# Intro 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:
ECSE-2500 Engineering Probability
and
one of:
MATH-2010 Multivar Calc & Matrix Algebra MATH-2011 Multivariable Calculus
and
MATH-2012 Matrix Algebra

# Past Term Data
Offered
Not Offered
Offered as Cross-Listing Only
No Term Data
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Detailed View

| Spring | Summer | Fall
--- | --- | --- | ---
| (Session 1) | (Session 2) |
2026 |  |  |

2025

Intro To Machine Learning (3c)

- Richard J Radke

Seats Taken: 21/45

2024

Intro To Machine Learning (3c)

- Richard J Radke

Seats Taken: 41/65

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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