Pattern Recognition and Machine Learning

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Description: Statistical pattern classification, supervised and unsupervised learning, feature selection and extraction, clustering, image classification and syntactical pattern recognition.

Prerequisite: Recommended preparation is EE210 or equivalent probability and statistics and linear algebra courses.

Note: Students must have good programming skills in either MATLAB or Python or other programming language suitable for machine learning.

“Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.” —Wikipedia

“Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning.” —Wikipedia

“In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.” — Coursera