CS 446 D4 - 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

Academic Program Restrictions:

NDEG: HRE Online -UIUC EDD:Ed Pol Org &Ldshp Onl-UIUC NDEG:Computer Science Onl-UIUC

Option 1

Course Level: Graduate

Credit: 4

Term(s): Fall