Course Websites

ECE 449 - Machine Learning

Last offered Fall 2022

Official Description

Course Information: Same as CS 446. See CS 446.

Related Faculty

Goals

The goal of Machine Learning is to build computer systems that can adapt and learn from data. In this course we will cover three main areas, (1) discriminative models, (2) generative models, and (3) reinforcement learning models. In particular we will cover the following: linear regression, logistic regression, support vector machines, deep nets, structured methods, learning theory, kMeans, Gaussian mixtures, expectation maximization, VAEs, GANs, Markov decision processes, Q-learning and Reinforce.

Topics

  • linear regression,
  • logistic regression,
  • support vector machines,
  • deep nets,
  • structured methods,
  • learning theory basics,
  • kMeans,
  • Gaussian mixtures,
  • expectation maximization,
  • VAEs,
  • GANs,
  • Markov decision processes,
  • Q-learning
  • Reinforce

Topical Prerequisites

  • Linear Algebra
  • Probability
  • Multivariate Calculus
  • Python

Texts

No text.

ABET Category

Engineering Science: 1 credit
TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Machine LearningB373595LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningB473597LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningOG77677ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui
Machine LearningOU77675ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui