Introduction to Machine Learning

ECSE-4840

A broad introduction to statistical machine learning. Topics include supervised learning: generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines; unsupervised learning: clustering, dimensionality reduction, kernel methods; learning theory: bias/variance tradeoffs, practical advice; online learning and reinforcement learning. Recent applications of machine learning, such as to data mining, robot navigation, speech recognition, image processing, and signal processing.

3 credits

Past Term Data

Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
Spring Summer Fall
(Session 1) (Session 2)
2023
Intro To Machine Learning (3c)
  • Tianyi Chen
Seats Taken: 20/70
2022
Intro To Machine Learning (3c)
  • Tianyi Chen
Seats Taken: 23/50
2021
Intro To Machine Learning (3c)
  • Tianyi Chen
Seats Taken: 41/50
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