CS 446

CS 446 - Machine Learning

Fall 2020

Machine LearningCS446D346792ONL31230 - 1345 W F    Sanmi Koyejo
Machine LearningCS446D446793ONL41230 - 1345 W F    Sanmi Koyejo
Machine LearningECE449D373595ONL31230 - 1345 W F    Sanmi Koyejo
Machine LearningECE449D473597ONL41230 - 1345 W F    Sanmi Koyejo

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