Resources

Python and Jupyter Resources

• Google’s Python Class
• Stanford Numpy Tutorial
  • numpy for matlab users -great summary table
• Jupyter Notebook Tutorial
  • Good intro, including installation. You can skip the lengthy middle section, but note the sample notebooks
• Matplotlib Tutorial Notebook
• Python Data Science Handbook
• Scikit-Learn Documentation
• Google Colab Introduction

Linear Algebra Resources

• Notes from Stanford - short
• 3blue1brown -beautiful animated expanations
• Linear Algebra lectures by Professor Gil Strang at MIT - full course
• 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

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

Optimization Resources

• convex optimization from Stanford

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  • now a bit outdated.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.
• Andrew Moore’s lecture slides
  • we will use a number of these