Resources
Math/Stat/ML/CS resources ordered by increasing prerequisite knowledge:
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MIT 18.05: Jon and Jerry's intro course on probability, Bayesian stats, and frequentist stats. Completely self-contained on OCW!
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MIT 18.06: Gil Strang's legendary linear algebra course.
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Talking Machines: incredible podcast on machine learning by friends of the SMRC Ryan Adams and Katherine Gorman.
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MIT 6.001x: intro to programming using Python.
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Bayesian optimization: Ryan Adams' colloquium at Broad.
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Scalable Machine Learning: go big or go home with pySpark in this archived BerkeleyX course.
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Bioinformatic Algorithms: excellent 6-part series on Coursera from UCSD.
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Pattern Recognition and Machine Learning, Christopher Bishop. Bayesian treatment of ML.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. Both stats and ML.
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Machine Learning: A Probabilistic Perspective, Kevin Murphy. Encyclopedic on ML.
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Probability: Theory and Examples, Rick Durrett: a standard reference on modern, measure-theoretic probability theory.
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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:
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MIT 7.00x: Eric Lander's introduction to biology!
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The Eighth Day of Creation, 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.
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MPG primer videos: experts from across the Broad give in-depth introductions to principals, data, and analysis underlying medical and population genetics.
