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Morten Hjorth-Jensen
2024-06-22 11:44:06 +02:00
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doc/pub/week34/html/._week34-bs001.html
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@@ -369,7 +369,7 @@ MathJax.Hub.Config({
<li> Tuesdays 815am-12pm and 1215pm-4pm</li>
<li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
</ul>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics as well as solving specific problems. These lectures will also be recorded.</li>
<ul>
<li> <a href="https://youtu.be/" target="_self">Link to recording of lecture TBA</a></li>
</ul>
doc/pub/week34/html/._week34-bs007.html
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@@ -362,9 +362,9 @@ MathJax.Hub.Config({
<h2 id="deadlines-for-projects-tentative" class="anchor">Deadlines for projects (tentative) </h2>
<ol>
<li> Project 1: October 15 (available September 4) graded with feedback)</li>
<li> Project 2: November 13 (available October 8, graded with feedback)</li>
<li> Project 3: December 11 (available November 12, graded with feedback)</li>
<li> Project 1: October 7 (available September 2) graded with feedback)</li>
<li> Project 2: November 4 (available October 8, graded with feedback)</li>
<li> Project 3: December 9 (available November 5, graded with feedback)</li>
</ol>
<div class="panel panel-default">
<div class="panel-body">
doc/pub/week34/html/._week34-bs009.html
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@@ -366,7 +366,7 @@ MathJax.Hub.Config({
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</tt></a>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.</p>
<p>In addition to the lecture notes, we recommend the books of Rasckha et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. </p>
</div>
</div>
doc/pub/week34/html/._week34-bs010.html
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@@ -365,11 +365,23 @@ MathJax.Hub.Config({
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_self">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Goodfellow, Bengio, and Courville (GBC), Deep Learning <a href="https://www.deeplearningbook.org/" target="_self"><tt>https://www.deeplearningbook.org/</tt></a></li>
<li> Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at <a href="https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312" target="_self"><tt>https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312</tt></a>, see also <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
</div>
<div class="panel panel-default">
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ul>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_self">Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer</a>.</li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_self">Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_self">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
<li> David Foster, Generative Deep Learning, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/" target="_self"><tt>https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/</tt></a></li>
<li> Babcock and Gavras, Generative AI with Python and TensorFlow, <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_self"><tt>https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2</tt></a></li>
</ul>
</div>
</div>
doc/pub/week34/html/._week34-bs011.html
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@@ -364,7 +364,7 @@ MathJax.Hub.Config({
<div class="panel panel-default">
<div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
<p>This week: Refresh linear algebra, GBC chapter 2. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
</div>
</div>
doc/pub/week34/html/week34-reveal.html
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@@ -208,7 +208,7 @@ MathJax.Hub.Config({
<p><li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
</ul>
<p>
<p><li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
<p><li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics as well as solving specific problems. These lectures will also be recorded.</li>
<ul>
<p><li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
@@ -319,9 +319,9 @@ MathJax.Hub.Config({
<h2 id="deadlines-for-projects-tentative">Deadlines for projects (tentative) </h2>
<ol>
<p><li> Project 1: October 15 (available September 4) graded with feedback)</li>
<p><li> Project 2: November 13 (available October 8, graded with feedback)</li>
<p><li> Project 3: December 11 (available November 12, graded with feedback)</li>
<p><li> Project 1: October 7 (available September 2) graded with feedback)</li>
<p><li> Project 2: November 4 (available October 8, graded with feedback)</li>
<p><li> Project 3: December 9 (available November 5, graded with feedback)</li>
</ol>
<p>
<div class="alert alert-block alert-block alert-text-normal">
@@ -358,7 +358,7 @@ MathJax.Hub.Config({
<p>
<p>The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</tt></a>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.</p>
<p>In addition to the lecture notes, we recommend the books of Rasckha et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. </p>
</div>
</section>
@@ -366,14 +366,25 @@ MathJax.Hub.Config({
<h2 id="textbooks">Textbooks </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<b>Main texts</b>
<p>
<ul>
<p><li> Goodfellow, Bengio, and Courville (GBC), Deep Learning <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a></li>
<p><li> Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at <a href="https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312" target="_blank"><tt>https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312</tt></a>, see also <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
<div class="alert alert-block alert-block alert-text-normal">
<b>Other texts</b>
<p>
<ul>
<p><li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<p><li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<p><li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer</a>.</li>
<p><li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<p><li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
<p><li> David Foster, Generative Deep Learning, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/" target="_blank"><tt>https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/</tt></a></li>
<p><li> Babcock and Gavras, Generative AI with Python and TensorFlow, <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_blank"><tt>https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2</tt></a></li>
</ul>
</div>
</section>
@@ -384,7 +395,7 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<p>This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
<p>This week: Refresh linear algebra, GBC chapter 2. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
</div>
</section>
doc/pub/week34/html/week34-solarized.html
+19 -8
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@@ -320,7 +320,7 @@ MathJax.Hub.Config({
<li> Tuesdays 815am-12pm and 1215pm-4pm</li>
<li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
</ul>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics as well as solving specific problems. These lectures will also be recorded.</li>
<ul>
<li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
</ul>
@@ -400,9 +400,9 @@ MathJax.Hub.Config({
<h2 id="deadlines-for-projects-tentative">Deadlines for projects (tentative) </h2>
<ol>
<li> Project 1: October 15 (available September 4) graded with feedback)</li>
<li> Project 2: November 13 (available October 8, graded with feedback)</li>
<li> Project 3: December 11 (available November 12, graded with feedback)</li>
<li> Project 1: October 7 (available September 2) graded with feedback)</li>
<li> Project 2: November 4 (available October 8, graded with feedback)</li>
<li> Project 3: December 9 (available November 5, graded with feedback)</li>
</ol>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
@@ -436,7 +436,7 @@ MathJax.Hub.Config({
<p>
<p>The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</tt></a>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.</p>
<p>In addition to the lecture notes, we recommend the books of Rasckha et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. </p>
</div>
@@ -444,14 +444,25 @@ MathJax.Hub.Config({
<h2 id="textbooks">Textbooks </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<b>Main texts</b>
<p>
<ul>
<li> Goodfellow, Bengio, and Courville (GBC), Deep Learning <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a></li>
<li> Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at <a href="https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312" target="_blank"><tt>https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312</tt></a>, see also <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
<div class="alert alert-block alert-block alert-text-normal">
<b>Other texts</b>
<p>
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer</a>.</li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
<li> David Foster, Generative Deep Learning, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/" target="_blank"><tt>https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/</tt></a></li>
<li> Babcock and Gavras, Generative AI with Python and TensorFlow, <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_blank"><tt>https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2</tt></a></li>
</ul>
</div>
@@ -462,7 +473,7 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<p>This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
<p>This week: Refresh linear algebra, GBC chapter 2. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
</div>
doc/pub/week34/html/week34.html
+19 -8
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@@ -397,7 +397,7 @@ MathJax.Hub.Config({
<li> Tuesdays 815am-12pm and 1215pm-4pm</li>
<li> Wednesdays 815am-12pm and 1215pm-4pm.</li>
</ul>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics. These lectures will also be recorded.</li>
<li> On Mondays we have a regular lecture which will be organized as a mix of active learning sessions and lecturing. These lectures/active learning sessions start at 1015am and end at 12pm and serve the aims of giving an overview over various topics as well as solving specific problems. These lectures will also be recorded.</li>
<ul>
<li> <a href="https://youtu.be/" target="_blank">Link to recording of lecture TBA</a></li>
</ul>
@@ -477,9 +477,9 @@ MathJax.Hub.Config({
<h2 id="deadlines-for-projects-tentative">Deadlines for projects (tentative) </h2>
<ol>
<li> Project 1: October 15 (available September 4) graded with feedback)</li>
<li> Project 2: November 13 (available October 8, graded with feedback)</li>
<li> Project 3: December 11 (available November 12, graded with feedback)</li>
<li> Project 1: October 7 (available September 2) graded with feedback)</li>
<li> Project 2: November 4 (available October 8, graded with feedback)</li>
<li> Project 3: December 9 (available November 5, graded with feedback)</li>
</ol>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
@@ -513,7 +513,7 @@ MathJax.Hub.Config({
<p>
<p>The lecture notes are collected as a jupyter-book at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</tt></a>.</p>
<p>In addition to the lecture notes, we recommend the books of Bishop, Hastie et al, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. Finally, we also recommend the hands-on text by Geron, see next slide for links.</p>
<p>In addition to the lecture notes, we recommend the books of Rasckha et al and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these texts. The text by Hastie et al is also widely used in the Machine Learning community. </p>
</div>
@@ -521,14 +521,25 @@ MathJax.Hub.Config({
<h2 id="textbooks">Textbooks </h2>
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<b>Main texts</b>
<p>
<ul>
<li> Goodfellow, Bengio, and Courville (GBC), Deep Learning <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a></li>
<li> Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at <a href="https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312" target="_blank"><tt>https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312</tt></a>, see also <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank"><tt>https://sebastianraschka.com/blog/2022/ml-pytorch-book.html</tt></a></li>
</ul>
</div>
<div class="alert alert-block alert-block alert-text-normal">
<b>Other texts</b>
<p>
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_blank">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> <a href="https://www.springer.com/gp/book/9780387848570" target="_blank">Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer</a>.</li>
<li> <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/" target="_blank">Aurelien Geron (AG), Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
<li> <a href="https://probml.github.io/pml-book/book1.html" target="_blank">Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction</a></li>
<li> David Foster, Generative Deep Learning, <a href="https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/" target="_blank"><tt>https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/</tt></a></li>
<li> Babcock and Gavras, Generative AI with Python and TensorFlow, <a href="https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2" target="_blank"><tt>https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2</tt></a></li>
</ul>
</div>
@@ -539,7 +550,7 @@ MathJax.Hub.Config({
<div class="alert alert-block alert-block alert-text-normal">
<b></b>
<p>
<p>This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
<p>This week: Refresh linear algebra, GBC chapter 2. Install scikit-learn. See lecture notes for week 34 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> (these notes).</p>
</div>
doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
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doc/src/week34/week34.do.txt
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@@ -130,20 +130,26 @@ In addition to the lecture notes, we recommend the books of Rasckha et al and G
!split
===== Textbooks =====
!bblock Main texts
* Goodfellow, Bengio, and Courville (GBC), Deep Learning URL:"https://www.deeplearningbook.org/"
* Sebastian Raschka, Yuxi Lie, and Vahid Mirjalili, Machine Learning with PyTorch and Scikit-Learn at URL:"https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312", see also URL:"https://sebastianraschka.com/blog/2022/ml-pytorch-book.html"
!eblock
!bblock
* "Goodfellow, Bengio, and Courville (GBC), Deep Learning":"https://www.deeplearningbook.org/"
!bblock Other texts
* Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
* "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570".
* "Aurelien Geron (AG), Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/". This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
* "Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction":"https://probml.github.io/pml-book/book1.html"
* David Foster, Generative Deep Learning, URL:"https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/"
* Babcock and Gavras, Generative AI with Python and TensorFlow, URL:"https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2"
!eblock
!split
===== Reading suggestions week 34 =====
!bblock
This week: Refresh linear algebra, GBC chapters 1 and 2. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html (these notes).
This week: Refresh linear algebra, GBC chapter 2. Install scikit-learn. See lecture notes for week 34 at https://compphysics.github.io/MachineLearning/doc/web/course.html (these notes).
!eblock
@@ -2646,3 +2652,4 @@ Add now a model which allows you to make polynomials up to degree $15$. Perform