EdusalsaDiscover Your Stanford

- Not Offered

3 - 4 units

Letter or Credit/No Credit

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/numpy, familiarity with probability theory to the equivalency of CS109 or STATS116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51.

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**Pranav Rajpurkar** is a PhD student in Computer Science at Stanford, working on Artificial Intelligence for Healthcare. He was previously a Stanford undergrad ('16).

**Brad Girardeau** got his B.S, M.S. degrees in computer science at Stanford ('16, '17). When not thinking about computer security, he can be found playing violin or running across the Golden Gate Bridge.

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