CS446: Machine Learning (Spring 2018)

Course Information

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, Markov decision processes, Q-learning.

Pre-requisites: Probability, linear algebra, and proficiency in Python.
Recommended Text: (1) Machine Learning: A Probabilistic Perspective by Kevin Murphy, (2) Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville, (3) Pattern Recognition and Machine Learning by Christopher Bishop, (4) Graphical Models by Daphne Koller and Nir Friedman.
Grading:

For 3 credit students: ```max(0.5*\text{Final} + 0.5*\text{Homework}, 0.25*\text{Midterm} + 0.25*\text{Final} + 0.5*\text{Homework})```

For 4 credit students: ```0.25*\text{Midterm} + 0.25*\text{Final} + 0.5*\text{Homework}```

The lowest homework grade will be dropped; We do not accept late homework.
Communication: To share information, please use Piazza for all communications.

Note: This syllabus is subject to change.

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Instructors & Teaching Assistants

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