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update with video

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Morten Hjorth-Jensen
2025-08-25 16:21:07 +02:00
parent 9ad479c2cf
commit 8854d3bb29
7 changed files with 435 additions and 1087 deletions
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doc/pub/week35/html/._week35-bs001.html
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@@ -333,9 +333,9 @@ MathJax.Hub.Config({
<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
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@@ -207,9 +207,9 @@ MathJax.Hub.Config({
<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
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@@ -292,9 +292,9 @@ MathJax.Hub.Config({
<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
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@@ -369,9 +369,9 @@ MathJax.Hub.Config({
<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/ipynb/ipynb-week35-src.tar.gz
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doc/pub/week35/ipynb/week35.ipynb
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doc/src/week35/week35.do.txt
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@@ -17,8 +17,8 @@ o Introduction of Ridge and Lasso regression
=== Reading recommendations: ===
o These lecture notes
o "Video of lecture":"https://youtu.be/2mvizAQFST8"
# o "Video for exercises week 35":"https://youtu.be/yiY0OltU1s8"
o Video of lecture at URL:"https://youtu.be/2mvizAQFST8"
o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek35.pdf"
o Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra
o Raschka et al on preprocessing of data, relevant for exercise 3 this week, see chapter 4.
o 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.