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Morten Hjorth-Jensen
2024-08-25 21:33:20 +02:00
parent e831542c43
commit 053f351848
9 changed files with 436 additions and 456 deletions
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doc/pub/week35/html/._week35-bs000.html
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@@ -367,18 +367,15 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo</b>
</center>
<br>
<center>
<h4>August 26-30</h4>
<h4>August 26-30, 2024</h4>
</center> <!-- date -->
<br>
doc/pub/week35/html/._week35-bs001.html
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@@ -374,7 +374,7 @@ MathJax.Hub.Config({
<h3 id="reading-recommendations" class="anchor">Reading recommendations: </h3>
<ol>
<li> See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a></li>
<li> These lecture notes</li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</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-bs.html
+4 -7
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@@ -367,18 +367,15 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo</b>
</center>
<br>
<center>
<h4>August 26-30</h4>
<h4>August 26-30, 2024</h4>
</center> <!-- date -->
<br>
doc/pub/week35/html/week35-reveal.html
+5 -8
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@@ -173,18 +173,15 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo</b>
</center>
<br>
<center>
<h4>August 26-30</h4>
<h4>August 26-30, 2024</h4>
</center> <!-- date -->
<br>
@@ -210,7 +207,7 @@ MathJax.Hub.Config({
<h3 id="reading-recommendations">Reading recommendations: </h3>
<ol>
<p><li> See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a></li>
<p><li> These lecture notes</li>
<p><li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</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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@@ -296,18 +296,15 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo</b>
</center>
<br>
<center>
<h4>August 26-30</h4>
<h4>August 26-30, 2024</h4>
</center> <!-- date -->
<br>
@@ -326,7 +323,7 @@ MathJax.Hub.Config({
<h3 id="reading-recommendations">Reading recommendations: </h3>
<ol>
<li> See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a></li>
<li> These lecture notes</li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</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
+5 -8
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@@ -373,18 +373,15 @@ MathJax.Hub.Config({
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
<b>Morten Hjorth-Jensen</b>
</center>
<!-- institution(s) -->
<!-- institution -->
<center>
[1] <b>Department of Physics, University of Oslo</b>
</center>
<center>
[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b>
<b>Department of Physics, University of Oslo</b>
</center>
<br>
<center>
<h4>August 26-30</h4>
<h4>August 26-30, 2024</h4>
</center> <!-- date -->
<br>
@@ -403,7 +400,7 @@ MathJax.Hub.Config({
<h3 id="reading-recommendations">Reading recommendations: </h3>
<ol>
<li> See lecture notes for week 35 at <a href="https://compphysics.github.io/MachineLearning/doc/web/course.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/web/course.html</tt></a></li>
<li> These lecture notes</li>
<li> Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)</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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@@ -1,6 +1,6 @@
TITLE: Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
DATE: August 26-30
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo
DATE: August 26-30, 2024
!split
@@ -16,7 +16,7 @@ o Monday: Ridge and Lasso regression and Singular Value Decomposition
=== Reading recommendations: ===
o See lecture notes for week 35 at URL:"https://compphysics.github.io/MachineLearning/doc/web/course.html"
o These lecture notes
o Goodfellow, Bengio and Courville, Deep Learning, chapter 2 on linear algebra and sections 3.1-3.10 on elements of statistics (background)
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.
@@ -47,11 +47,6 @@ Similarly, "Mehta et al's article":"https://arxiv.org/abs/1803.08823" is also re
!split
===== The equations for ordinary least squares =====