ECE 449 - Machine Learning

Spring 2022

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Machine LearningCS446P331421LCD31230 - 1345 T R  1002 Electrical & Computer Eng Bldg  Alexander Schwing
Matus Jan Telgarsky
Machine LearningCS446P439433LCD31230 - 1345 T R  1002 Electrical & Computer Eng Bldg  Alexander Schwing
Matus Jan Telgarsky
ECE411ONL0 -    
Neuroscience for EngineersECE421LEC0 -    
ECE425ONL0 -    
ECE438ONL0 -    
ECE448ONL0 -    
ECE449ONL0 -    
Machine LearningECE449P370856LCD31230 - 1345 T R  1002 Electrical & Computer Eng Bldg  Alexander Schwing
Matus Jan Telgarsky
Machine LearningECE449P470857LCD31230 - 1345 T R  1002 Electrical & Computer Eng Bldg  Alexander Schwing
Matus Jan Telgarsky
Machine LearningECE449ZJU73867LEC41800 - 1920U T     Alexander Schwing
ECE490ONL0 -    
ECE534ONL0 -    

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