CS 446

CS 446 - Machine Learning

Spring 2021

Title	Rubric	Section	CRN	Type	Hours	Times	Days	Instructor
Machine Learning	CS446	P3	31421	ONL	3	1530 - 1645	T R	Matus Jan Telgarsky
Machine Learning	CS446	P4	39433	ONL	4	1530 - 1645	T R	Matus Jan Telgarsky
Machine Learning	CS446	R3	68039	OLC	3	1530 - 1645	T R	Alexander Schwing
Machine Learning	CS446	R4	68040	OLC	4	1530 - 1645	T R	Alexander Schwing
Machine Learning	ECE449	P3	70856	ONL	3	1530 - 1645	T R	Matus Jan Telgarsky
Machine Learning	ECE449	P4	70857	ONL	4	1530 - 1645	T R	Matus Jan Telgarsky
Machine Learning	ECE449	R3	72808	OLC	3	1530 - 1645	T R	Alexander Schwing
Machine Learning	ECE449	R4	72809	OLC	4	1530 - 1645	T R	Alexander Schwing

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Web Page

https://courses.grainger.illinois.edu/cs446/

Official 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.

Subject Area

Computational Materials