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CS 446 D3 - Machine Learning

Campus: Urbana-Champaign

Description:

Principles and applications of machine learning. Main paradigms and techniques, including discriminative and generative methods, reinforcement learning: linear regression, logistic regression, support vector machines, deep nets, structured methods, dimensionality reduction, k-means, Gaussian mixtures, expectation maximization, Markov decision processes, and Q-learning. Application areas such as natural language and text understanding, speech recognition, computer vision, data mining, and adaptive computer systems, among others. Course Information: Same as ECE 449. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225; One of MATH 225, MATH 415, MATH 416 or ASRM 406; One of CS 361, ECE 313, MATH 461 or STAT 400.

Special Instructions:

All class meetings will be online and synchronous. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 3

Term(s): Fall


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