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Resources

 

Useful Links and Resources

matlab:

• registered students can get a free matlab license here

Linear Algebra Resources:

• Linear Algebra lectures by Professor Gil Strang at MIT
• A Tutorial on Linear Algebra by Professor C. T. Abdallah
• Linear Algebra Review by Professor Fernando Paganini, UCLA
• The Matrix Cookbook - It won’t teach you linear algebra, but this free desktop reference on matrices may come in handy.

Probability Resources:

• Review of probability from a course by David Blei at Princeton
• Andrew Moore’s Probability tutorial slides (somewhat incomplete)
• Another probability review, from UCI

Online Courses:

• Coursera ML course - Much more basic than this course, but good intro to ML
• Caltech short ML course - good coverage of several of our topics

TextBooks:

• C. Bishop, Pattern Recognition and Machine Learning. 2007
  • our semi-textbook; from an engineering perspective
• LIONbook
  • Less technical than what we are doing, but very clearly written
• T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2009
  • uses the language and notation of statistics
• K. P. Murphy, Machine Learning: A Probabilistic Perspective 2012
  • more depth on probability than we will cover, but good.
• T. Mitchell, Machine Learning. 1997.
  • now a bit outdated.

Other Resources:

• L^AT_EX Tutorial
• Theoretical CS cheat sheet - only a subset of this incredible dense packet applies to our course, but it’s extremely concise and has seemingly innumerable useful identities.
• Octave Installation - but note; we only support Matlab, and require all Homework to run in pure matlab
• Andrew Moore’s lecture slides - we will use a number of these