Machine learning algorithms from a signal processing viewpoint; unsupervised learning (K-means, deterministic annealing, EM algorithm); supervised learning (Support Vector Machines, neural networks); regression; Bayesian inference and tracking using Markov chain Monte Carlo and sequential Monte Carlo (particle filter) techniques.

Prerequisites: ECE 235.

4

Units

Letter

Grading

1, 2, 3

Passtime

Graduate students only

Level Limit

Engineering

College
JEONG H
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Spring 2024 . Jeong H
PHELP1437
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