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Morten Hjorth-Jensen
2023-08-21 09:54:36 +02:00
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doc/pub/week34/html/._week34-bs009.html
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@@ -362,7 +362,7 @@ MathJax.Hub.Config({
<ul>
<li> <a href="https://www.deeplearningbook.org/" target="_self">Goodfellow, Bengio, and Courville (GBC), Deep Learning</a></li>
<li> Christopher M. Bishop (CB), Pattern Recognition and Machine Learning</li>
<li> "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_self"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.</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>
</ul>
doc/pub/week34/html/week34-reveal.html
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@@ -347,7 +347,7 @@ MathJax.Hub.Config({
<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> "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.</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>
</ul>
doc/pub/week34/html/week34-solarized.html
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@@ -425,7 +425,7 @@ MathJax.Hub.Config({
<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> "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.</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>
</ul>
doc/pub/week34/html/week34.html
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@@ -502,7 +502,7 @@ MathJax.Hub.Config({
<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> "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, <a href="https://www.springer.com/gp/book/9780387848570" target="_blank"><tt>https://www.springer.com/gp/book/9780387848570</tt></a>.</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>
</ul>
doc/pub/week34/ipynb/ipynb-week34-src.tar.gz
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doc/pub/week34/ipynb/week34.ipynb
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doc/src/week34/beamerweek34.do.txt
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@@ -118,7 +118,8 @@ In addition to the lecture notes, we recommend the books of Bishop, Hastie et al
!bblock
* "Goodfellow, Bengio, and Courville (GBC), Deep Learning":"https://www.deeplearningbook.org/"
* Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
* Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"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.
* "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"
!eblock
doc/src/week34/week34.do.txt
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@@ -116,7 +116,7 @@ In addition to the lecture notes, we recommend the books of Bishop, Hastie et al
!bblock
* "Goodfellow, Bengio, and Courville (GBC), Deep Learning":"https://www.deeplearningbook.org/"
* Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
* "Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570".
* "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"
!eblock