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index 6d1d07448..120f7df34 100644
Binary files a/doc/LectureNotes/_build/.doctrees/schedule.doctree and b/doc/LectureNotes/_build/.doctrees/schedule.doctree differ
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--- a/doc/LectureNotes/_build/html/_sources/schedule.md
+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -77,8 +77,8 @@ For the reading assignments we use the following abbreviations:
 
 ###  Week 38 September 19-23
 - Lab Wednesday: Work on  Project 1
-- Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
-  - Video of Lecture at 
+- Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories and gradient optmization
+  - Video of Lecture at https://youtu.be/sdt_BFla8uA
 - Lecture Friday: Logistic Regression and gradient optimization
   - Video of Lecture at 
 - Reading recommendations:
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 095af1729..b2f9beaa7 100644
--- a/doc/LectureNotes/_build/html/schedule.html
+++ b/doc/LectureNotes/_build/html/schedule.html
@@ -500,9 +500,9 @@ const thebe_selector_output = ".output, .cell_output"
 <h3>Week 38 September 19-23<a class="headerlink" href="#week-38-september-19-23" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Lab Wednesday: Work on  Project 1</p></li>
-<li><p>Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories</p>
+<li><p>Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories and gradient optmization</p>
 <ul>
-<li><p>Video of Lecture at</p></li>
+<li><p>Video of Lecture at <a class="reference external" href="https://youtu.be/sdt_BFla8uA">https://youtu.be/sdt_BFla8uA</a></p></li>
 </ul>
 </li>
 <li><p>Lecture Friday: Logistic Regression and gradient optimization</p>
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index 65fb0847b..fd5ae7d3a 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
+++ b/doc/LectureNotes/_build/html/searchindex.js
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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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\ No newline at end of file
diff --git a/doc/LectureNotes/schedule.md b/doc/LectureNotes/schedule.md
index 455d2a23a..50a40f515 100644
--- a/doc/LectureNotes/schedule.md
+++ b/doc/LectureNotes/schedule.md
@@ -77,8 +77,8 @@ For the reading assignments we use the following abbreviations:
 
 ###  Week 38 September 19-23
 - Lab Wednesday: Work on  Project 1
-- Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories
-  - Video of Lecture at 
+- Lecture Thursday:  Classification problems and Logistic Regression, from binary cases to several categories and gradient optmization
+  - Video of Lecture at https://youtu.be/sdt_BFla8uA
 - Lecture Friday: Logistic Regression and gradient optimization
   - Video of Lecture at 
 - Reading recommendations: