ECE 449 - Machine Learning

Fall 2022

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Machine LearningCS446B346792LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningCS446B446793LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningCS446OG77676ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui
Machine LearningCS446OU77674ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui
Machine LearningECE449B373595LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningECE449B473597LCD31230 - 1345 W F  1404 Siebel Center for Comp Sci Shenlong Wang
Liangyan Gui
Machine LearningECE449OG77677ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui
Machine LearningECE449OU77675ONL31230 - 1345 W F    Shenlong Wang
Liangyan Gui

Official Description

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

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

Last updated

1/22/2020