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Advice for students of machine learning - David Mimno

mimno.org · 290 words · saved by 1 readers

One of my students recently asked me for advice on learning ML. Here’s what I wrote. It’s biased toward my own experience, but should generalize. My current favorite introduction is Kevin Murphy’s book (Machine Learning). You might also want to look at books by Chris Bishop (Pattern Recognition), Daphne Koller (Probabilistic Graphical Models), and David MacKay (Information Theory, Inference and Learning Algorithms). Anything you can learn about linear algebra and probability/statistics will be useful. Strang’s Introduction to Linear Algebra, Gelman, Carlin, Stern and Rubin’s Bayesian Data Analysis, and Gelman and Hill’s Data Analysis using Regression and Multilevel/Hierarchical models are some of my favorite books. Don’t expect to get anything the first time. Read descriptions of the same thing from several different sources. There’s nothing like trying something yourself. Pick a model and implement it. Work through open source implementations and compare. Are there computational or ma

One of my students recently asked me for advice on learning ML. Here’s what I wrote. It’s biased toward my own experience, but should generalize. My current favorite introduction is Kevin Murphy’s book (Machine Learning). You might also want to look at books by Chris Bishop (Pattern Recognition), Daphne Koller (Probabilistic Graphical Models), and David MacKay (Information Theory, Inference and Learning Algorithms). Anything you can learn about linear algebra and probability/statistics will be useful. Strang’s Introduction to Linear Algebra, Gelman, Carlin, Stern and Rubin’s Bayesian Data…

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