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## Textbooks
_Recommended textbooks_:
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the electure 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 lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- 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.
- 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 large extent this text.
- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
The weekly plans will include reading suggestions from these two textbooks.
_Additional textbooks_:
- 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.
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<div class="section" id="textbooks">
<h1>Textbooks<a class="headerlink" href="#textbooks" title="Permalink to this headline"></a></h1>
<p><em>Recommended textbooks</em>:
The lecture notes are collected as a jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</a>. In addition to the electure 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.</p>
The lecture notes are collected as a jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</a>. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.</p>
<ul class="simple">
<li><p>Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387310732">https://www.springer.com/gp/book/9780387310732</a>. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at <a class="reference external" href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf">https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf</a></p></li>
<li><p>Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a class="reference external" href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a>. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text.
<li><p>Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a class="reference external" href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a>. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text.</p></li>
<li><p>Kevin Murphy, Probabilistic Machine Learning, an Introduction, <a class="reference external" href="https://probml.github.io/pml-book/book1.html">https://probml.github.io/pml-book/book1.html</a>
The weekly plans will include reading suggestions from these two textbooks.
<em>Additional textbooks</em>:</p></li>
<li><p>Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">https://www.springer.com/gp/book/9780387848570</a>. This is a well-known text and serves as additional literature.</p></li>
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## Textbooks
_Recommended textbooks_:
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the electure 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 lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
- 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.
- 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 large extent this text.
- Kevin Murphy, Probabilistic Machine Learning, an Introduction, https://probml.github.io/pml-book/book1.html
The weekly plans will include reading suggestions from these two textbooks.
_Additional textbooks_:
- 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.