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Morten Hjorth-Jensen
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doc/pub/week34/html/._week34-bs009.html
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<h2 id="recommended-textbooks" class="anchor">Recommended textbooks </h2>
<ol>
<li> 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></li>
<li> 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>.</li>
</ol>
<p>In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two 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 below.</p>
<ol>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_self"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_self"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732" target="_self"><tt>https://www.springer.com/gp/book/9780387310732</tt></a>.</li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/" target="_self"><tt>https://www.deeplearningbook.org/</tt></a>. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
<p>Additional textbooks:</p>
<ol>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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> This is a well-known text and serves as additional literature.</li>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_self"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
</ol>
<p>
doc/pub/week34/html/week34-reveal.html
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<h2 id="recommended-textbooks">Recommended textbooks </h2>
<ol>
<p><li> 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></li>
<p><li> 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>.</li>
</ol>
<p>
<p>In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two 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 below.</p>
<ol>
<p><li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<p><li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
<p><li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732" target="_blank"><tt>https://www.springer.com/gp/book/9780387310732</tt></a>.</li>
<p><li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a>. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
<p>
<p>Additional textbooks:</p>
<ol>
<p><li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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> This is a well-known text and serves as additional literature.</li>
<p><li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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>. This is a well-known text and serves as additional literature.</li>
<p><li> Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
</ol>
</section>
doc/pub/week34/html/week34-solarized.html
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<h2 id="recommended-textbooks">Recommended textbooks </h2>
<ol>
<li> 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></li>
<li> 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>.</li>
</ol>
<p>In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two 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 below.</p>
<ol>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732" target="_blank"><tt>https://www.springer.com/gp/book/9780387310732</tt></a>.</li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a>. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
<p>Additional textbooks:</p>
<ol>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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> This is a well-known text and serves as additional literature.</li>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
</ol>
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doc/pub/week34/html/week34.html
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@@ -510,18 +510,18 @@ MathJax.Hub.Config({
<h2 id="recommended-textbooks">Recommended textbooks </h2>
<ol>
<li> 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></li>
<li> 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>.</li>
</ol>
<p>In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two 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 below.</p>
<ol>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732." target="_blank"><tt>https://www.springer.com/gp/book/9780387310732.</tt></a></li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/." target="_blank"><tt>https://www.deeplearningbook.org/.</tt></a> Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
<li> Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a href="https://www.springer.com/gp/book/9780387310732" target="_blank"><tt>https://www.springer.com/gp/book/9780387310732</tt></a>.</li>
<li> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a href="https://www.deeplearningbook.org/" target="_blank"><tt>https://www.deeplearningbook.org/</tt></a>. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.</li>
</ol>
<p>Additional textbooks:</p>
<ol>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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> This is a well-known text and serves as additional literature.</li>
<li> Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, 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>. This is a well-known text and serves as additional literature.</li>
<li> Aurelien Geron, Hands&#8209;On Machine Learning with Scikit&#8209;Learn and TensorFlow, O'Reilly, <a href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/." target="_blank"><tt>https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/.</tt></a> This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</li>
</ol>
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===== Recommended textbooks =====
o The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html.
o The lecture notes are collected as a jupyter-book at URL:"https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html".
In addition to the lecture notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two 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 below.
o Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732.
o Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, URL:"https://www.springer.com/gp/book/9780387310732".
o Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
o Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at URL:"https://www.deeplearningbook.org/". Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text. The weekly plans will include reading suggestions from these two textbooks.
Additional textbooks:
o Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
o Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, URL:"https://www.springer.com/gp/book/9780387848570". This is a well-known text and serves as additional literature.
o Aurelien Geron, 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.