ECE 449 - Machine Learning

Spring 2021

TitleRubricSectionCRNTypeHoursTimesDaysLocationInstructor
Machine LearningCS446P331421ONL31530 - 1645 T R    Matus Jan Telgarsky
Machine LearningCS446P439433ONL41530 - 1645 T R    Matus Jan Telgarsky
Machine LearningCS446R368039OLC31530 - 1645 T R     Alexander Schwing
Machine LearningCS446R468040OLC41530 - 1645 T R     Alexander Schwing
Machine LearningECE449P370856ONL31530 - 1645 T R    Matus Jan Telgarsky
Machine LearningECE449P470857ONL41530 - 1645 T R    Matus Jan Telgarsky
Machine LearningECE449R372808OLC31530 - 1645 T R     Alexander Schwing
Machine LearningECE449R472809OLC41530 - 1645 T R     Alexander Schwing

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