Topics #

Foundations #

• Information theory

Topics #

• Automated machine learning
• Cluster analysis
• Dimensionality reduction
• Image completion
• Machine learning for healthcare
• Machine learning for physical sciences
• One-shot learning
• Recommender system
• Training dataset

Methods #

• Artificial neural network
• Boosting (machine learning)
• Decision tree
  • Random forest
• Deep learning
• EM algorithm
• Naive Bayes classifier
• Cross validation and Stability selection

Books #

• The elements of statistical learning

• An Introduction to Statistical Learning with applications in R - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

• free machine learning books

• Machine Learning by Tom M. MItchell
• Bayesian Reasoning and Machine Learning
• Machine Learning: A Probabilistic Perspective by Kevin Murphy
• a reading list by Michael I. Jordan

Courses #

• Andrew Ng's artificial intelligence | machine learning - open lecture.

  • http://ml-class.org/ - open course
  • http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

• Tom Mitchell

• Practical Machine Learning by Michael Jordan (computer scientist)

• Machine Learning Summer School 2011 - Bordeaux

• Machine Learning Fall 2009 by Carlos Guestrin
• http://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA - Machine Learning by mathematical monk
• CALTECH: learning from data
• Machine Learning and Probabilistic Graphical Models Course by Sargur Srihari
• Kaggle Python Tutorial on Machine Learning

Tools and libraries #

• Python#Modules_.26_Libraries
• scikit-learn
• http://mldemos.epfl.ch/ - MLDemos
• http://harthur.github.com/brain/ - Javascript supervised machine learning library
• GraphLab
• [[https://github.com/JohnLangford/vowpal_wabbit/wiki]] - vowpal wabbit, a fast online learning algorithm
• http://www.cs.waikato.ac.nz/ml/weka/index.html - Weka
• http://blog.mashape.com/post/48074869493/list-of-machine-learning-apis

Tutorials and Articles #

• How to Write a Spelling Corrector by Peter Norvig
• The Ideal Large Scale Learning Class
• Self-Study Guide to Machine Learning
• Machine learning in 10 pictures
• kaggle:Learning from the best
• Machine Learning Done Wrong
• Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur
• Machine Learning is Fun!
  • Machine Learning is Fun! Part 2
  • Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks
  • Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
• What I learned from Deep Learning Summer School 2016
• Model evaluation, model selection, and algorithm selection in machine learning - Part I
  • Model evaluation, model selection, and algorithm selection in machine learning - Part II
  • Model evaluation, model selection, and algorithm selection in machine learning - Part III - Cross-validation and hyperparameter tuning
• Google developers: Machine Learning Recipes
• Rules of Machine Learning: Best Practices for ML Engineering

Websites and blogs #

• http://metaoptimize.com/qa/questions/3163/good-machine-learning-blogs

Talks and lectures #

• http://blog.videolectures.net/100-most-popular-machine-learning-talks-at-videolectures-net/
• mathematical monk: Machine learning
• Advice for applying Machine Learning by Andrew Ng

Other resources #

• Machine Learning Cheat Sheet (for scikit-learn)
• Machine learning classifier gallery

References #

Reviews #

• Machine learning: Trends, perspectives, and prospects

Explaining the prediction #

• "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Incoming Links #

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• Python/Modules
• Recommender system
• Regular expression
• scikit-learn

Suggested Pages #

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