Difference between revisions of "Machine learning"
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* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar | * [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar | ||
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy | * [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy | ||
| − | * Expectation Maximization - Brandon and Jonathan | + | * [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan |
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa | * [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa | ||
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh | * [[Media:KalmanFilter.pdf | Kalman filter]] - Josh | ||
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott | * [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott | ||
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry | * [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry | ||
| − | * Deep belief networks | + | * [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger |
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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]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R). | ||
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| + | == Online Machine Learning Resources == | ||
| + | * [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://setosa.io/ev/markov-chains/ Markov Chains explained visually] | ||
| + | * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop | ||
| + | * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations. | ||
Latest revision as of 20:14, 11 October 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; also James, Witten, Hastie, and Tibshirani's book, which has less focus on mathematical foundations and more on applications (in R).
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
- arXiv Sanity Preserver, an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
