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index fb3928933..3cbb2329d 100644
--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -40,7 +40,7 @@ For the reading assignments we use the following abbreviations:
 - Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition
 - Video of lecture Thursday at https://youtu.be/jYdg2xzKa5E
 - Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition
-- Video of lecture Friday at 
+- Video of lecture Friday at https://youtu.be/07e-SUYRzM0
 
 - Reading recommendations:
   - See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 48b992c41..caea6725f 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -537,7 +537,7 @@ const thebe_selector_output = ".output, .cell_output"
 <li><p>Thursday: Review of  ordinary Least Squares with applications, reminder on statistics  and start discussion of Ridge Regression and Singular Value Decomposition</p></li>
 <li><p>Video of lecture Thursday at <a class="reference external" href="https://youtu.be/jYdg2xzKa5E">https://youtu.be/jYdg2xzKa5E</a></p></li>
 <li><p>Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition</p></li>
-<li><p>Video of lecture Friday at</p></li>
+<li><p>Video of lecture Friday at <a class="reference external" href="https://youtu.be/07e-SUYRzM0">https://youtu.be/07e-SUYRzM0</a></p></li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 35 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 9c0a6c2c9..ce1ccc582 100644
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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and 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class=\"section-number\">3. </span>Linear Regression","<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network","<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning","<span class=\"section-number\">16. </span>Convolutional Neural Networks","<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view","<span class=\"section-number\">4. </span>Ridge and Lasso Regression","<span class=\"section-number\">5. </span>Resampling Methods","<span class=\"section-number\">6. </span>Logistic Regression","<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims","<span class=\"section-number\">9. </span>Decision trees, overarching aims","<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)","<span class=\"section-number\">13. </span>Neural networks","<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm","<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis","Teachers and 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