update with video

Author: mhjensen

doc/pub/week35/html/._week35-bs001.html

@@ -333,9 +333,9 @@ <h2 id="plans-for-week-35" class="anchor">Plans for week 35 </h2>
 <h3 id="reading-recommendations" class="anchor">Reading recommendations: </h3>
 
 <ol>
-<li> These lecture notes
-<!-- o <a href="https://youtu.be/VKakN-e4aUA" target="_self">Video of lecture</a> -->
-<!-- o <a href="https://youtu.be/yiY0OltU1s8" target="_self">Video for exercises week 35</a> --></li>
+<li> These lecture notes</li>
+<li> Video of lecture at <a href="https://youtu.be/2mvizAQFST8" target="_self"><tt>https://youtu.be/2mvizAQFST8</tt></a></li>
+<li> Whiteboard notes  at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf</tt></a></li>
 <li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra</li>
 <li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
 <li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>

doc/pub/week35/html/week35-reveal.html

@@ -207,9 +207,9 @@ <h2 id="plans-for-week-35">Plans for week 35 </h2>
 <h3 id="reading-recommendations">Reading recommendations: </h3>
 
 <ol>
-<p><li> These lecture notes
-<!-- o <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a> -->
-<!-- o <a href="https://youtu.be/yiY0OltU1s8" target="_blank">Video for exercises week 35</a> --></li>
+<p><li> These lecture notes</li>
+<p><li> Video of lecture at <a href="https://youtu.be/2mvizAQFST8" target="_blank"><tt>https://youtu.be/2mvizAQFST8</tt></a></li>
+<p><li> Whiteboard notes  at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf</tt></a></li>
 <p><li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra</li>
 <p><li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
 <p><li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>

doc/pub/week35/html/week35-solarized.html

@@ -292,9 +292,9 @@ <h2 id="plans-for-week-35">Plans for week 35 </h2>
 <h3 id="reading-recommendations">Reading recommendations: </h3>
 
 <ol>
-<li> These lecture notes
-<!-- o <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a> -->
-<!-- o <a href="https://youtu.be/yiY0OltU1s8" target="_blank">Video for exercises week 35</a> --></li>
+<li> These lecture notes</li>
+<li> Video of lecture at <a href="https://youtu.be/2mvizAQFST8" target="_blank"><tt>https://youtu.be/2mvizAQFST8</tt></a></li>
+<li> Whiteboard notes  at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf</tt></a></li>
 <li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra</li>
 <li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
 <li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>

doc/pub/week35/html/week35.html

@@ -369,9 +369,9 @@ <h2 id="plans-for-week-35">Plans for week 35 </h2>
 <h3 id="reading-recommendations">Reading recommendations: </h3>
 
 <ol>
-<li> These lecture notes
-<!-- o <a href="https://youtu.be/VKakN-e4aUA" target="_blank">Video of lecture</a> -->
-<!-- o <a href="https://youtu.be/yiY0OltU1s8" target="_blank">Video for exercises week 35</a> --></li>
+<li> These lecture notes</li>
+<li> Video of lecture at <a href="https://youtu.be/2mvizAQFST8" target="_blank"><tt>https://youtu.be/2mvizAQFST8</tt></a></li>
+<li> Whiteboard notes  at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf</tt></a></li>
 <li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra</li>
 <li> Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.</li>
 <li> For exercise 1 of week 35, the book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth on the Mathematics of Machine Learning, may be very relevant. In particular chapter 5 at URL"https://mml-book.github.io/" (section 5.5 on derivatives) is very useful for exercise 1 this coming week.</li>