CS 446 - Machine Learning
|Machine Learning||CS446||D3||46792||ONL||3||1230 - 1345||W F||Sanmi Koyejo|
|Machine Learning||CS446||D4||46793||ONL||4||1230 - 1345||W F||Sanmi Koyejo|
|Machine Learning||ECE449||D3||73595||ONL||3||1230 - 1345||W F||Sanmi Koyejo|
|Machine Learning||ECE449||D4||73597||ONL||4||1230 - 1345||W F||Sanmi Koyejo|
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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.
- Computational Materials