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ECE 449 - Machine Learning

Spring 2021

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 LearningP370856ONL31530 - 1645 T R    Matus Jan Telgarsky
Machine LearningP470857ONL41530 - 1645 T R    Matus Jan Telgarsky
Machine LearningR372808OLC31530 - 1645 T R     Alexander Schwing
Machine LearningR472809OLC41530 - 1645 T R     Alexander Schwing