16 +3411,13 @@ "TrainError = np.zeros(maxdegree)\n", "polydegree = np.zeros(maxdegree)\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", - "scaler = StandardScaler()\n", - "scaler.fit(x_train)\n", - "x_train_scaled = scaler.transform(x_train)\n", - "x_test_scaled = scaler.transform(x_test)\n", + "\n", "\n", "for degree in range(maxdegree):\n", " model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n", - " clf = model.fit(x_train_scaled,y_train)\n", - " y_fit = clf.predict(x_train_scaled)\n", - " y_pred = clf.predict(x_test_scaled) \n", + " clf = model.fit(x_train,y_train)\n", + " y_fit = clf.predict(x_train)\n", + " y_pred = clf.predict(x_test) \n", " polydegree[degree] = degree\n", " TestError[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n", " TrainError[degree] = np.mean( np.mean((y_train - y_fit)**2) )\n", diff --git a/doc/LectureNotes/_build/html/_sources/chapter3.ipynb b/doc/LectureNotes/_build/html/_sources/chapter3.ipynb index ad19ad998..4f608ee8b 100644 --- a/doc/LectureNotes/_build/html/_sources/chapter3.ipynb +++ b/doc/LectureNotes/_build/html/_sources/chapter3.ipynb @@ -1708,42 +1708,6 @@ "$$" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We assume \n", - "that every column of $\\boldsymbol{X}$ is centered, which we can do by subtracting the mean," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], - "source": [ - "X = X - np.mean(X,axis=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This means that we need to rewrite $X_{ij}$ as $\\tilde{X}_{ij}=X_{ij}-\\mu_j$, where" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "\\mu_j = \\frac{1}{n}\\sum_{i=0}^{n-1}X_{ij}.\n", - "$$" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1765,7 +1729,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Assuming that the matrix elements $X_{i1}$ are centered, what we have is" + "We obtain then" ] }, { @@ -1773,7 +1737,7 @@ "metadata": {}, "source": [ "$$\n", - "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} \\left(X_{i1}-\\mu_{1}\\right),\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", "$$" ] }, @@ -1781,7 +1745,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "where" + "If we define" ] }, { @@ -1821,7 +1785,7 @@ "metadata": {}, "source": [ "$$\n", - "\\beta_0 = \\mu_y - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} (X_{i1}-\\mu_{1}),\n", + "\\beta_0 = \\mu_y - \\beta_1\\mu_{1}.\n", "$$" ] }, @@ -1829,7 +1793,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "and it is easy to see that the last sum equals zero! This means that we have" + "In the general case withmore parameters than $\\beta_0$ and $\\beta_1$, we have" ] }, { @@ -1837,7 +1801,7 @@ "metadata": {}, "source": [ "$$\n", - "\\beta_0 = \\mu_y,\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", "$$" ] }, @@ -1845,26 +1809,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "if the columns of the design matrix are centered. It is straight forward to generalize this results to more values of $\\beta$.\n", - "We have thus" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$$\n", - "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1} y_i = \\overline{\\boldsymbol{y}},\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "the average value of $\\boldsymbol{y}$.\n", - "\n", - "Replacing $y_i$ with $y_i - \\beta_0 = y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)" + "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)" ] }, { @@ -2238,8 +2183,6 @@ " 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 @ OwnRidgeBeta + intercept_ \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", @@ -2925,8 +2868,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "6\n", - "0\n", + "5\n", + "8\n", " \n", "<\n", "<\n", diff --git a/doc/LectureNotes/_build/html/_sources/schedule.md b/doc/LectureNotes/_build/html/_sources/schedule.md index 572474bbf..e925d1209 100644 --- a/doc/LectureNotes/_build/html/_sources/schedule.md +++ b/doc/LectureNotes/_build/html/_sources/schedule.md @@ -124,7 +124,9 @@ For the reading assignments we use the following abbreviations: ### Week 42 October 18-22 - Lab Wednesday: Work on project 2 - Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureOctober21.mp4?vrtx=view-as-webpage - Lecture Friday: Convolutional Neural Networks and classification problems + - Video of Lecture at https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h21/forelesningsvideoer/LectureOctober22.mp4?vrtx=view-as-webpage - Reading recommendations: - See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html. - For neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. See also chapter 11 and 12 on practicalities and applications diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index 790930373..6fdc7dfdb 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -799,13 +799,13 @@ example of the functionality of Scikit-Learn.

The intercept alpha: 
- [1.9082084]
+ [2.08534155]
 Coefficient beta : 
- [[5.307514]]
-Mean squared error: 0.21
-Variance score: 0.91
+ [[4.76326745]]
+Mean squared error: 0.26
+Variance score: 0.88
 Mean squared log error: 0.01
-Mean absolute error: 0.39
+Mean absolute error: 0.42
_images/chapter1_13_1.png @@ -905,7 +905,7 @@ a linear \(x\)-dependence we s
_images/chapter1_27_0.png -
0.004999999999999996
+
0.0050000000000000044
@@ -1243,7 +1243,7 @@ A 270 3344 160 110 270 Ds 7.253775 7.253775 [267 rows x 6 columns] -0.009883615646716184 +0.009883615646716186
@@ -1300,8 +1300,6 @@ functionality.

warnings.warn( /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. warnings.warn( -/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. - warnings.warn( 
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -1318,8 +1316,6 @@ functionality.

   warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
 
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -1328,6 +1324,12 @@ functionality.

   warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
+
+
+
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
@@ -1356,8 +1358,18 @@ functionality.

warnings.warn( /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. warnings.warn( +
+ +
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
 /Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
   warnings.warn(
+/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
+
+
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
 
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
@@ -1372,15 +1384,7 @@ functionality.

   warnings.warn(
-
/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
-
-_images/chapter1_61_10.png +_images/chapter1_61_12.png @@ -2326,15 +2330,15 @@ the fit becomes better or worse.

-
[ 2.00396825 -0.03005885  5.03970472]
+
[2.00240127 0.09885613 4.86421839]
 Training R2
-0.9951332163662493
+0.994788006691541
 Training MSE
-0.010400010476420727
+0.011239358265567037
 Test R2
-0.9922167804917393
+0.9940257354319456
 Test MSE
-0.01533265828497683
+0.010708859852916969
@@ -2437,13 +2441,13 @@ but now splitting the data into a training set and a test set.

Training R2
-0.9999871853340275
+0.9999940124132914
 Training MSE
-6.459553321949357
+2.1043064188572242
 Test R2
-0.999960835374545
+0.9999292683946882
 Test MSE
-6.844139253527833
+56.241106846210215
@@ -3189,16 +3193,13 @@ ourmodel (here in terms of the polynomial degree of the model).

TrainError = np.zeros(maxdegree) polydegree = np.zeros(maxdegree) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2) -scaler = StandardScaler() -scaler.fit(x_train) -x_train_scaled = scaler.transform(x_train) -x_test_scaled = scaler.transform(x_test) + for degree in range(maxdegree): model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False)) - clf = model.fit(x_train_scaled,y_train) - y_fit = clf.predict(x_train_scaled) - y_pred = clf.predict(x_test_scaled) + clf = model.fit(x_train,y_train) + y_fit = clf.predict(x_train) + y_pred = clf.predict(x_test) polydegree[degree] = degree TestError[degree] = np.mean( np.mean((y_test - y_pred)**2) ) TrainError[degree] = np.mean( np.mean((y_train - y_fit)**2) ) diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 3dcfe5083..bb89f5b0e 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -1886,12 +1886,11 @@ Accuracy score on test set: 0.8666666666666667 Learning rate = 1.0 Lambda = 1e-05 Accuracy score on test set: 0.09166666666666666 - -Learning rate = 1.0 -Lambda = 0.0001
-
Accuracy score on test set:  0.11944444444444445
+
Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.11944444444444445
 
 Learning rate  =  1.0
 Lambda =  0.001
@@ -1927,12 +1926,11 @@ Accuracy score on test set:  0.08611111111111111
 Learning rate  =  10.0
 Lambda =  0.001
 Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  10.0
-Lambda =  0.01
 

 

-
Accuracy score on test set:  0.08888888888888889
+
Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.08888888888888889
 
 Learning rate  =  10.0
 Lambda =  0.1
diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html
index ad78945bd..e80b9104d 100644
--- a/doc/LectureNotes/_build/html/chapter2.html
+++ b/doc/LectureNotes/_build/html/chapter2.html
@@ -1111,10 +1111,10 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
0.05805002932374468
-4.309519578637819
-[[ 1.23033954  3.6804336 ]
- [ 3.6804336  11.92527504]]
+
0.0119839668275544
+3.9026821944532157
+[[ 1.21805483  3.6417002 ]
+ [ 3.6417002  11.67950066]]
 

 

 

@@ -1151,10 +1151,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 

 

 

-
0.09198004868226574
-1.9493620821393187
-[[1.         0.68904673]
- [0.68904673 1.        ]]
+
0.08283349271892
+1.6319972672199556
+[[1.         0.65062105]
+ [0.65062105 1.        ]]
 

 

 

@@ -1184,30 +1184,30 @@ this matrix we easily see that it is a positive definite matrix.

 

 

 

-
[[-1.13352411e+00 -4.86145508e+00]
- [ 5.06793578e-01  2.24339370e+00]
- [ 4.08359441e-03 -1.44109702e+00]
- [-2.02972824e-01 -1.17393096e+00]
- [ 9.99703985e-01  5.00745587e+00]
- [-1.05430325e-01  3.31446832e-02]
- [ 4.31082669e-01  6.38783434e-01]
- [ 3.40259257e-02  5.84099240e-01]
- [-8.44862840e-01 -2.71546272e+00]
- [ 3.11100342e-01  1.68506886e+00]]
+
[[ 0.30199745  0.72164987]
+ [ 1.04858244  2.95539603]
+ [ 0.46840512  1.52940309]
+ [ 0.24117177  1.78848481]
+ [-1.38690487 -4.17705119]
+ [-0.65735283 -2.46141305]
+ [ 0.94916469  2.81774574]
+ [-0.52449732 -2.22140879]
+ [-0.66999836 -2.66511337]
+ [ 0.22943192  1.71230687]]
           0         1
-0 -1.133524 -4.861455
-1  0.506794  2.243394
-2  0.004084 -1.441097
-3 -0.202973 -1.173931
-4  0.999704  5.007456
-5 -0.105430  0.033145
-6  0.431083  0.638783
-7  0.034026  0.584099
-8 -0.844863 -2.715463
-9  0.311100  1.685069
+0  0.301997  0.721650
+1  1.048582  2.955396
+2  0.468405  1.529403
+3  0.241172  1.788485
+4 -1.386905 -4.177051
+5 -0.657353 -2.461413
+6  0.949165  2.817746
+7 -0.524497 -2.221409
+8 -0.669998 -2.665113
+9  0.229432  1.712307
           0         1
-0  1.000000  0.958527
-1  0.958527  1.000000
+0  1.000000  0.975127
+1  0.975127  1.000000
 

 

 

@@ -1264,37 +1264,37 @@ this matrix we easily see that it is a positive definite matrix.

 

 
     0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.082212  0.080493  0.081616  0.077560  0.073611  0.073557  0.069528   
-2   0.0  0.080493  0.080583  0.082413  0.079267  0.075998  0.075624  0.072071   
-3   0.0  0.081616  0.082413  0.087107  0.084071  0.080840  0.082136  0.078414   
-4   0.0  0.077560  0.079267  0.084071  0.081732  0.079085  0.080069  0.076853   
-5   0.0  0.073611  0.075998  0.080840  0.079085  0.076946  0.077662  0.074900   
-6   0.0  0.073557  0.075624  0.082136  0.080069  0.077662  0.079815  0.076731   
-7   0.0  0.069528  0.072071  0.078414  0.076853  0.074900  0.076731  0.074079   
-8   0.0  0.065794  0.068700  0.074858  0.073728  0.072170  0.073713  0.071447   
-9   0.0  0.062352  0.065534  0.071499  0.070739  0.069526  0.070812  0.068888   
-10  0.0  0.065582  0.068203  0.075503  0.074125  0.072352  0.074961  0.072447   
-11  0.0  0.062022  0.064909  0.071917  0.070915  0.069494  0.071782  0.069625   
-12  0.0  0.058765  0.061857  0.068581  0.067904  0.066793  0.068790  0.066951   
-13  0.0  0.055788  0.059038  0.065492  0.065096  0.064256  0.065991  0.064434   
-14  0.0  0.053069  0.056441  0.062636  0.062486  0.061885  0.063380  0.062076   
+1   0.0  0.096033  0.089499  0.100065  0.095636  0.091356  0.093135  0.089366   
+2   0.0  0.089499  0.084679  0.095430  0.091860  0.088348  0.090343  0.087109   
+3   0.0  0.100065  0.095430  0.109996  0.106272  0.102568  0.105983  0.102429   
+4   0.0  0.095636  0.091860  0.106272  0.103072  0.099840  0.103254  0.100074   
+5   0.0  0.091356  0.088348  0.102568  0.099840  0.097041  0.100443  0.097606   
+6   0.0  0.093135  0.090343  0.105983  0.103254  0.100443  0.104606  0.101680   
+7   0.0  0.089366  0.087109  0.102429  0.100074  0.097606  0.101680  0.099049   
+8   0.0  0.085816  0.084032  0.099028  0.097007  0.094850  0.098834  0.096471   
+9   0.0  0.082468  0.081101  0.095771  0.094052  0.092175  0.096070  0.093951   
+10  0.0  0.085269  0.083748  0.099374  0.097448  0.095375  0.099827  0.097488   
+11  0.0  0.082003  0.080845  0.096087  0.094440  0.092628  0.096955  0.094852   
+12  0.0  0.078938  0.078099  0.092968  0.091572  0.089996  0.094201  0.092313   
+13  0.0  0.076058  0.075503  0.090008  0.088838  0.087475  0.091561  0.089869   
+14  0.0  0.073350  0.073045  0.087198  0.086231  0.085061  0.089033  0.087519   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.065794  0.062352  0.065582  0.062022  0.058765  0.055788  0.053069  
-2   0.068700  0.065534  0.068203  0.064909  0.061857  0.059038  0.056441  
-3   0.074858  0.071499  0.075503  0.071917  0.068581  0.065492  0.062636  
-4   0.073728  0.070739  0.074125  0.070915  0.067904  0.065096  0.062486  
-5   0.072170  0.069526  0.072352  0.069494  0.066793  0.064256  0.061885  
-6   0.073713  0.070812  0.074961  0.071782  0.068790  0.065991  0.063380  
-7   0.071447  0.068888  0.072447  0.069625  0.066951  0.064434  0.062076  
-8   0.069161  0.066912  0.069940  0.067442  0.065059  0.062803  0.060678  
-9   0.066912  0.064945  0.067492  0.065289  0.063171  0.061154  0.059245  
-10  0.069940  0.067492  0.071541  0.068800  0.066196  0.063738  0.061430  
-11  0.067442  0.065289  0.068800  0.066372  0.064050  0.061846  0.059767  
-12  0.065059  0.063171  0.066196  0.064050  0.061983  0.060011  0.058141  
-13  0.062803  0.061154  0.063738  0.061846  0.060011  0.058249  0.056570  
-14  0.060678  0.059245  0.061430  0.059767  0.058141  0.056570  0.055065  
+1   0.085816  0.082468  0.085269  0.082003  0.078938  0.076058  0.073350  
+2   0.084032  0.081101  0.083748  0.080845  0.078099  0.075503  0.073045  
+3   0.099028  0.095771  0.099374  0.096087  0.092968  0.090008  0.087198  
+4   0.097007  0.094052  0.097448  0.094440  0.091572  0.088838  0.086231  
+5   0.094850  0.092175  0.095375  0.092628  0.089996  0.087475  0.085061  
+6   0.098834  0.096070  0.099827  0.096955  0.094201  0.091561  0.089033  
+7   0.096471  0.093951  0.097488  0.094852  0.092313  0.089869  0.087519  
+8   0.094138  0.091843  0.095173  0.092754  0.090414  0.088152  0.085968  
+9   0.091843  0.089754  0.092891  0.090673  0.088516  0.086423  0.084394  
+10  0.095173  0.092891  0.096568  0.094135  0.091779  0.089500  0.087297  
+11  0.092754  0.090673  0.094135  0.091901  0.089727  0.087616  0.085567  
+12  0.090414  0.088516  0.091779  0.089727  0.087721  0.085765  0.083859  
+13  0.088152  0.086423  0.089500  0.087616  0.085765  0.083951  0.082179  
+14  0.085968  0.084394  0.087297  0.085567  0.083859  0.082179  0.080529  
 

 

 

@@ -2150,15 +2150,13 @@ set of \(\lambda\) values.

 

 

 
[ 2.03099776 -0.17917768  5.18029127]
-

-

-
Training MSE for OLS
+Training MSE for OLS
 0.009163470508352228
 Test MSE OLS
 0.008675369724976777
 

 

-_images/chapter2_249_2.png
+_images/chapter2_249_1.png
 

 

 
Both these example send a clear message. The addition of a
diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html
index c9ebaa5f1..7293a85ac 100644
--- a/doc/LectureNotes/_build/html/chapter3.html
+++ b/doc/LectureNotes/_build/html/chapter3.html
@@ -645,10 +645,10 @@ number \(i\) is left out. Usin
 

 

 

-
Runtime: 0.135707 sec
+
Runtime: 0.135976 sec
 Jackknife Statistics :
 original           bias      std. error
- 100.099        100.089        0.150795
+  99.655         99.645        0.148675
 

 

 

@@ -867,7 +867,7 @@ theorem.

 

 
Bootstrap Statistics :
 original           bias      std. error
- 100.186  15.0063        100.185        0.148455
+ 100.098   15.248        100.098        0.153966
 

 

 

@@ -1069,9 +1069,10 @@ Error: 0.32149601703519126
 Bias^2: 0.3123314713548606
 Var: 0.009164545680330616
 0.32149601703519126 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912
+Polynomial degree:
-
Polynomial degree: 1
+
 1
 Error: 0.08426840630693411
 Bias^2: 0.07968918676726028
 Var: 0.004579219539673833
@@ -1113,14 +1114,14 @@ Error: 0.017355848195591973
 Bias^2: 0.010331721306655588
 Var: 0.007024126888936384
 0.017355848195591973 >= 0.010331721306655588 + 0.007024126888936384 = 0.017355848195591973
-

-

-
Polynomial degree: 9
+Polynomial degree: 9
 Error: 0.026605727637189085
 Bias^2: 0.010018312644140933
 Var: 0.016587414993048166
 0.026605727637189085 >= 0.010018312644140933 + 0.016587414993048166 = 0.0266057276371891
-Polynomial degree: 10
+

+

+
Polynomial degree: 10
 Error: 0.021592704588043153
 Bias^2: 0.010516485576652981
 Var: 0.011076219011390184
@@ -1135,16 +1136,14 @@ Error: 0.1154777721897675
 Bias^2: 0.01628578269590588
 Var: 0.09919198949386163
 0.1154777721897675 >= 0.01628578269590588 + 0.09919198949386163 = 0.11547777218976751
-

-

-
Polynomial degree: 13
+Polynomial degree: 13
 Error: 0.22842468702166951
 Bias^2: 0.01975416527163567
 Var: 0.20867052175003387
 0.22842468702166951 >= 0.01975416527163567 + 0.20867052175003387 = 0.22842468702166954
 

 

-_images/chapter3_62_5.png
+_images/chapter3_62_4.png

The bias-variance tradeoff summarizes the fundamental tension in @@ -1437,12 +1436,12 @@ Mean squared error on test data: 873.95463048 Degree of polynomial: 23 Mean squared error on training data: 0.00085890 Mean squared error on test data: 5535.20053452 - - -

Degree of polynomial:  24
+Degree of polynomial:  24
 Mean squared error on training data: 0.00084714
 Mean squared error on test data: 1289.22422186
-Degree of polynomial:  25
+
+
+
Degree of polynomial:  25
 Mean squared error on training data: 0.00079022
 Mean squared error on test data: 136582.88824397
 Degree of polynomial:  26
@@ -1801,32 +1800,18 @@ When we take the derivative with respect to \(\boldsymbol{X}\) is centered, which we can do by subtracting the mean,

-

-

-
X = X - np.mean(X,axis=0)
-

-

-

-

-
This means that we need to rewrite \(X_{ij}\) as \(\tilde{X}_{ij}=X_{ij}-\mu_j\), where

-

-\[
-\mu_j = \frac{1}{n}\sum_{i=0}^{n-1}X_{ij}.
-\]

 
Let us special first to the case where we have only two parameters \(\beta_0\) and \(\beta_1\).
 Our result for \(\beta_0\) simplifies then to

 

 \[
 n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1.
 \]

-
Assuming that the matrix elements \(X_{i1}\) are centered, what we have is

+
We obtain then

 

 \[
-\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} \left(X_{i1}-\mu_{1}\right),
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} X_{i1}.
 \]

-
where

+
If we define

 

 \[
 \mu_1=\frac{1}{n}\sum_{i=0}^{n-1} (X_{i1},
@@ -1839,21 +1824,14 @@ n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1.
 
we have

 

 \[
-\beta_0 = \mu_y - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} (X_{i1}-\mu_{1}),
+\beta_0 = \mu_y - \beta_1\mu_{1}.
 \]

-
and it is easy to see that the last sum equals zero!  This means that we have

+
In the general case withmore parameters than \(\beta_0\) and \(\beta_1\), we have

 

 \[
-\beta_0 = \mu_y,
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \frac{1}{n}\sum_{i=0}^{n-1}\sum_{j=1}^{p-1} X_{ij}\beta_j.
 \]

-
if the columns of the design matrix are centered. It is straight forward to generalize this results to more values of \(\beta\).
-We have thus

-

-\[
-\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1} y_i = \overline{\boldsymbol{y}},
-\]

-
the average value of \(\boldsymbol{y}\).

-
Replacing \(y_i\) with \(y_i - \beta_0 = y_i - \overline{\boldsymbol{y}}\) and centering also our design matrix results in a cost function (in vector-matrix disguise)

+
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}).
@@ -1982,7 +1960,7 @@ MSE with Sklearn intercept
 0.004113634617443135
-_images/chapter3_109_1.png +_images/chapter3_103_1.png

The intercept is the value of our output/target variable @@ -2177,7 +2155,7 @@ MSE values for Scikit-Learn Ridge implementation 0.26409315307910036 -_images/chapter3_117_1.png +_images/chapter3_111_1.png

The results here agree when we force Scikit-Learn’s Ridge function to include the first column in our design matrix. @@ -2236,8 +2214,6 @@ Let us see how we can change this code by zero centering.

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 @ OwnRidgeBeta + intercept_ - #Add intercept to prediction ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler RegRidge = linear_model.Ridge(lmb) RegRidge.fit(X_train,y_train) @@ -2382,7 +2358,7 @@ MSE values for Scikit-Learn Ridge implementation 0.002381316302584886 -_images/chapter3_119_1.png +_images/chapter3_113_1.png

We see here, when compared to the code which includes explicitely the @@ -2596,11 +2572,11 @@ linear system as an equation would reduce this down to

-
<ipython-input-21-6f7a6bd7d79f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-20-6f7a6bd7d79f>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

-_images/chapter3_150_1.png
+_images/chapter3_144_1.png

It is interesting to note that OLS @@ -2740,11 +2716,11 @@ with the form utilized in linear regression, viz.

-
<ipython-input-26-5dd54edf2138>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-25-5dd54edf2138>:7: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

-_images/chapter3_168_1.png
+_images/chapter3_162_1.png

The results agree perfectly with our previous discussion where we used our own code.

@@ -2753,8 +2729,8 @@ regression. In ridge regression we include a regularizer. This involves a new cost function which leads to a new estimate for the weights \(\boldsymbol{\beta}\). This results in a penalized regression problem. The cost function is given by

-

6 -0

+

5 +8

< < < @@ -2788,11 +2764,11 @@ K

-
<ipython-input-27-fe5b9d300cc0>:10: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-26-fe5b9d300cc0>:10: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

-_images/chapter3_171_1.png
+_images/chapter3_165_1.png

In the Least Absolute Shrinkage and Selection Operator (LASSO)-method we get a third cost function.

@@ -2823,11 +2799,11 @@ K

-
<ipython-input-28-25845e8df859>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
+
<ipython-input-27-25845e8df859>:9: UserWarning: FixedFormatter should only be used together with FixedLocator
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 

 

-_images/chapter3_175_1.png
+_images/chapter3_169_1.png

It is quite striking how LASSO breaks the symmetry of the coupling @@ -2885,40 +2861,40 @@ constant as opposed to ridge and OLS. We get a sparse solution with 10%|█ | 1/10 [00:00<00:04, 2.02it/s]

-
 20%|██        | 2/10 [00:00<00:03,  2.38it/s]
+
 20%|██        | 2/10 [00:00<00:03,  2.25it/s]
 

 

-
 30%|███       | 3/10 [00:00<00:02,  2.99it/s]
+
 30%|███       | 3/10 [00:00<00:02,  2.82it/s]
 

 

-
 40%|████      | 4/10 [00:00<00:01,  3.72it/s]
+
 40%|████      | 4/10 [00:01<00:01,  3.33it/s]
 

 

-
 50%|█████     | 5/10 [00:01<00:01,  4.40it/s]
+
 50%|█████     | 5/10 [00:01<00:01,  3.94it/s]
 

 

-
 60%|██████    | 6/10 [00:01<00:00,  5.16it/s]
+
 60%|██████    | 6/10 [00:01<00:00,  4.61it/s]
 

 

-
 70%|███████   | 7/10 [00:01<00:00,  5.95it/s]
+
 70%|███████   | 7/10 [00:01<00:00,  5.17it/s]
 

 

-
 80%|████████  | 8/10 [00:01<00:00,  6.55it/s]
+
 80%|████████  | 8/10 [00:01<00:00,  5.73it/s]
 

 

-
 90%|█████████ | 9/10 [00:01<00:00,  6.99it/s]
+
 90%|█████████ | 9/10 [00:01<00:00,  6.46it/s]
 

 

-
100%|██████████| 10/10 [00:01<00:00,  7.35it/s]
+
100%|██████████| 10/10 [00:01<00:00,  6.87it/s]
 

 

-
100%|██████████| 10/10 [00:01<00:00,  5.87it/s]
+
100%|██████████| 10/10 [00:01<00:00,  5.22it/s]
 

 

 

 

 

-_images/chapter3_177_13.png
+_images/chapter3_171_13.png
 

 

 
We see that LASSO reaches a good solution for low
@@ -2967,7 +2943,7 @@ testing set that is close to the accuracy of the training set.

 

 

 

-_images/chapter3_179_0.png
+_images/chapter3_173_0.png
 

 

 
From the above figure we can see that LASSO with \(\lambda = 10^{-2}\)
@@ -3059,7 +3035,7 @@ which polynomial fits the data best.

 

 

 

-_images/chapter3_183_0.png
+_images/chapter3_177_0.png
 

 

 

@@ -3216,7 +3192,7 @@ Python program using

 

 
---------------------------------------------------------------------------
 NameError                                 Traceback (most recent call last)
-<ipython-input-32-d985fb40c43d> in <module>
+<ipython-input-31-d985fb40c43d> in <module>
 ----> 1 scipy.misc.imread
 
 NameError: name 'scipy' is not defined
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index fdf5a4606..ec9001aa6 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -552,9 +552,9 @@ predicting the target features of query instances is as follows:

 

 

 
2nd degree coefficients:
-zero power:  1.055455110765643
-first power:  0.022116476923538613
-second power:  -0.00019348106804731915
+zero power:  -2.575591916051299
+first power:  -0.07310737041838755
+second power:  0.0002644543695187602
 

 

 _images/chapter6_1_1.png
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 34a947499..84f3695fb 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -532,10 +532,10 @@ covariance matrix through the np.linalg.eig() function.

 

 

 

-
-0.1014841451296029
-3.744674093727182
-[[ 1.04621283  3.2509618 ]
- [ 3.2509618  11.28244928]]
+
-0.13740265686642364
+3.647867896223553
+[[0.88732076 2.58645106]
+ [2.58645106 8.40379027]]
 

 

 

@@ -575,10 +575,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 

 

 

-
0.08899392007617646
-1.960919045835183
-[[1.         0.64606848]
- [0.64606848 1.        ]]
+
0.09282061240353208
+2.0679953259809554
+[[1.         0.65276752]
+ [0.65276752 1.        ]]
 

 

 

@@ -607,30 +607,30 @@ this matrix we easily see that it is a positive definite matrix.

 

 

 

-
[[-0.26338419 -0.43128913]
- [-0.40052348 -0.40977892]
- [-1.21495309 -3.03292103]
- [-0.68288031 -2.86961626]
- [ 1.847187    7.221629  ]
- [-0.01324363  0.06365882]
- [-0.08321393 -1.11171059]
- [ 0.6497097   1.58382502]
- [-0.3196681  -1.46807752]
- [ 0.48097003  0.45428062]]
+
[[ 0.27416797  2.69235507]
+ [-0.30895532 -1.36796587]
+ [ 1.03285324  2.17106672]
+ [-0.35304998 -2.85655181]
+ [-0.79084976 -1.67951873]
+ [ 0.19247017  0.868215  ]
+ [-0.45745139 -2.25170803]
+ [-0.63165748 -1.85258411]
+ [-0.19636844  0.14649905]
+ [ 1.238841    4.13019269]]
           0         1
-0 -0.263384 -0.431289
-1 -0.400523 -0.409779
-2 -1.214953 -3.032921
-3 -0.682880 -2.869616
-4  1.847187  7.221629
-5 -0.013244  0.063659
-6 -0.083214 -1.111711
-7  0.649710  1.583825
-8 -0.319668 -1.468078
-9  0.480970  0.454281
+0  0.274168  2.692355
+1 -0.308955 -1.367966
+2  1.032853  2.171067
+3 -0.353050 -2.856552
+4 -0.790850 -1.679519
+5  0.192470  0.868215
+6 -0.457451 -2.251708
+7 -0.631657 -1.852584
+8 -0.196368  0.146499
+9  1.238841  4.130193
           0         1
-0  1.000000  0.959043
-1  0.959043  1.000000
+0  1.000000  0.899606
+1  0.899606  1.000000
 

 

 

@@ -687,37 +687,37 @@ this matrix we easily see that it is a positive definite matrix.

 

 
     0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.077527  0.078731  0.077076  0.078129  0.079210  0.068328  0.069362   
-2   0.0  0.078731  0.081031  0.079460  0.081106  0.082747  0.071284  0.072687   
-3   0.0  0.077076  0.079460  0.081529  0.083368  0.085189  0.075205  0.076784   
-4   0.0  0.078129  0.081106  0.083368  0.085569  0.087738  0.077404  0.079228   
-5   0.0  0.079210  0.082747  0.085189  0.087738  0.090239  0.079562  0.081623   
-6   0.0  0.068328  0.071284  0.075205  0.077404  0.079562  0.071309  0.073110   
-7   0.0  0.069362  0.072687  0.076784  0.079228  0.081623  0.073110  0.075086   
-8   0.0  0.070455  0.074134  0.078402  0.081085  0.083711  0.074934  0.077084   
-9   0.0  0.071609  0.075631  0.080066  0.082982  0.085835  0.076789  0.079111   
-10  0.0  0.059604  0.062733  0.067433  0.069731  0.071979  0.065235  0.067080   
-11  0.0  0.060603  0.063986  0.068834  0.071311  0.073734  0.066778  0.068758   
-12  0.0  0.061660  0.065292  0.070286  0.072940  0.075537  0.068363  0.070479   
-13  0.0  0.062777  0.066653  0.071794  0.074624  0.077395  0.069997  0.072248   
-14  0.0  0.063954  0.068071  0.073361  0.076367  0.079312  0.071682  0.074070   
+1   0.0  0.090368  0.089828  0.084745  0.089694  0.094385  0.072722  0.077607   
+2   0.0  0.089828  0.091016  0.082203  0.087699  0.093293  0.069684  0.074647   
+3   0.0  0.084745  0.082203  0.084433  0.088351  0.091653  0.075222  0.079774   
+4   0.0  0.089694  0.087699  0.088351  0.092763  0.096661  0.078226  0.083104   
+5   0.0  0.094385  0.093293  0.091653  0.096661  0.101332  0.080548  0.085760   
+6   0.0  0.072722  0.069684  0.075222  0.078226  0.080548  0.068780  0.072668   
+7   0.0  0.077607  0.074647  0.079774  0.083104  0.085760  0.072668  0.076854   
+8   0.0  0.082726  0.079963  0.084408  0.088121  0.091191  0.076564  0.081069   
+9   0.0  0.088020  0.085632  0.089011  0.093180  0.096774  0.080344  0.085192   
+10  0.0  0.062054  0.059107  0.065855  0.068244  0.069993  0.061366  0.064683   
+11  0.0  0.066283  0.063260  0.070074  0.072690  0.074644  0.065135  0.068701   
+12  0.0  0.070793  0.067729  0.074516  0.077390  0.079587  0.069075  0.072910   
+13  0.0  0.075587  0.072539  0.079162  0.082333  0.084822  0.073160  0.077286   
+14  0.0  0.080656  0.077711  0.083974  0.087490  0.090338  0.077343  0.081784   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.070455  0.071609  0.059604  0.060603  0.061660  0.062777  0.063954  
-2   0.074134  0.075631  0.062733  0.063986  0.065292  0.066653  0.068071  
-3   0.078402  0.080066  0.067433  0.068834  0.070286  0.071794  0.073361  
-4   0.081085  0.082982  0.069731  0.071311  0.072940  0.074624  0.076367  
-5   0.083711  0.085835  0.071979  0.073734  0.075537  0.077395  0.079312  
-6   0.074934  0.076789  0.065235  0.066778  0.068363  0.069997  0.071682  
-7   0.077084  0.079111  0.067080  0.068758  0.070479  0.072248  0.074070  
-8   0.079253  0.081450  0.068941  0.070753  0.072608  0.074513  0.076473  
-9   0.081450  0.083817  0.070824  0.072770  0.074761  0.076803  0.078901  
-10  0.068941  0.070824  0.060591  0.062149  0.063743  0.065378  0.067060  
-11  0.070753  0.072770  0.062149  0.063814  0.065517  0.067262  0.069055  
-12  0.072608  0.074761  0.063743  0.065517  0.067329  0.069185  0.071092  
-13  0.074513  0.076803  0.065378  0.067262  0.069185  0.071155  0.073178  
-14  0.076473  0.078901  0.067060  0.069055  0.071092  0.073178  0.075318  
+1   0.082726  0.088020  0.062054  0.066283  0.070793  0.075587  0.080656  
+2   0.079963  0.085632  0.059107  0.063260  0.067729  0.072539  0.077711  
+3   0.084408  0.089011  0.065855  0.070074  0.074516  0.079162  0.083974  
+4   0.088121  0.093180  0.068244  0.072690  0.077390  0.082333  0.087490  
+5   0.091191  0.096774  0.069993  0.074644  0.079587  0.084822  0.090338  
+6   0.076564  0.080344  0.061366  0.065135  0.069075  0.073160  0.077343  
+7   0.081069  0.085192  0.064683  0.068701  0.072910  0.077286  0.081784  
+8   0.085636  0.090149  0.067977  0.072252  0.076743  0.081428  0.086266  
+9   0.090149  0.095107  0.071137  0.075674  0.080455  0.085465  0.090671  
+10  0.067977  0.071137  0.055542  0.058856  0.062305  0.065864  0.069488  
+11  0.072252  0.075674  0.058856  0.062396  0.066086  0.069899  0.073789  
+12  0.076743  0.080455  0.062305  0.066086  0.070032  0.074118  0.078298  
+13  0.081428  0.085465  0.065864  0.069899  0.074118  0.078497  0.082989  
+14  0.086266  0.090671  0.069488  0.073789  0.078298  0.082989  0.087822  
 

 

 

@@ -906,10 +906,10 @@ We can write our own code or simply use either the functionaly of numpy<
 

 

 
          0         1
-0  3.982025  2.012742
-1  2.012742  2.042269
-[[3.98202489 2.01274179]
- [2.01274179 2.04226949]]
+0  3.900266  1.942180
+1  1.942180  1.965724
+[[3.90026646 1.9421795 ]
+ [1.9421795  1.96572363]]
 

 

 

@@ -936,8 +936,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 

 

 
Centered covariance using own code
-[[3.98202489 2.01274179]
- [2.01274179 2.04226949]]
+[[3.90026646 1.9421795 ]
+ [1.9421795  1.96572363]]
 

 

 _images/chapter8_65_1.png
@@ -997,16 +997,16 @@ questions.

 

 

 
Eigenvalues of Covariance matrix
-5.246379112732898
-0.7779152676639822
+5.102712819944882
+0.763277270613554
 First eigenvector
-[0.84678774 0.53193095]
+[0.85023678 0.52640043]
 Second eigenvector
-[-0.53193095  0.84678774]
+[-0.52640043  0.85023678]
 

 

 
Eigenvector of largest eigenvalue
-[0.84678774 0.53193095]
+[-0.85023678 -0.52640043]
 

 

 

diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
index 5cdcb785a..837f79068 100644
--- a/doc/LectureNotes/_build/html/chapteroptimization.html
+++ b/doc/LectureNotes/_build/html/chapteroptimization.html
@@ -752,7 +752,7 @@ which equals

 

 

 

-
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7fc6b1bd1790>
+
<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f9f087db790>
 

 

 _images/chapteroptimization_56_1.png
@@ -810,7 +810,7 @@ which equals

 

 

 

-
[<matplotlib.lines.Line2D at 0x7fc6a0739340>]
+
[<matplotlib.lines.Line2D at 0x7f9f1a921340>]
 

 

 _images/chapteroptimization_64_1.png
@@ -1067,11 +1067,11 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[0.30875823 4.41879302]
-[[3.96285336]
- [3.28329188]]
-[[3.96285336]
- [3.28329188]]
+
[0.30862336 4.94226154]
+[[4.09553421]
+ [2.99517651]]
+[[4.09553421]
+ [2.99517651]]
 

 

 _images/chapteroptimization_118_1.png
@@ -1100,9 +1100,9 @@ when \(||\nabla_\beta C(\beta_k) || \
 

 

 

-
[[3.75582035]
- [3.15167261]]
-[3.7821634] [3.18303503]
+
[[4.1224808 ]
+ [2.97290346]]
+[4.09600813] [2.93854356]
 

 

 

@@ -1173,10 +1173,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
 

 

 

-
[[4.05636001]
- [2.75240368]]
-[[3.99394477]
- [2.80463702]]
+
[[3.95338481]
+ [2.95611545]]
+[[3.99545147]
+ [2.91812296]]
 

 

 _images/chapteroptimization_127_1.png
@@ -1401,21 +1401,19 @@ function.

 

 

 
Own inversion
-[[3.88168518]
- [3.04348504]]
+[[3.87218485]
+ [3.01403105]]
 sgdreg from scikit
-[3.92115534] [3.13964362]
+[3.9030809] [3.07154525]
 theta from own gd
-[[3.88168518]
- [3.04348504]]
+[[3.87218485]
+ [3.01403105]]
+theta from own sdg
+[[3.86110774]
+ [3.00070278]]
 

 

-
theta from own sdg
-[[3.92555588]
- [3.0163616 ]]
-

-

-_images/chapteroptimization_141_2.png
+_images/chapteroptimization_141_1.png
 

 

 

diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 4d61bfaf1..63f11e249 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -455,8 +455,8 @@ matrices and vectors.

 

 

 

-
[-0.98226332  0.21126669  0.23494196  1.04532409 -0.994015   -0.37831577
- -0.22534931  0.39445208 -1.97921062  1.042552  ]
+
[-0.62370636  0.34673123  0.80408254  2.08638654  1.16894795  1.8464107
+  0.13620973 -1.33881021 -0.05404886 -0.99995891]
 

 

 

diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 282df1ad3..6ef3e56d3 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -585,8 +585,16 @@
 
Week 42 October 18-22

 
    
 
  • Lab Wednesday: Work on project 2
    • 
-
  • Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
    • 
-
  • Lecture Friday: Convolutional Neural Networks and classification problems
    • 
+
  • Lecture Thursday: Solving differential equations with neural networks and start Convolutional Neural Networks and classification problems
    
+
+
    • 
+
  • Lecture Friday: Convolutional Neural Networks and classification problems
    
+
+
    • 
 
  • Reading recommendations:
    
 
      
 
    • See lecture notes for week 42 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
      • 
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 594a34612..b1f06feee 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
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diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html
index 331aab2cd..392e57d89 100644
--- a/doc/LectureNotes/_build/html/statistics.html
+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -1216,27 +1216,27 @@ uncorrelated.
      
 

 

 

-
0.9524796542295857
-[[12.32978852 17.93351637  8.83074569  6.12730791  9.17878199 10.22862364
-  14.56202678 10.49110643  8.12113897 14.46581332]
- [17.93351637 26.08406534 12.84420428  8.91208933 13.35041853 14.87739949
-  21.18027777 15.25917728 11.81209057 21.04033655]
- [ 8.83074569 12.84420428  6.32468833  4.38845304  6.57395619  7.32586565
-  10.4295021   7.51385904  5.81645929 10.36059285]
- [ 6.12730791  8.91208933  4.38845304  3.04497536  4.56141023  5.0831307
-   7.23662224  5.21357193  4.03581286  7.18880881]
- [ 9.17878199 13.35041853  6.57395619  4.56141023  6.83304817  7.61459179
-  10.8405484   7.80999435  6.04569689 10.7689233 ]
- [10.22862364 14.87739949  7.32586565  5.0831307   7.61459179  8.48552604
-  12.08045792  8.70327816  6.73718563 12.00064055]
- [14.56202678 21.18027777 10.4295021   7.23662224 10.8405484  12.08045792
-  17.19839911 12.39046173  9.59142511 17.08476676]
- [10.49110643 15.25917728  7.51385904  5.21357193  7.80999435  8.70327816
-  12.39046173  8.92661816  6.91007256 12.30859612]
- [ 8.12113897 11.81209057  5.81645929  4.03581286  6.04569689  6.73718563
-   9.59142511  6.91007256  5.3490697   9.52805315]
- [14.46581332 21.04033655 10.36059285  7.18880881 10.7689233  12.00064055
-  17.08476676 12.30859612  9.52805315 16.97188519]]
+
2.4890704720929593
+[[11.60990408  7.82544198 14.11622885  1.78979284  6.76376813 14.09060397
+  14.04624055  3.77253612  5.11082097 14.99821162]
+ [ 7.82544198  5.27459502  9.51478402  1.20637689  4.55899331  9.49751204
+   9.4676097   2.54280848  3.44485473 10.10926826]
+ [14.11622885  9.51478402 17.16361442  2.17617003  8.22391797 17.13245767
+  17.07851716  4.58694429  6.21413561 18.23599801]
+ [ 1.78979284  1.20637689  2.17617003  0.27591601  1.04270833  2.17221967
+   2.16538057  0.58157742  0.78788857  2.31213725]
+ [ 6.76376813  4.55899331  8.22391797  1.04270833  3.94047694  8.20898927
+   8.18314376  2.19782691  2.97749299  8.73774883]
+ [14.09060397  9.49751204 17.13245767  2.17221967  8.20898927 17.10135749
+  17.04751489  4.57861771  6.20285522 18.20289459]
+ [14.04624055  9.4676097  17.07851716  2.16538057  8.18314376 17.04751489
+  16.99384182  4.56420221  6.18332591 18.14558387]
+ [ 3.77253612  2.54280848  4.58694429  0.58157742  2.19782691  4.57861771
+   4.56420221  1.2258524   1.66071628  4.87353683]
+ [ 5.11082097  3.44485473  6.21413561  0.78788857  2.97749299  6.20285522
+   6.18332591  1.66071628  2.24984554  6.60239515]
+ [14.99821162 10.10926826 18.23599801  2.31213725  8.73774883 18.20289459
+  18.14558387  4.87353683  6.60239515 19.37538418]]
 

 

 

@@ -1544,15 +1544,15 @@ more practically oriented methods like the blocking technique.

 

 

 

-
0.0553457008138535
-4.04568240116212
-0.3078370846883027
-0.8876822883855056 9.132825151794417 26.525617578564635
-2.6555465443226955 3.4427165765504326 10.884978392264447
-[[ 0.88768229  2.65554654  3.44271658]
- [ 2.65554654  9.13282515 10.88497839]
- [ 3.44271658 10.88497839 26.52561758]]
-[32.33737942  0.10238075  4.10636484]
+
-0.000171990965637209
+3.967581388294481
+-0.025521898567525465
+0.853884005749657 8.208733310166176 9.244798847617261
+2.497355838775297 2.088848443911507 6.174325810031482
+[[0.85388401 2.49735584 2.08884844]
+ [2.49735584 8.20873331 6.17432581]
+ [2.08884844 6.17432581 9.24479885]]
+[15.62869983  0.07851446  2.60020187]
 

 

 

@@ -1959,7 +1959,7 @@ assumption for approximating \(\sigma
 

 

 

-
-0.00532744652682568 1.0584909962471034
+
0.005481494389472717 1.029117514654528
 

 

 _images/statistics_184_1.png
diff --git a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
index 82c23b74b..94a43c061 100644
--- a/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb
@@ -475,7 +475,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.4697279930114746 seconds\n"
+      "Runtime: 0.4887218475341797 seconds\n"
      ]
     }
    ],
@@ -604,7 +604,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.41312265396118164 seconds\n"
+      "Runtime: 0.432811975479126 seconds\n"
      ]
     }
    ],
@@ -745,7 +745,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.8252480030059814 seconds\n",
+      "Runtime: 0.8653810024261475 seconds\n",
       " "
      ]
     }
@@ -877,7 +877,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.0037932395935058594 seconds\n"
+      "Runtime: 0.0036406517028808594 seconds\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
index 761ea4b64..1c0661faf 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb
@@ -310,7 +310,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -461,7 +461,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -526,18 +526,18 @@
      "output_type": "stream",
      "text": [
       "The intercept alpha: \n",
-      " [1.93113373]\n",
+      " [2.08534155]\n",
       "Coefficient beta : \n",
-      " [[5.06182265]]\n",
-      "Mean squared error: 0.20\n",
-      "Variance score: 0.91\n",
+      " [[4.76326745]]\n",
+      "Mean squared error: 0.26\n",
+      "Variance score: 0.88\n",
       "Mean squared log error: 0.01\n",
-      "Mean absolute error: 0.37\n"
+      "Mean absolute error: 0.42\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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arRD28Y+3X86woAWoJ1GBFAhE8lDE9A7155wxA5gyZeI+zYLRZz+bzYCw+qAF5NOTSN1WI6nXkEgvGR11OYHHHmveaygqAGQhj55EnfTaKgn1GhKReFoteRjWDPT8850FgVZP4nn0JPJ5UR4PKBCI+C6PJo2wALDffi4AHHhg8uPGGUCWR08idVttSoFAxGdZj8RtlggOWzmsXXGexPPoSaRuq00pEIj4LKsmjXXrwgNApQKMjHR27FpxnsSbJdLTqg2p22pzZubda+7cuSYiZkZW++hMfJHxPj8yYlapuP0rFfd32PFqXwMDbr80VCrh56hU4pV9YCC9soVdiy4CYMwS3nPVa0jEZ530qAnrKVNvxgw35USS48eR5RxLMoF6DYl0q06aNMKalWqZucnnwqSVRO1k/IQSvLlRIBDxWSc30rCnacAdp9oSkEcStVWX1ShK8OZGgUDEd0lupFHTQwMTb6Q+J1F9LluXySUQkNxA8j6S95BU479IVqK6g1bV30iLmPoiLp/L1mVySRaT3ABg0My2xNlfyWKRNkXd/EdG4k0pIaWnZLFIlnyerKzZgDCz5O3z0lPyCgQG4OckV5McDtuB5DDJMZJjmzdvzqlYIi34usbu/fdnMzOo9KS8moYON7MnSL4KwK0APmVmd0Ttr6Yh8YaPfdnznBlUSsP7piEzeyL4uQnAjwC8NY/zinQsaV/2LJqTwpqBLrlEQUA6NjnrE5CcCmCSmW0Pfn8vgIuyPq9IKmbPDq8RNOvLXj+attqcBCRro49KBCsASEryqBEcAuA/Sf4ewG8B/MTMfprDeUU6l6Qve1oTxb33vcoDSC4yDwRmtt7M3hy83mBmGg0i5ZGkL3saUyOQwK23TtyWNAD43OtJvJB505BI6Q0Ntdekk6Q5qSqsBvDss8DBB8c/f620m6mkK2kcgUjakjQnNRsPkDQILF4MzJ+vJRqlJQUCkU6ENbu005zUakBYUosXA9/6VvT7msFTaqhpSCSpVs0uzZpe7rkHOPHExu1pJYGXL2/+vmbwlBqqEYgklbR3ENkYBNLuCbR3b/R7msFT6igQiCTVbu+gsGagr30tm66gfX3R72kGT6mjQCD+87X7Y9yFU5rlAc4/P/1yAeNNVPUWLVIQkAYKBOI3Xyd9A1r3DjrllOIGhF1+ubvpV2sGfX3u78svz/a8UkpavF785uOkb7VGR8Pn+9fEcJKzTiadUyAQv02aFH4DJd0c+75Je0CYSEzezz4qklhZFjDPYkCYSE4UCMRvPi9gPjqa3YAwkRwpEIh/ansJLV0KnH22fwuYL1vmpm+oNzKiACCloxyB+KV+tC7gagA+3PyrotYHAPxJYkvPUY5Aukdac/lnIaoZqFavz+Hj65gPaUqBQPySxlz+aYsTAKpe+crevRH6POZDmlIgEL/41EvotNOiA8DUqcCUKRO39fcD27dPvBEuXAjMnNkbgcHn2pw0pUAgfvGllxAJ/OIX0e/v2AHs2QPMmDGexJ4+Hdi1a+J+u3cDW7f2xhOyj7U5iUWBQPySZGnINIU1Ax15ZPi++/YB06a5nxs2AM880/r43fyE7FNtTtqiQNANui1BNzTkbqzVG2weQaDZeICNG6M/V/u0G/eG161PyL7U5qRtCgRlV9YE3eLFwOTJ7uY7ebL7uwhxBoQ1u8HXvhd2I2z1mTKKevAoujYnyZmZd6+5c+eaxFSpVG9ZE1+VStEli7ZoUXiZFy3Krwxr1oSXIczIiNmUKY379ve79+r3rVTMSLMZMxo/NzDQ+JkyGRlx36GbvlOXADBmCe+5GlBWdmWblA1wNYCwFbT6+lwCNmtJZgYdHQXOO88lfgGXJL700tZPu1Gzk5aV77PB9rBMB5SRvJXkm5McXHJQxgRd1DKKzZZXTENYM9All8SbEmJoCNiyZfw5eMuWeDf0OPmOMuV41DOoK8XJEfwjgEtIXkPy0KwLJG0qY4IuahnFZssrdqJZHuC887I5Z1xly/GU8cFDWmoZCMzsbjN7F4CbAPyU5AUkD8i+aBJLGRN0UcsoRm1P6s/+zP+ZQcs2CKuMDx7SUqwcAUkCeAOAdwD4KoCXAHzBzK7NolDKEfSAxYtdwNq719UEhofTW0bRzDWzhG33TRlzPN2W9+gSma5QRnIVgKMAPADgTgB3AfgDgPMA7GdmKT/GKRBIB8JqANu2AQcdlHtRYlHyVVKS9eyjwwAON7P3mNmXzOwmM1tnZp8CcEqSk4o06DRh2iwP4GsQANTUIl6IkyN4wKKrDX+VcnmkF3WSMC37CmFlzPFI1+loZLGZrU+rIFKwIrswJkmYrllT7gBQq4gpNURqTM7rRCT7AIwBeMLM3p/XeSWG+lXBqk/kQD43pXb7picZECYikfKca+g8AA/meD6JK+4TeVa1hrh908OagS69VEFApEO5BAKSR8DlE67K43zSpjhP5FkOfGqVMG2WB/j0pzs/v0iPy6tGcAmA8wFEdowmOUxyjOTY5s2bcyqWAIj3RJ7lwKeohOnKld2TBxDxWOaBgOT7AWwys9XN9jOz5WY2aGaDs2bNyrpYUitOF8as55ipTZg+8ggwfz5w660T91EAEMlEHjWCtwP4AMkNAL4H4C9IjuRwXokrThfGvOaYIRtHBT//vAKASIYyDwRm9gUzO8LM5gA4E8AvzGx+1ueVNrXqwpj1wKdmeYADD0znHCISSiuUlV1UT560e/hkNfCp7APCRLpAbuMIAMDMbgdwe57n7GpR/f9XrXKJ1jTGBWQ1wdj99wNvfGPjdt38RXKnFcrKLGrCsr6+8EVe2p3IrD7QAK45qNOagAaEiaQu60nnxFdRPXaiVvpqt4dP2l1Gw5qBVqxQEBApWK5NQ5Ky2bPbqxG028MnrS6jYTUAQAFAxBOqEZRZVE+e4eF0evh02mX0Ix9RIlikBBQIyiyqJ8/ll6fTwydpl1Ezd94f/rBxuwKAiHeULJbm2u01FFYD2L4dmDYtuzKKiJLF0oZ2xxfEnSu/2XgABQERrylZ3EuyWHdAiWCR0lONoJek2R30vvvKkQgucuU1kZJQjaCXZNkd1Kebf1XRK6+JlIRqBL2k0+6gYXmAa67xMwgA2a6hINJFFAh6SdLuoM0SwQsWxD9/3s00Wa+hINIleiMQpHEDKktbc7NytjuD6F//dTp5gNFRYOZMt9hMFktdRslrDQWRsjMz715z58611IyMmA0MVG9d7jUw4LbneYwsjIyYVSpmpPu5aFE65dy3b+Ixqq+kZawvU+2rUkl23KTn9uG/m0gGAIxZwntu4Tf9sFeqgaBS6fwGlMYx0hZ2kyM7L2fY5194IXk5o65dffmqwSztm3R9sFQQkC7VSSDo/pHFkyaFN2OQbpBUXsdIW9QU1GHilDOr8QBR1672vLXvpzHNdVayWptBJAUaWdxMGu3EPrY1t5PwbFbOrFcIa3Xu+nPs3AmcfbZ/uZhqV9Q8cxwiOen+QJDGWrtZr9ebRNQNtv6mHlbO0VHgsMPyGRAWdu0AYMaM6PPs3Tt+s50/3yWai77hqiuqdLOkbUpZvlLNEZil007sW1tzVCJ00aLm5RwZCW+nz/L7RF27OPkDX5K8UfkXsrgyidSAcgQ9Ko2ZQavaXcYyDWFLYTZTRBmronIyRZZJpIZyBL2q05lBaxUxyKp+XENfX/P9ixwI5mPzoEhKFAi62d//fesAUFVU4rs2mK1cGZ5PqCoyOd/uYDyRElEg6Dajo+M3q+XLJ75nBoyM+PtkW73ZzpjR+J4PZYxbAxMpGQWCbjI66nrZ1DehrFgx3kMn7GZ7wAH5lbGVoSFgyxYXsPT0LZILTUPdLZo1AV14IbBw4cRtL744/vvWrf5Nzzw05E9ZRLqcagRllyQRXFSf+LJM3CfSYxQIymrduuSJ4CKmZ9bIXBFvKRCUEQkce+zEbe0kgouYMkMjc0W8pUBQJmHNQD/5SWMiuFWStYg+8VnXQtTsJJKYksVl0M7MoHGSrNX385xJc/bs8JG5adRCtDaxSEcyrxGQ3J/kb0n+nuQDJC/M+pxd47LLooPAwEBnT71594nPshaiZieRjuTRNPQygL8wszcDOAHA6SRPzuG85WXmAsCnPx29T9ludFmOzNXaxCIdyTwQBBPjvRD82R+8/Jvprh1ZtkeT7rj128KU7UaXVS3Ex/UiREokl2QxyT6S9wDYBOBWM7srZJ9hkmMkxzZv3pxHsZLJqhtkWCJ4wQJ3Dt3omtOEcCIdySUQmNleMzsBwBEA3kry+JB9lpvZoJkNzpo1K49iJZN2e/TBB0cvEHPNNe533eia04RwIh3JtfuomW0DcBuA0/M8b6qi1glut5nm8cfdTeu55yZuD1shTDe61jQhnEhimXcfJTkLwG4z20byAADvAfC1rM+bicWLo99rp5kmqgbQjObeEZGM5FEjOBTAbSTvBfA7uBzBTTmcN12jo8AVV4S/R8ZrpgnLA6xale4awSIibcq8RmBm9wI4MevzZG7p0ugbtlmyJSIVAETEA5piIq5mOYBKJXz7ypXRzUAKAiLiie4PBEn6/Id9JioHENYsVB0QtmBB4/Y0AoDm1RGRFHV3IEjS5z/qM2ec0diFkwTOPXdis1DYgLCXX06vBqDpnEUkZd0dCJL0+Y/6zM03N3bhvPZa4PLL3T5hieALL3Q36ylTOv8urcpXpukmRMQrNA/bqgcHB21sbKzzA02aFP4kTrr+5ml85kMfAn7848btWV3XJN9JRLoeydVmNpjks91dI0gyNUPcz2za5G6+9UGgukBMVm34mm5CRFLWXYGgPoka1q7famqGONM5kMAhh0zcp5oIzroNX9NNiEjazMy719y5c61tIyNmAwPV27F7DQyYLVpkVqmYke7nyEi8Y4V9pvbY1dfatRM/W6mE71eptP+dosqT5DvFOW7S44hI4QCMWcJ7bvfkCObMCZ8HqFJxc8904uijgfXrJ26bOxcIK2Pabfj1q28BrgbQ6VxDWR1XRAqhHAGQzeIkv/61u4HXBwGz8CAARLfVv/KVwMyZ472LZs6M11wUt5dQu2ML1PtIRALdEwjSTKJWB4S9/e2N21vVoMLa8Pv73SyjW7eOb9u6FVi4sPUNO06AS5KX0KpeIhLonkCQVhI1bEDYnj3xu4OGTRk9fbo7Rr3du1s/gccJcEme7tX7SEQC3RMIOp2zP2xA2E9/6gJAX1/7ZamdG/+ZZ6L3bfUEvmxZ44C0KVMmBrgkT/fqfSQige4JBECyxUkuu6wxABxyiAsAf/mX6ZQrybiFWvW1kfq/kzzda7EbEQl0T6+hdj3/PHDQQY3bs7geo6PAOecAu3ZN3N7f75ajbHbzjdMbSj2ARHqeeg21i2wMAllODT00BKxYAcyYMb5t6lSXO/jEJ5r38onT7KOnexHpQG8FgrA8wBNP5LM2wNAQsGXL+BQUZq7nUKtePnGbfbRmr4gk1BuB4JxzGgPAkiXuJnzYYfmXp51ePkrqikjGMl+qslAPPwwcc0zj9qLzIu308qk+2S9d6t6fPdsFAT3xi0hKujMQmDWOBahu98Hs2eEJ4KhmoKEh3fhFJDPd1zQUNiBs715/ggCg5h4R8Up5AkGruXTOOqsxD7B6dXTtoEjq5SMiHvHsDhmh2Vw6a9e6m+m1147vf9FFbr+3vCWfsiVZhEa9fETEE+XIEUT1spk/v3HfPJuA6gdyVQMUoBu7iJRGOWoEcWbEHBlxTSxZLA8ZRVM5i0gXKEeNIKqXDQDs2AH86EfFPJlrKmcR6QLlqBGcdVbjtv33d7WAgYHinsw1lbOIdAG/A8FLLwHHHQd85SsTt1cqwFVXjT/tF/Vkrm6gItIF/A0En/wkcMABwEMPub/nzRufGK6+l01RT+bqBioiXcDPaaiPOcbGHn7Y/bFwIXD11Y1jBGppGmYR6XHdNw31tGnAxRcDL7zgpm9uFgQAPZmLiHQg80BA8kiSt5FcS/IBkue1/NDkycDnPufm7I9jdFSTsomIJJRH99E9AJaY2d0kDwSwmuStZrY2laNrUJeISEcyrxGY2VNmdnfw+3YADwI4PLUTaFCXiEhHcs0RkJwD4EQAd4W8N0xyjOTY5s2b4x9Ug7pERDqSWyAgOQ3ADwB8xsyer3/fzJab2aCZDc6aNSv+gTWoS0SkI7kEApL9cEFg1Mx+mOrBNahLRKQjefQaIoCrATxoZt9I/QTqOioi0pHMB5SRfAeAXwG4D8C+YPM/mdnNUZ8ZHBy0sbGxTMslItJNvB5QZmb/aWY0szeZ2QnBKzIINEi68IuIiMTi9zTUGiMgIpI5P6eYqNIYARGRzPkdCDRGQEQkc34HAo0REBHJnN+BQGMEREQy53cg0BgBEZHM+d1rCHA3fd34RUQy43eNQEREMudnILjvPg0gExHJiZ9NQ7t2uZ8aQCYikjk/awS1NIBMRCRT/gcCQAPIREQyVI5AoAFkIiKZ8T8QaACZiEimMl+PIIlXkvteDXA3sOtJ4IktwDNFl6lAMwFsKboQHtB1GKdrMU7XYtxrzezAJB/0MhCQHEu6wEK30bVwdB3G6VqM07UY18m18L9pSEREMqVAICLS43wNBMuLLoBHdC0cXYdxuhbjdC3GJb4WXuYIREQkP77WCEREJCcKBCIiPa6wQEDydJIPkVxH8vMh7+9H8rrg/btIzimgmLmIcS0+R3ItyXtJ/gfJShHlzEOra1Gz30dIGsmu7ToY51qQ/Fjwb+MBkt/Nu4x5ifH/yGySt5FcE/x/ckYR5cwDyRUkN5G8P+J9kvyX4FrdS/ItLQ9qZrm/APQBeBjAqwFMAfB7AK+v22cxgCuC388EcF0RZfXkWrwLwEDw+6JevhbBfgcCuAPAnQAGiy53gf8ujgWwBsArgr9fVXS5C7wWywEsCn5/PYANRZc7w+vx5wDeAuD+iPfPAHALAAI4GcBdrY5ZVI3grQDWmdl6M9sF4HsAPli3zwcBrAx+vwHAaSSZYxnz0vJamNltZrYz+PNOAEfkXMa8xPl3AQBfAfA1AC/lWbicxbkWfwfgm2b2LACY2aacy5iXONfCAEwPfj8IwJM5li9XZnYHms+28EEA/9ucOwEcTPLQZscsKhAcDuDxmr83BttC9zGzPQCeAzAjl9LlK861qPW3cNG+G7W8FkE190gz+0meBStAnH8XrwHwGpKrSN5J8vTcSpevONfiywDmk9wI4GYAn8qnaF5q957i6cI0EorkfACDAE4tuixFIDkJwDcALCi4KL6YDNc89E64WuIdJN9oZtuKLFRBPg7gO2Z2Mcm3AbiW5PFmtq/ogpVBUTWCJwAcWfP3EcG20H1IToar7m3NpXT5inMtQPLdAJYC+ICZvZxT2fLW6locCOB4ALeT3ADX/nljlyaM4/y72AjgRjPbbWaPAPi/cIGh28S5Fn8L4HoAMLPfANgfbkK6XhTrnlKrqEDwOwDHkjyK5BS4ZPCNdfvcCODs4Pe/AfALCzIhXabltSB5IoAr4YJAt7YDAy2uhZk9Z2YzzWyOmc2By5d8wMzGiilupuL8P/JvcLUBkJwJ11S0Pscy5iXOtXgMwGkAQPJ1cIFgc66l9MeNAM4Keg+dDOA5M3uq2QcKaRoysz0kPwngZ3A9AlaY2QMkLwIwZmY3Argarnq3Di4xcmYRZc1azGvxdQDTAHw/yJc/ZmYfKKzQGYl5LXpCzGvxMwDvJbkWwF4A/93Muq7WHPNaLAHwbZKfhUscL+jSB0eQ/Fe4B4CZQU7kAgD9AGBmV8DlSM4AsA7ATgALWx6zS6+ViIjEpJHFIiI9ToFARKTHKRCIiPQ4BQIRkR6nQCAi0uMUCEREepwCgYhIj1MgEIkhmOv+PcHvXyV5WdFlEkmLJp0TiecCABeRfBWAEwF03chu6V0aWSwSE8lfwk318U4z2150eUTSoqYhkRhIvhHAoQB2KQhIt1EgEGkhWN1pFG7lpxe6eAEY6VEKBCJNkBwA8EMAS8zsQbhlMi8otlQi6VKOQESkx6lGICLS4xQIRER6nAKBiEiPUyAQEelxCgQiIj1OgUBEpMcpEIiI9Lj/D7v/1qvhPhbPAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "
"
       ]
@@ -730,7 +730,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -747,7 +747,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.005000000000000009\n"
+      "0.0050000000000000044\n"
      ]
     }
    ],
@@ -1322,7 +1322,7 @@
       "270 3344  160  110  270      Ds  7.253775  7.253775\n",
       "\n",
       "[267 rows x 6 columns]\n",
-      "0.009883615646716182\n"
+      "0.009883615646716186\n"
      ]
     }
    ],
@@ -1395,8 +1395,6 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1409,8 +1407,6 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1420,6 +1416,16 @@
      "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
@@ -1434,6 +1440,16 @@
      "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n",
+      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
@@ -1477,8 +1493,6 @@
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n",
       "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
-      "  warnings.warn(\n",
-      "/Users/mhjensen/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
       "  warnings.warn(\n"
      ]
     },
@@ -1512,14 +1526,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_61_10.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_61_12.png"
       }
      },
      "output_type": "display_data"
@@ -3266,15 +3280,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.02224424 -0.14283432  5.20056289]\n",
+      "[2.00240127 0.09885613 4.86421839]\n",
       "Training R2\n",
-      "0.9952679649399017\n",
+      "0.994788006691541\n",
       "Training MSE\n",
-      "0.010488032968482625\n",
+      "0.011239358265567037\n",
       "Test R2\n",
-      "0.9935374069137406\n",
+      "0.9940257354319456\n",
       "Test MSE\n",
-      "0.012025581214177655\n"
+      "0.010708859852916969\n"
      ]
     }
    ],
@@ -3378,19 +3392,13 @@
      "output_type": "stream",
      "text": [
       "Training R2\n",
-      "0.9999874172097344\n",
+      "0.9999940124132914\n",
       "Training MSE\n",
-      "5.124696456785513\n",
+      "2.1043064188572242\n",
       "Test R2\n",
-      "0.9999717079103604\n",
-      "Test MSE\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "14.60488784333949\n"
+      "0.9999292683946882\n",
+      "Test MSE\n",
+      "56.241106846210215\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
index 5eac2c8e5..85d81c6c5 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb
@@ -2336,38 +2336,34 @@
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.09166666666666666\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11944444444444445\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.1361111111111111\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1527777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.16666666666666666\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.1111111111111111\n",
       "\n"
      ]
     },
@@ -2375,10 +2371,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.1111111111111111\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.05\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08888888888888889\n",
@@ -2388,15 +2394,15 @@
       "Accuracy score on test set:  0.08611111111111111\n",
       "\n",
       "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n"
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.08888888888888889\n",
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_11_0.png
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1_61_12.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_61_12.png
index c84cd98b3..4665b32d3 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
index 1b2e7053b..e5bb8edc9 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb
@@ -1470,10 +1470,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.05805002932374468\n",
-      "4.309519578637819\n",
-      "[[ 1.23033954  3.6804336 ]\n",
-      " [ 3.6804336  11.92527504]]\n"
+      "0.0119839668275544\n",
+      "3.9026821944532157\n",
+      "[[ 1.21805483  3.6417002 ]\n",
+      " [ 3.6417002  11.67950066]]\n"
      ]
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@@ -1513,10 +1513,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.09198004868226574\n",
-      "1.9493620821393187\n",
-      "[[1.         0.68904673]\n",
-      " [0.68904673 1.        ]]\n"
+      "0.08283349271892\n",
+      "1.6319972672199556\n",
+      "[[1.         0.65062105]\n",
+      " [0.65062105 1.        ]]\n"
      ]
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@@ -1571,30 +1571,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-1.13352411e+00 -4.86145508e+00]\n",
-      " [ 5.06793578e-01  2.24339370e+00]\n",
-      " [ 4.08359441e-03 -1.44109702e+00]\n",
-      " [-2.02972824e-01 -1.17393096e+00]\n",
-      " [ 9.99703985e-01  5.00745587e+00]\n",
-      " [-1.05430325e-01  3.31446832e-02]\n",
-      " [ 4.31082669e-01  6.38783434e-01]\n",
-      " [ 3.40259257e-02  5.84099240e-01]\n",
-      " [-8.44862840e-01 -2.71546272e+00]\n",
-      " [ 3.11100342e-01  1.68506886e+00]]\n",
+      "[[ 0.30199745  0.72164987]\n",
+      " [ 1.04858244  2.95539603]\n",
+      " [ 0.46840512  1.52940309]\n",
+      " [ 0.24117177  1.78848481]\n",
+      " [-1.38690487 -4.17705119]\n",
+      " [-0.65735283 -2.46141305]\n",
+      " [ 0.94916469  2.81774574]\n",
+      " [-0.52449732 -2.22140879]\n",
+      " [-0.66999836 -2.66511337]\n",
+      " [ 0.22943192  1.71230687]]\n",
       "          0         1\n",
-      "0 -1.133524 -4.861455\n",
-      "1  0.506794  2.243394\n",
-      "2  0.004084 -1.441097\n",
-      "3 -0.202973 -1.173931\n",
-      "4  0.999704  5.007456\n",
-      "5 -0.105430  0.033145\n",
-      "6  0.431083  0.638783\n",
-      "7  0.034026  0.584099\n",
-      "8 -0.844863 -2.715463\n",
-      "9  0.311100  1.685069\n",
+      "0  0.301997  0.721650\n",
+      "1  1.048582  2.955396\n",
+      "2  0.468405  1.529403\n",
+      "3  0.241172  1.788485\n",
+      "4 -1.386905 -4.177051\n",
+      "5 -0.657353 -2.461413\n",
+      "6  0.949165  2.817746\n",
+      "7 -0.524497 -2.221409\n",
+      "8 -0.669998 -2.665113\n",
+      "9  0.229432  1.712307\n",
       "          0         1\n",
-      "0  1.000000  0.958527\n",
-      "1  0.958527  1.000000\n"
+      "0  1.000000  0.975127\n",
+      "1  0.975127  1.000000\n"
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@@ -1636,37 +1636,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.082212  0.080493  0.081616  0.077560  0.073611  0.073557  0.069528   \n",
-      "2   0.0  0.080493  0.080583  0.082413  0.079267  0.075998  0.075624  0.072071   \n",
-      "3   0.0  0.081616  0.082413  0.087107  0.084071  0.080840  0.082136  0.078414   \n",
-      "4   0.0  0.077560  0.079267  0.084071  0.081732  0.079085  0.080069  0.076853   \n",
-      "5   0.0  0.073611  0.075998  0.080840  0.079085  0.076946  0.077662  0.074900   \n",
-      "6   0.0  0.073557  0.075624  0.082136  0.080069  0.077662  0.079815  0.076731   \n",
-      "7   0.0  0.069528  0.072071  0.078414  0.076853  0.074900  0.076731  0.074079   \n",
-      "8   0.0  0.065794  0.068700  0.074858  0.073728  0.072170  0.073713  0.071447   \n",
-      "9   0.0  0.062352  0.065534  0.071499  0.070739  0.069526  0.070812  0.068888   \n",
-      "10  0.0  0.065582  0.068203  0.075503  0.074125  0.072352  0.074961  0.072447   \n",
-      "11  0.0  0.062022  0.064909  0.071917  0.070915  0.069494  0.071782  0.069625   \n",
-      "12  0.0  0.058765  0.061857  0.068581  0.067904  0.066793  0.068790  0.066951   \n",
-      "13  0.0  0.055788  0.059038  0.065492  0.065096  0.064256  0.065991  0.064434   \n",
-      "14  0.0  0.053069  0.056441  0.062636  0.062486  0.061885  0.063380  0.062076   \n",
+      "1   0.0  0.096033  0.089499  0.100065  0.095636  0.091356  0.093135  0.089366   \n",
+      "2   0.0  0.089499  0.084679  0.095430  0.091860  0.088348  0.090343  0.087109   \n",
+      "3   0.0  0.100065  0.095430  0.109996  0.106272  0.102568  0.105983  0.102429   \n",
+      "4   0.0  0.095636  0.091860  0.106272  0.103072  0.099840  0.103254  0.100074   \n",
+      "5   0.0  0.091356  0.088348  0.102568  0.099840  0.097041  0.100443  0.097606   \n",
+      "6   0.0  0.093135  0.090343  0.105983  0.103254  0.100443  0.104606  0.101680   \n",
+      "7   0.0  0.089366  0.087109  0.102429  0.100074  0.097606  0.101680  0.099049   \n",
+      "8   0.0  0.085816  0.084032  0.099028  0.097007  0.094850  0.098834  0.096471   \n",
+      "9   0.0  0.082468  0.081101  0.095771  0.094052  0.092175  0.096070  0.093951   \n",
+      "10  0.0  0.085269  0.083748  0.099374  0.097448  0.095375  0.099827  0.097488   \n",
+      "11  0.0  0.082003  0.080845  0.096087  0.094440  0.092628  0.096955  0.094852   \n",
+      "12  0.0  0.078938  0.078099  0.092968  0.091572  0.089996  0.094201  0.092313   \n",
+      "13  0.0  0.076058  0.075503  0.090008  0.088838  0.087475  0.091561  0.089869   \n",
+      "14  0.0  0.073350  0.073045  0.087198  0.086231  0.085061  0.089033  0.087519   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.065794  0.062352  0.065582  0.062022  0.058765  0.055788  0.053069  \n",
-      "2   0.068700  0.065534  0.068203  0.064909  0.061857  0.059038  0.056441  \n",
-      "3   0.074858  0.071499  0.075503  0.071917  0.068581  0.065492  0.062636  \n",
-      "4   0.073728  0.070739  0.074125  0.070915  0.067904  0.065096  0.062486  \n",
-      "5   0.072170  0.069526  0.072352  0.069494  0.066793  0.064256  0.061885  \n",
-      "6   0.073713  0.070812  0.074961  0.071782  0.068790  0.065991  0.063380  \n",
-      "7   0.071447  0.068888  0.072447  0.069625  0.066951  0.064434  0.062076  \n",
-      "8   0.069161  0.066912  0.069940  0.067442  0.065059  0.062803  0.060678  \n",
-      "9   0.066912  0.064945  0.067492  0.065289  0.063171  0.061154  0.059245  \n",
-      "10  0.069940  0.067492  0.071541  0.068800  0.066196  0.063738  0.061430  \n",
-      "11  0.067442  0.065289  0.068800  0.066372  0.064050  0.061846  0.059767  \n",
-      "12  0.065059  0.063171  0.066196  0.064050  0.061983  0.060011  0.058141  \n",
-      "13  0.062803  0.061154  0.063738  0.061846  0.060011  0.058249  0.056570  \n",
-      "14  0.060678  0.059245  0.061430  0.059767  0.058141  0.056570  0.055065  \n"
+      "1   0.085816  0.082468  0.085269  0.082003  0.078938  0.076058  0.073350  \n",
+      "2   0.084032  0.081101  0.083748  0.080845  0.078099  0.075503  0.073045  \n",
+      "3   0.099028  0.095771  0.099374  0.096087  0.092968  0.090008  0.087198  \n",
+      "4   0.097007  0.094052  0.097448  0.094440  0.091572  0.088838  0.086231  \n",
+      "5   0.094850  0.092175  0.095375  0.092628  0.089996  0.087475  0.085061  \n",
+      "6   0.098834  0.096070  0.099827  0.096955  0.094201  0.091561  0.089033  \n",
+      "7   0.096471  0.093951  0.097488  0.094852  0.092313  0.089869  0.087519  \n",
+      "8   0.094138  0.091843  0.095173  0.092754  0.090414  0.088152  0.085968  \n",
+      "9   0.091843  0.089754  0.092891  0.090673  0.088516  0.086423  0.084394  \n",
+      "10  0.095173  0.092891  0.096568  0.094135  0.091779  0.089500  0.087297  \n",
+      "11  0.092754  0.090673  0.094135  0.091901  0.089727  0.087616  0.085567  \n",
+      "12  0.090414  0.088516  0.091779  0.089727  0.087721  0.085765  0.083859  \n",
+      "13  0.088152  0.086423  0.089500  0.087616  0.085765  0.083951  0.082179  \n",
+      "14  0.085968  0.084394  0.087297  0.085567  0.083859  0.082179  0.080529  \n"
      ]
     }
    ],
@@ -3273,14 +3273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 2.03099776 -0.17917768  5.18029127]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
+      "[ 2.03099776 -0.17917768  5.18029127]\n",
       "Training MSE for OLS\n",
       "0.009163470508352228\n",
       "Test MSE OLS\n",
@@ -3296,7 +3289,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter2_249_2.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter2_249_1.png"
       },
       "needs_background": "light"
      },
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
index 8be9fcbd1..6c3ba2f29 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb
@@ -397,10 +397,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Runtime: 0.135707 sec\n",
+      "Runtime: 0.135976 sec\n",
       "Jackknife Statistics :\n",
       "original           bias      std. error\n",
-      " 100.099        100.089        0.150795\n"
+      "  99.655         99.645        0.148675\n"
      ]
     }
    ],
@@ -774,7 +774,7 @@
      "text": [
       "Bootstrap Statistics :\n",
       "original           bias      std. error\n",
-      " 100.186  15.0063        100.185        0.148455\n"
+      " 100.098   15.248        100.098        0.153966\n"
      ]
     }
    ],
@@ -828,7 +828,7 @@
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -1087,14 +1087,15 @@
       "Error: 0.32149601703519126\n",
       "Bias^2: 0.3123314713548606\n",
       "Var: 0.009164545680330616\n",
-      "0.32149601703519126 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n"
+      "0.32149601703519126 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n",
+      "Polynomial degree:"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 1\n",
+      " 1\n",
       "Error: 0.08426840630693411\n",
       "Bias^2: 0.07968918676726028\n",
       "Var: 0.004579219539673833\n",
@@ -1139,18 +1140,18 @@
       "Error: 0.017355848195591973\n",
       "Bias^2: 0.010331721306655588\n",
       "Var: 0.007024126888936384\n",
-      "0.017355848195591973 >= 0.010331721306655588 + 0.007024126888936384 = 0.017355848195591973\n"
+      "0.017355848195591973 >= 0.010331721306655588 + 0.007024126888936384 = 0.017355848195591973\n",
+      "Polynomial degree: 9\n",
+      "Error: 0.026605727637189085\n",
+      "Bias^2: 0.010018312644140933\n",
+      "Var: 0.016587414993048166\n",
+      "0.026605727637189085 >= 0.010018312644140933 + 0.016587414993048166 = 0.0266057276371891\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Polynomial degree: 9\n",
-      "Error: 0.026605727637189085\n",
-      "Bias^2: 0.010018312644140933\n",
-      "Var: 0.016587414993048166\n",
-      "0.026605727637189085 >= 0.010018312644140933 + 0.016587414993048166 = 0.0266057276371891\n",
       "Polynomial degree: 10\n",
       "Error: 0.021592704588043153\n",
       "Bias^2: 0.010516485576652981\n",
@@ -1165,13 +1166,7 @@
       "Error: 0.1154777721897675\n",
       "Bias^2: 0.01628578269590588\n",
       "Var: 0.09919198949386163\n",
-      "0.1154777721897675 >= 0.01628578269590588 + 0.09919198949386163 = 0.11547777218976751\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "0.1154777721897675 >= 0.01628578269590588 + 0.09919198949386163 = 0.11547777218976751\n",
       "Polynomial degree: 13\n",
       "Error: 0.22842468702166951\n",
       "Bias^2: 0.01975416527163567\n",
@@ -1188,7 +1183,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_62_5.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_62_4.png"
       },
       "needs_background": "light"
      },
@@ -1521,16 +1516,16 @@
       "Mean squared error on test data: 873.95463048\n",
       "Degree of polynomial:  23\n",
       "Mean squared error on training data: 0.00085890\n",
-      "Mean squared error on test data: 5535.20053452\n"
+      "Mean squared error on test data: 5535.20053452\n",
+      "Degree of polynomial:  24\n",
+      "Mean squared error on training data: 0.00084714\n",
+      "Mean squared error on test data: 1289.22422186\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Degree of polynomial:  24\n",
-      "Mean squared error on training data: 0.00084714\n",
-      "Mean squared error on test data: 1289.22422186\n",
       "Degree of polynomial:  25\n",
       "Mean squared error on training data: 0.00079022\n",
       "Mean squared error on test data: 136582.88824397\n",
@@ -2147,42 +2142,6 @@
     "$$"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We assume \n",
-    "that every column of $\\boldsymbol{X}$ is centered, which we can do by subtracting the mean,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "X = X - np.mean(X,axis=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This means that we need to rewrite $X_{ij}$ as $\\tilde{X}_{ij}=X_{ij}-\\mu_j$, where"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\n",
-    "\\mu_j = \\frac{1}{n}\\sum_{i=0}^{n-1}X_{ij}.\n",
-    "$$"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2204,7 +2163,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Assuming that the matrix elements $X_{i1}$ are centered, what we have is"
+    "We obtain then"
    ]
   },
   {
@@ -2212,7 +2171,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1}y_i - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} \\left(X_{i1}-\\mu_{1}\\right),\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",
     "$$"
    ]
   },
@@ -2220,7 +2179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "where"
+    "If we define"
    ]
   },
   {
@@ -2260,7 +2219,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\beta_0 = \\mu_y - \\beta_1\\frac{1}{n}\\sum_{i=0}^{n-1} (X_{i1}-\\mu_{1}),\n",
+    "\\beta_0 = \\mu_y - \\beta_1\\mu_{1}.\n",
     "$$"
    ]
   },
@@ -2268,7 +2227,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "and it is easy to see that the last sum equals zero!  This means that we have"
+    "In the general case withmore parameters than $\\beta_0$ and $\\beta_1$, we have"
    ]
   },
   {
@@ -2276,7 +2235,7 @@
    "metadata": {},
    "source": [
     "$$\n",
-    "\\beta_0 = \\mu_y,\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",
     "$$"
    ]
   },
@@ -2284,26 +2243,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "if the columns of the design matrix are centered. It is straight forward to generalize this results to more values of $\\beta$.\n",
-    "We have thus"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$\n",
-    "\\beta_0 = \\frac{1}{n}\\sum_{i=0}^{n-1} y_i = \\overline{\\boldsymbol{y}},\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "the average value of $\\boldsymbol{y}$.\n",
-    "\n",
-    "Replacing $y_i$ with $y_i - \\beta_0 = y_i - \\overline{\\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)"
+    "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)"
    ]
   },
   {
@@ -2363,7 +2303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2399,7 +2339,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_109_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_103_1.png"
       },
       "needs_background": "light"
      },
@@ -2571,7 +2511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2680,7 +2620,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_117_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_111_1.png"
       },
       "needs_background": "light"
      },
@@ -2767,7 +2707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -2898,7 +2838,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_119_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_113_1.png"
       },
       "needs_background": "light"
      },
@@ -2956,8 +2896,6 @@
     "    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 @ OwnRidgeBeta + intercept_ \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",
@@ -3039,7 +2977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3153,7 +3091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3218,7 +3156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3309,7 +3247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3323,7 +3261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3342,7 +3280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3361,7 +3299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3371,7 +3309,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -3384,7 +3322,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_150_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_144_1.png"
       }
      },
      "output_type": "display_data"
@@ -3451,7 +3389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3560,7 +3498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3593,7 +3531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3612,7 +3550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3631,7 +3569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3641,7 +3579,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      ":7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -3654,7 +3592,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_168_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_162_1.png"
       }
      },
      "output_type": "display_data"
@@ -3689,8 +3627,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "6\n",
-    "0\n",
+    "5\n",
+    "8\n",
     " \n",
     "<\n",
     "<\n",
@@ -3711,7 +3649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3721,7 +3659,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      ":10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -3734,7 +3672,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_171_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_165_1.png"
       }
      },
      "output_type": "display_data"
@@ -3786,7 +3724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3796,7 +3734,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      ":9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
+      ":9: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
       "  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n"
      ]
     },
@@ -3809,7 +3747,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_175_1.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_169_1.png"
       }
      },
      "output_type": "display_data"
@@ -3845,7 +3783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3874,7 +3812,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|██        | 2/10 [00:00<00:03,  2.38it/s]"
+      " 20%|██        | 2/10 [00:00<00:03,  2.25it/s]"
      ]
     },
     {
@@ -3882,7 +3820,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 30%|███       | 3/10 [00:00<00:02,  2.99it/s]"
+      " 30%|███       | 3/10 [00:00<00:02,  2.82it/s]"
      ]
     },
     {
@@ -3890,7 +3828,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 40%|████      | 4/10 [00:00<00:01,  3.72it/s]"
+      " 40%|████      | 4/10 [00:01<00:01,  3.33it/s]"
      ]
     },
     {
@@ -3898,7 +3836,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 50%|█████     | 5/10 [00:01<00:01,  4.40it/s]"
+      " 50%|█████     | 5/10 [00:01<00:01,  3.94it/s]"
      ]
     },
     {
@@ -3906,7 +3844,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 60%|██████    | 6/10 [00:01<00:00,  5.16it/s]"
+      " 60%|██████    | 6/10 [00:01<00:00,  4.61it/s]"
      ]
     },
     {
@@ -3914,7 +3852,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 70%|███████   | 7/10 [00:01<00:00,  5.95it/s]"
+      " 70%|███████   | 7/10 [00:01<00:00,  5.17it/s]"
      ]
     },
     {
@@ -3922,7 +3860,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 80%|████████  | 8/10 [00:01<00:00,  6.55it/s]"
+      " 80%|████████  | 8/10 [00:01<00:00,  5.73it/s]"
      ]
     },
     {
@@ -3930,7 +3868,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 90%|█████████ | 9/10 [00:01<00:00,  6.99it/s]"
+      " 90%|█████████ | 9/10 [00:01<00:00,  6.46it/s]"
      ]
     },
     {
@@ -3938,7 +3876,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 10/10 [00:01<00:00,  7.35it/s]"
+      "100%|██████████| 10/10 [00:01<00:00,  6.87it/s]"
      ]
     },
     {
@@ -3946,7 +3884,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 10/10 [00:01<00:00,  5.87it/s]"
+      "100%|██████████| 10/10 [00:01<00:00,  5.22it/s]"
      ]
     },
     {
@@ -3965,7 +3903,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_177_13.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_171_13.png"
       }
      },
      "output_type": "display_data"
@@ -4030,7 +3968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4045,7 +3983,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_179_0.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_173_0.png"
       }
      },
      "output_type": "display_data"
@@ -4161,7 +4099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4176,7 +4114,7 @@
      },
      "metadata": {
       "filenames": {
-       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_183_0.png"
+       "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_177_0.png"
       }
      },
      "output_type": "display_data"
@@ -4467,7 +4405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -4480,7 +4418,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined"
      ]
     }
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.py b/doc/LectureNotes/_build/jupyter_execute/chapter3.py
index 53e60a46a..279c5c06b 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter3.py
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.py
@@ -1236,17 +1236,6 @@ $$
 \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.
 $$
 
-We assume 
-that every column of $\boldsymbol{X}$ is centered, which we can do by subtracting the mean,
-
-X = X - np.mean(X,axis=0)
-
-This means that we need to rewrite $X_{ij}$ as $\tilde{X}_{ij}=X_{ij}-\mu_j$, where
-
-$$
-\mu_j = \frac{1}{n}\sum_{i=0}^{n-1}X_{ij}.
-$$
-
 Let us special first to the case where we have only two parameters $\beta_0$ and $\beta_1$.
 Our result for $\beta_0$ simplifies then to
 
@@ -1254,13 +1243,13 @@ $$
 n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1.
 $$
 
-Assuming that the matrix elements $X_{i1}$ are centered, what we have is
+We obtain then
 
 $$
-\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} \left(X_{i1}-\mu_{1}\right),
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} X_{i1}.
 $$
 
-where
+If we define
 
 $$
 \mu_1=\frac{1}{n}\sum_{i=0}^{n-1} (X_{i1},
@@ -1275,25 +1264,16 @@ $$
 we have
 
 $$
-\beta_0 = \mu_y - \beta_1\frac{1}{n}\sum_{i=0}^{n-1} (X_{i1}-\mu_{1}),
+\beta_0 = \mu_y - \beta_1\mu_{1}.
 $$
 
-and it is easy to see that the last sum equals zero!  This means that we have
+In the general case withmore parameters than $\beta_0$ and $\beta_1$, we have
 
 $$
-\beta_0 = \mu_y,
+\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1}y_i - \frac{1}{n}\sum_{i=0}^{n-1}\sum_{j=1}^{p-1} X_{ij}\beta_j.
 $$
 
-if the columns of the design matrix are centered. It is straight forward to generalize this results to more values of $\beta$.
-We have thus
-
-$$
-\beta_0 = \frac{1}{n}\sum_{i=0}^{n-1} y_i = \overline{\boldsymbol{y}},
-$$
-
-the average value of $\boldsymbol{y}$.
-
-Replacing $y_i$ with $y_i - \beta_0 = y_i - \overline{\boldsymbol{y}}$ and centering also our design matrix results in a cost function (in vector-matrix disguise)
+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}).
@@ -1567,8 +1547,6 @@ for i in range(nlambdas):
     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 @ OwnRidgeBeta + intercept_ 
-    #Add intercept to prediction
     ypredictOwnRidge = X_test_scaled @ OwnRidgeBeta + y_scaler 
     RegRidge = linear_model.Ridge(lmb)
     RegRidge.fit(X_train,y_train)
@@ -1939,8 +1917,8 @@ involves a new cost function which leads to a new estimate for the
 weights $\boldsymbol{\beta}$. This results in a penalized regression problem. The
 cost function is given by
 
-6
-0
+5
+8
  
 <
 <
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3_62_4.png b/doc/LectureNotes/_build/jupyter_execute/chapter3_62_4.png
new file mode 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
index b6a5760a3..23471fc56 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb
@@ -86,14 +86,14 @@
      "output_type": "stream",
      "text": [
       "2nd degree coefficients:\n",
-      "zero power:  1.055455110765643\n",
-      "first power:  0.022116476923538613\n",
-      "second power:  -0.00019348106804731915\n"
+      "zero power:  -2.575591916051299\n",
+      "first power:  -0.07310737041838755\n",
+      "second power:  0.0002644543695187602\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -108,7 +108,7 @@
     },
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "
"
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png
index c622be8e6..968e34cb6 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png
index 1e05630ce..3fff4c0a7 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
index fd0248617..494a3222f 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb
@@ -295,10 +295,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.1014841451296029\n",
-      "3.744674093727182\n",
-      "[[ 1.04621283  3.2509618 ]\n",
-      " [ 3.2509618  11.28244928]]\n"
+      "-0.13740265686642364\n",
+      "3.647867896223553\n",
+      "[[0.88732076 2.58645106]\n",
+      " [2.58645106 8.40379027]]\n"
      ]
     }
    ],
@@ -340,10 +340,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.08899392007617646\n",
-      "1.960919045835183\n",
-      "[[1.         0.64606848]\n",
-      " [0.64606848 1.        ]]\n"
+      "0.09282061240353208\n",
+      "2.0679953259809554\n",
+      "[[1.         0.65276752]\n",
+      " [0.65276752 1.        ]]\n"
      ]
     }
    ],
@@ -397,30 +397,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[-0.26338419 -0.43128913]\n",
-      " [-0.40052348 -0.40977892]\n",
-      " [-1.21495309 -3.03292103]\n",
-      " [-0.68288031 -2.86961626]\n",
-      " [ 1.847187    7.221629  ]\n",
-      " [-0.01324363  0.06365882]\n",
-      " [-0.08321393 -1.11171059]\n",
-      " [ 0.6497097   1.58382502]\n",
-      " [-0.3196681  -1.46807752]\n",
-      " [ 0.48097003  0.45428062]]\n",
+      "[[ 0.27416797  2.69235507]\n",
+      " [-0.30895532 -1.36796587]\n",
+      " [ 1.03285324  2.17106672]\n",
+      " [-0.35304998 -2.85655181]\n",
+      " [-0.79084976 -1.67951873]\n",
+      " [ 0.19247017  0.868215  ]\n",
+      " [-0.45745139 -2.25170803]\n",
+      " [-0.63165748 -1.85258411]\n",
+      " [-0.19636844  0.14649905]\n",
+      " [ 1.238841    4.13019269]]\n",
       "          0         1\n",
-      "0 -0.263384 -0.431289\n",
-      "1 -0.400523 -0.409779\n",
-      "2 -1.214953 -3.032921\n",
-      "3 -0.682880 -2.869616\n",
-      "4  1.847187  7.221629\n",
-      "5 -0.013244  0.063659\n",
-      "6 -0.083214 -1.111711\n",
-      "7  0.649710  1.583825\n",
-      "8 -0.319668 -1.468078\n",
-      "9  0.480970  0.454281\n",
+      "0  0.274168  2.692355\n",
+      "1 -0.308955 -1.367966\n",
+      "2  1.032853  2.171067\n",
+      "3 -0.353050 -2.856552\n",
+      "4 -0.790850 -1.679519\n",
+      "5  0.192470  0.868215\n",
+      "6 -0.457451 -2.251708\n",
+      "7 -0.631657 -1.852584\n",
+      "8 -0.196368  0.146499\n",
+      "9  1.238841  4.130193\n",
       "          0         1\n",
-      "0  1.000000  0.959043\n",
-      "1  0.959043  1.000000\n"
+      "0  1.000000  0.899606\n",
+      "1  0.899606  1.000000\n"
      ]
     }
    ],
@@ -461,37 +461,37 @@
      "text": [
       "     0         1         2         3         4         5         6         7   \\\n",
       "0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-      "1   0.0  0.077527  0.078731  0.077076  0.078129  0.079210  0.068328  0.069362   \n",
-      "2   0.0  0.078731  0.081031  0.079460  0.081106  0.082747  0.071284  0.072687   \n",
-      "3   0.0  0.077076  0.079460  0.081529  0.083368  0.085189  0.075205  0.076784   \n",
-      "4   0.0  0.078129  0.081106  0.083368  0.085569  0.087738  0.077404  0.079228   \n",
-      "5   0.0  0.079210  0.082747  0.085189  0.087738  0.090239  0.079562  0.081623   \n",
-      "6   0.0  0.068328  0.071284  0.075205  0.077404  0.079562  0.071309  0.073110   \n",
-      "7   0.0  0.069362  0.072687  0.076784  0.079228  0.081623  0.073110  0.075086   \n",
-      "8   0.0  0.070455  0.074134  0.078402  0.081085  0.083711  0.074934  0.077084   \n",
-      "9   0.0  0.071609  0.075631  0.080066  0.082982  0.085835  0.076789  0.079111   \n",
-      "10  0.0  0.059604  0.062733  0.067433  0.069731  0.071979  0.065235  0.067080   \n",
-      "11  0.0  0.060603  0.063986  0.068834  0.071311  0.073734  0.066778  0.068758   \n",
-      "12  0.0  0.061660  0.065292  0.070286  0.072940  0.075537  0.068363  0.070479   \n",
-      "13  0.0  0.062777  0.066653  0.071794  0.074624  0.077395  0.069997  0.072248   \n",
-      "14  0.0  0.063954  0.068071  0.073361  0.076367  0.079312  0.071682  0.074070   \n",
+      "1   0.0  0.090368  0.089828  0.084745  0.089694  0.094385  0.072722  0.077607   \n",
+      "2   0.0  0.089828  0.091016  0.082203  0.087699  0.093293  0.069684  0.074647   \n",
+      "3   0.0  0.084745  0.082203  0.084433  0.088351  0.091653  0.075222  0.079774   \n",
+      "4   0.0  0.089694  0.087699  0.088351  0.092763  0.096661  0.078226  0.083104   \n",
+      "5   0.0  0.094385  0.093293  0.091653  0.096661  0.101332  0.080548  0.085760   \n",
+      "6   0.0  0.072722  0.069684  0.075222  0.078226  0.080548  0.068780  0.072668   \n",
+      "7   0.0  0.077607  0.074647  0.079774  0.083104  0.085760  0.072668  0.076854   \n",
+      "8   0.0  0.082726  0.079963  0.084408  0.088121  0.091191  0.076564  0.081069   \n",
+      "9   0.0  0.088020  0.085632  0.089011  0.093180  0.096774  0.080344  0.085192   \n",
+      "10  0.0  0.062054  0.059107  0.065855  0.068244  0.069993  0.061366  0.064683   \n",
+      "11  0.0  0.066283  0.063260  0.070074  0.072690  0.074644  0.065135  0.068701   \n",
+      "12  0.0  0.070793  0.067729  0.074516  0.077390  0.079587  0.069075  0.072910   \n",
+      "13  0.0  0.075587  0.072539  0.079162  0.082333  0.084822  0.073160  0.077286   \n",
+      "14  0.0  0.080656  0.077711  0.083974  0.087490  0.090338  0.077343  0.081784   \n",
       "\n",
       "          8         9         10        11        12        13        14  \n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
-      "1   0.070455  0.071609  0.059604  0.060603  0.061660  0.062777  0.063954  \n",
-      "2   0.074134  0.075631  0.062733  0.063986  0.065292  0.066653  0.068071  \n",
-      "3   0.078402  0.080066  0.067433  0.068834  0.070286  0.071794  0.073361  \n",
-      "4   0.081085  0.082982  0.069731  0.071311  0.072940  0.074624  0.076367  \n",
-      "5   0.083711  0.085835  0.071979  0.073734  0.075537  0.077395  0.079312  \n",
-      "6   0.074934  0.076789  0.065235  0.066778  0.068363  0.069997  0.071682  \n",
-      "7   0.077084  0.079111  0.067080  0.068758  0.070479  0.072248  0.074070  \n",
-      "8   0.079253  0.081450  0.068941  0.070753  0.072608  0.074513  0.076473  \n",
-      "9   0.081450  0.083817  0.070824  0.072770  0.074761  0.076803  0.078901  \n",
-      "10  0.068941  0.070824  0.060591  0.062149  0.063743  0.065378  0.067060  \n",
-      "11  0.070753  0.072770  0.062149  0.063814  0.065517  0.067262  0.069055  \n",
-      "12  0.072608  0.074761  0.063743  0.065517  0.067329  0.069185  0.071092  \n",
-      "13  0.074513  0.076803  0.065378  0.067262  0.069185  0.071155  0.073178  \n",
-      "14  0.076473  0.078901  0.067060  0.069055  0.071092  0.073178  0.075318  \n"
+      "1   0.082726  0.088020  0.062054  0.066283  0.070793  0.075587  0.080656  \n",
+      "2   0.079963  0.085632  0.059107  0.063260  0.067729  0.072539  0.077711  \n",
+      "3   0.084408  0.089011  0.065855  0.070074  0.074516  0.079162  0.083974  \n",
+      "4   0.088121  0.093180  0.068244  0.072690  0.077390  0.082333  0.087490  \n",
+      "5   0.091191  0.096774  0.069993  0.074644  0.079587  0.084822  0.090338  \n",
+      "6   0.076564  0.080344  0.061366  0.065135  0.069075  0.073160  0.077343  \n",
+      "7   0.081069  0.085192  0.064683  0.068701  0.072910  0.077286  0.081784  \n",
+      "8   0.085636  0.090149  0.067977  0.072252  0.076743  0.081428  0.086266  \n",
+      "9   0.090149  0.095107  0.071137  0.075674  0.080455  0.085465  0.090671  \n",
+      "10  0.067977  0.071137  0.055542  0.058856  0.062305  0.065864  0.069488  \n",
+      "11  0.072252  0.075674  0.058856  0.062396  0.066086  0.069899  0.073789  \n",
+      "12  0.076743  0.080455  0.062305  0.066086  0.070032  0.074118  0.078298  \n",
+      "13  0.081428  0.085465  0.065864  0.069899  0.074118  0.078497  0.082989  \n",
+      "14  0.086266  0.090671  0.069488  0.073789  0.078298  0.082989  0.087822  \n"
      ]
     }
    ],
@@ -916,10 +916,10 @@
      "output_type": "stream",
      "text": [
       "          0         1\n",
-      "0  3.982025  2.012742\n",
-      "1  2.012742  2.042269\n",
-      "[[3.98202489 2.01274179]\n",
-      " [2.01274179 2.04226949]]\n"
+      "0  3.900266  1.942180\n",
+      "1  1.942180  1.965724\n",
+      "[[3.90026646 1.9421795 ]\n",
+      " [1.9421795  1.96572363]]\n"
      ]
     }
    ],
@@ -949,13 +949,13 @@
      "output_type": "stream",
      "text": [
       "Centered covariance using own code\n",
-      "[[3.98202489 2.01274179]\n",
-      " [2.01274179 2.04226949]]\n"
+      "[[3.90026646 1.9421795 ]\n",
+      " [1.9421795  1.96572363]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1045,12 +1045,12 @@
      "output_type": "stream",
      "text": [
       "Eigenvalues of Covariance matrix\n",
-      "5.246379112732898\n",
-      "0.7779152676639822\n",
+      "5.102712819944882\n",
+      "0.763277270613554\n",
       "First eigenvector\n",
-      "[0.84678774 0.53193095]\n",
+      "[0.85023678 0.52640043]\n",
       "Second eigenvector\n",
-      "[-0.53193095  0.84678774]\n"
+      "[-0.52640043  0.85023678]\n"
      ]
     },
     {
@@ -1058,7 +1058,7 @@
      "output_type": "stream",
      "text": [
       "Eigenvector of largest eigenvalue\n",
-      "[0.84678774 0.53193095]\n"
+      "[-0.85023678 -0.52640043]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png
index c2862439b..6b5a8cdd0 100644
Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
index adfbe7aa3..b18caef4c 100644
--- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb
@@ -747,7 +747,7 @@
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
      "execution_count": 1,
@@ -903,7 +903,7 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "[]"
       ]
      },
      "execution_count": 5,
@@ -1453,16 +1453,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.30875823 4.41879302]\n",
-      "[[3.96285336]\n",
-      " [3.28329188]]\n",
-      "[[3.96285336]\n",
-      " [3.28329188]]\n"
+      "[0.30862336 4.94226154]\n",
+      "[[4.09553421]\n",
+      " [2.99517651]]\n",
+      "[[4.09553421]\n",
+      " [2.99517651]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1544,9 +1544,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[3.75582035]\n",
-      " [3.15167261]]\n",
-      "[3.7821634] [3.18303503]\n"
+      "[[4.1224808 ]\n",
+      " [2.97290346]]\n",
+      "[4.09600813] [2.93854356]\n"
      ]
     }
    ],
@@ -1631,15 +1631,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[4.05636001]\n",
-      " [2.75240368]]\n",
-      "[[3.99394477]\n",
-      " [2.80463702]]\n"
+      "[[3.95338481]\n",
+      " [2.95611545]]\n",
+      "[[3.99545147]\n",
+      " [2.91812296]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "
"
       ]
@@ -1949,34 +1949,28 @@
      "output_type": "stream",
      "text": [
       "Own inversion\n",
-      "[[3.88168518]\n",
-      " [3.04348504]]\n",
+      "[[3.87218485]\n",
+      " [3.01403105]]\n",
       "sgdreg from scikit\n",
-      "[3.92115534] [3.13964362]\n",
+      "[3.9030809] [3.07154525]\n",
       "theta from own gd\n",
-      "[[3.88168518]\n",
-      " [3.04348504]]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "[[3.87218485]\n",
+      " [3.01403105]]\n",
       "theta from own sdg\n",
-      "[[3.92555588]\n",
-      " [3.0163616 ]]\n"
+      "[[3.86110774]\n",
+      " [3.00070278]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_127_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_127_1.png
index c1020ec88..2999003a5 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_141_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_141_1.png
new file mode 100644
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diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
index 5aa7b0a51..77ba1a1a3 100644
--- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb
@@ -159,8 +159,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.98226332  0.21126669  0.23494196  1.04532409 -0.994015   -0.37831577\n",
-      " -0.22534931  0.39445208 -1.97921062  1.042552  ]\n"
+      "[-0.62370636  0.34673123  0.80408254  2.08638654  1.16894795  1.8464107\n",
+      "  0.13620973 -1.33881021 -0.05404886 -0.99995891]\n"
      ]
     }
    ],
diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
index 30490d3a0..f3dcb3d53 100644
--- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb
@@ -1089,27 +1089,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9524796542295857\n",
-      "[[12.32978852 17.93351637  8.83074569  6.12730791  9.17878199 10.22862364\n",
-      "  14.56202678 10.49110643  8.12113897 14.46581332]\n",
-      " [17.93351637 26.08406534 12.84420428  8.91208933 13.35041853 14.87739949\n",
-      "  21.18027777 15.25917728 11.81209057 21.04033655]\n",
-      " [ 8.83074569 12.84420428  6.32468833  4.38845304  6.57395619  7.32586565\n",
-      "  10.4295021   7.51385904  5.81645929 10.36059285]\n",
-      " [ 6.12730791  8.91208933  4.38845304  3.04497536  4.56141023  5.0831307\n",
-      "   7.23662224  5.21357193  4.03581286  7.18880881]\n",
-      " [ 9.17878199 13.35041853  6.57395619  4.56141023  6.83304817  7.61459179\n",
-      "  10.8405484   7.80999435  6.04569689 10.7689233 ]\n",
-      " [10.22862364 14.87739949  7.32586565  5.0831307   7.61459179  8.48552604\n",
-      "  12.08045792  8.70327816  6.73718563 12.00064055]\n",
-      " [14.56202678 21.18027777 10.4295021   7.23662224 10.8405484  12.08045792\n",
-      "  17.19839911 12.39046173  9.59142511 17.08476676]\n",
-      " [10.49110643 15.25917728  7.51385904  5.21357193  7.80999435  8.70327816\n",
-      "  12.39046173  8.92661816  6.91007256 12.30859612]\n",
-      " [ 8.12113897 11.81209057  5.81645929  4.03581286  6.04569689  6.73718563\n",
-      "   9.59142511  6.91007256  5.3490697   9.52805315]\n",
-      " [14.46581332 21.04033655 10.36059285  7.18880881 10.7689233  12.00064055\n",
-      "  17.08476676 12.30859612  9.52805315 16.97188519]]\n"
+      "2.4890704720929593\n",
+      "[[11.60990408  7.82544198 14.11622885  1.78979284  6.76376813 14.09060397\n",
+      "  14.04624055  3.77253612  5.11082097 14.99821162]\n",
+      " [ 7.82544198  5.27459502  9.51478402  1.20637689  4.55899331  9.49751204\n",
+      "   9.4676097   2.54280848  3.44485473 10.10926826]\n",
+      " [14.11622885  9.51478402 17.16361442  2.17617003  8.22391797 17.13245767\n",
+      "  17.07851716  4.58694429  6.21413561 18.23599801]\n",
+      " [ 1.78979284  1.20637689  2.17617003  0.27591601  1.04270833  2.17221967\n",
+      "   2.16538057  0.58157742  0.78788857  2.31213725]\n",
+      " [ 6.76376813  4.55899331  8.22391797  1.04270833  3.94047694  8.20898927\n",
+      "   8.18314376  2.19782691  2.97749299  8.73774883]\n",
+      " [14.09060397  9.49751204 17.13245767  2.17221967  8.20898927 17.10135749\n",
+      "  17.04751489  4.57861771  6.20285522 18.20289459]\n",
+      " [14.04624055  9.4676097  17.07851716  2.16538057  8.18314376 17.04751489\n",
+      "  16.99384182  4.56420221  6.18332591 18.14558387]\n",
+      " [ 3.77253612  2.54280848  4.58694429  0.58157742  2.19782691  4.57861771\n",
+      "   4.56420221  1.2258524   1.66071628  4.87353683]\n",
+      " [ 5.11082097  3.44485473  6.21413561  0.78788857  2.97749299  6.20285522\n",
+      "   6.18332591  1.66071628  2.24984554  6.60239515]\n",
+      " [14.99821162 10.10926826 18.23599801  2.31213725  8.73774883 18.20289459\n",
+      "  18.14558387  4.87353683  6.60239515 19.37538418]]\n"
      ]
     }
    ],
@@ -1669,15 +1669,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.0553457008138535\n",
-      "4.04568240116212\n",
-      "0.3078370846883027\n",
-      "0.8876822883855056 9.132825151794417 26.525617578564635\n",
-      "2.6555465443226955 3.4427165765504326 10.884978392264447\n",
-      "[[ 0.88768229  2.65554654  3.44271658]\n",
-      " [ 2.65554654  9.13282515 10.88497839]\n",
-      " [ 3.44271658 10.88497839 26.52561758]]\n",
-      "[32.33737942  0.10238075  4.10636484]\n"
+      "-0.000171990965637209\n",
+      "3.967581388294481\n",
+      "-0.025521898567525465\n",
+      "0.853884005749657 8.208733310166176 9.244798847617261\n",
+      "2.497355838775297 2.088848443911507 6.174325810031482\n",
+      "[[0.85388401 2.49735584 2.08884844]\n",
+      " [2.49735584 8.20873331 6.17432581]\n",
+      " [2.08884844 6.17432581 9.24479885]]\n",
+      "[15.62869983  0.07851446  2.60020187]\n"
      ]
     }
    ],
@@ -2293,7 +2293,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "
"
       ]
@@ -2400,12 +2400,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-0.00532744652682568 1.0584909962471034\n"
+      "0.005481494389472717 1.029117514654528\n"
      ]
     },
     {
      "data": {
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\n",
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\n",
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