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+++ b/doc/LectureNotes/_build/html/_sources/schedule.md
@@ -89,16 +89,13 @@ For the reading assignments we use the following abbreviations:
 
 ###  Week 39 September 26-30
 - Lab Wednesday: Work on  Project 1
-- Lecture Thursday: Gradient Optimization methods
-  - Video of Lecture at 
-- Lecture Friday:  Gradient methods
-  - Video of Lecture at 
-- Reading recommendations:
+- Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent
+* Friday: Stochastic Gradient descent with examples and automatic differentiation
+
+* Reading recommendations:
   - See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
   - For a good discussion on gradient methods, see Goodfellow et al section 4.3-4.5 and chapter 8. We will come back to the latter chapter in our discussion of Neural networks as well.
 
-
-
 ###  Week 40 October 3-7
 - Lab Wednesday: Wrap up project 1
 - Lecture Thursday: Stochastic gradient descent, automatic differentiation and start discussion of  feed-forward Neural Network code for regression and classification
diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html
index 28842c1d4..1326aed3b 100644
--- a/doc/LectureNotes/_build/html/schedule.html
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@@ -524,16 +524,10 @@ const thebe_selector_output = ".output, .cell_output"
 <h3>Week 39 September 26-30<a class="headerlink" href="#week-39-september-26-30" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Lab Wednesday: Work on  Project 1</p></li>
-<li><p>Lecture Thursday: Gradient Optimization methods</p>
-<ul>
-<li><p>Video of Lecture at</p></li>
+<li><p>Lecture Thursday: * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent</p></li>
 </ul>
-</li>
-<li><p>Lecture Friday:  Gradient methods</p>
-<ul>
-<li><p>Video of Lecture at</p></li>
-</ul>
-</li>
+<ul class="simple">
+<li><p>Friday: Stochastic Gradient descent with examples and automatic differentiation</p></li>
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 39 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>
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index 49efa771c..ffe1988ec 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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