CS 446

CS 446 - Machine Learning

Fall 2025

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Machine LearningCS446B346793ONL3 -    Liangyan Gui
Machine LearningCS446BG80991ONL4 -    Liangyan Gui
Machine LearningCS446BU46792ONL3 -    Liangyan Gui
Machine LearningCS446DS381003ONL3 -    Liangyan Gui
Machine LearningCS446DS460403ONL4 -    Liangyan Gui
Machine LearningCS446MC377676PKG3 -    Liangyan Gui
Machine LearningCS446MC377676PKG3 -    Liangyan Gui
Machine LearningCS446MC477674PKG4 -    Liangyan Gui
Machine LearningCS446MC477674PKG4 -    Liangyan Gui
Machine LearningECE449B373597ONL3 -    Liangyan Gui
Machine LearningECE449BG77677ONL4 -    Liangyan Gui
Machine LearningECE449BU73595ONL3 -    Liangyan Gui

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 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.

Subject Area

  • Computational Materials