BIOI603
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
7 reviews
Average rating:
4.57
0 reviews
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N/A
Spring 2024
1 review
Average rating:
5.00
Fall 2023
7 reviews
Average rating:
4.57
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.