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Course Information: Same as CS 446. See CS 446.

The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas, (1) discriminative models, (2) generative models, and (3) reinforcement learning models. In particular we will cover the following: linear regression, logistic regression, support vector machines, deep nets, structured methods, learning theory, kMeans, Gaussian mixtures, expectation maximization, VAEs, GANs, Markov decision processes, Q-learning and Reinforce.

• linear regression,
• logistic regression,
• support vector machines,
• deep nets,
• structured methods,
• learning theory basics,
• kMeans,
• Gaussian mixtures,
• expectation maximization,
• VAEs,
• GANs,
• Markov decision processes,
• Q-learning
• Reinforce

• Linear Algebra
• Probability
• Multivariate Calculus
• Python

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Engineering Science: 1 credit