CS 446
CS 446 - Machine Learning
Spring 2024
Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
---|---|---|---|---|---|---|---|---|---|
Machine Learning | CS446 | P3 | 31421 | LCD | 3 | 1230 - 1345 | T R | 1320 Digital Computer Laboratory | Shenlong Wang Han Zhao |
Machine Learning | CS446 | P4 | 39433 | LCD | 3 | 1230 - 1345 | T R | 1320 Digital Computer Laboratory | Shenlong Wang Han Zhao |
Machine Learning | ECE449 | P3 | 70856 | LCD | 3 | 1230 - 1345 | T R | 1320 Digital Computer Laboratory | Shenlong Wang Han Zhao |
Machine Learning | ECE449 | P4 | 70857 | LCD | 3 | 1230 - 1345 | T R | 1320 Digital Computer Laboratory | Shenlong Wang Han Zhao |
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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