MSQC603

Principles of Machine Learning

A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning: Bayes decision theory, discriminant functions, maximum likelihood estimation, nearest neighbor rule, linear discriminant analysis, support vector machines, neural networks, deep learning networks. Unsupervised learning: clustering, dimensionality reduction, PCA, auto-encoders. The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.

Fall 2024

9 reviews
Average rating: 4.44

0 reviews
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1 review
Average rating: 5.00

12 reviews
Average rating: 2.83

Spring 2024

1 review
Average rating: 5.00

Fall 2023

9 reviews
Average rating: 4.44

0 reviews
Average rating: N/A

* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.