Machine learning: Difference between revisions
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== Online Machine Learning Resources == | == Online Machine Learning Resources == | ||
* [https://www.coursera.org/learn/machine-learning ML | * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)] | ||
*[http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python] | *[http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python] | ||
*[http://setosa.io/ev/markov-chains/ Markov Chains explained visually] | *[http://setosa.io/ev/markov-chains/ Markov Chains explained visually] | ||
Revision as of 17:49, 14 September 2016
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
