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CS 229

Machine Learning (STATS 229)

  • autumn
  • spring
  • 2019-2020

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: linear algebra, and basic probability and statistics.

Sections

  • DIS

    • Friday 1:30:00 PM - 2:50:00 PM @ Gates B12

  • LEC

  • LEC

  • DIS

    • Thursday 10:30:00 AM - 11:50:00 AM

    • Thursday 1:30:00 PM - 2:50:00 PM

Grade Distribution

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