Difference between revisions of "Machine learning"
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* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book] | * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book] | ||
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book] | * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book] | ||
| + | * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book] | ||
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Revision as of 16:22, 12 September 2017
Our Journal Club slides about ML will be posted here.
- Machine learning introduction - Gunnar
- Linear Regression - Sisi
- Linear Classification - Parisa
- Markov Chain Montre Carlo - Gunnar
- Introduction to PCA and ICA - Cindy
- Expectation Maximization - Brandon and Jonathan
- Hidden Markov Models - Parisa
- Kalman filter - Josh
- Rate-based networks & error back-propagation learning - Scott
- Support Vector Machines - Jerry
- Deep belief networks - Tiger
Machine learning books
- Tom Mitchell's book
- Smola & Vishwanathan's book
- Daume's book
- Murphy's book
- Shalev-Shwartz & Ben-David's book
- Nilsson's book
- Harrington's book
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book
- Hastie, Tibshirani, and Friedman's book
Online Machine Learning Resources
- ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)
- Andrew Ng ML course exercises for Python
- Markov Chains explained visually
- Mo Chen's toolbox for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
