# 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
Prereqs:
MATH-2010 Multivariable Calculus and Matrix Algebra
and
ECSE-2500 Engineering Probability

# Past Term Data
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Not Offered
Offered as Cross-Listing Only
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| Spring | Summer | Fall
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| (Session 1) | (Session 2) |
2024 |  |  |
2023 |  |  | Intro To Machine Learning (3c) Tianyi Chen Seats Taken: 26/65
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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