Machine Learning credit: 3 or

4 Hours

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. Same as ECE 449 . 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225 ; One of MATH 225 , MATH 257 , MATH 415 , MATH 416 , ASRM 406 or BIOE 210 ; one of CS 361 , STAT 361 , ECE 313 , MATH 362 , MATH 461 , MATH 463 , STAT 400 or BIOE 310 .