Resources

Resources

Math/Stat/ML/CS resources ordered by increasing prerequisite knowledge:

  • : Jon and Jerry's intro course on probability, Bayesian stats, and frequentist stats. Completely self-contained on OCW!

  • : Gil Strang's legendary linear algebra course.

  • : incredible podcast on machine learning by friends of the SMRC Ryan Adams and Katherine Gorman.

  • : intro to programming using Python.

  • : Ryan Adams' colloquium at Ó³»­´«Ã½.

  • : go big or go home with pySpark in this archived BerkeleyX course.

  • : excellent 6-part series on Coursera from UCSD.

  • , Christopher Bishop. Bayesian treatment of ML.

  • , Trevor Hastie, Robert Tibshirani, Jerome Friedman. Both stats and ML.

  • , Kevin Murphy. Encyclopedic on ML.

  • , Rick Durrett: a standard reference on modern, measure-theoretic probability theory.

  • P1, P2, P3: problem sets from Alex's Harvard graduate course on the core concepts of modern, measure-theoretic probability theory.

Biology resources for computationalists:

  • : Eric Lander's introduction to biology!

  • , Horace Judson (1979): a masterpiece of history of science, covering the birth and development of molecular biology, based on interviews with over one hundred of the scientists who played key roles.

  • MPG primer videos: experts from across the Ó³»­´«Ã½ give in-depth introductions to principals, data, and analysis underlying medical and population genetics.