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
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* [[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
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* [[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
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* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger
* Supervised vs. unsupervised 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]
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* [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 ==
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* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
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* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
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* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
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* [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
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* [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 books


Online Machine Learning Resources