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2016-17

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CSE 474LR Intro Machine Learning
Computer Science & Engineering

Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.

Credits: 4
Semester(s) Typically Offered: Fall, Spring
Grading: Graded (GRD)
Pre-Requisites: CSE 250 and EAS 305 or MTH 411 or STA 301 or MTH 309. Approved Computer Science, Computer Engineering, Bioinformatics/CS Majors only.


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