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
11 reviews
Average rating:
4.36
0 reviews
Average rating:
N/A
2 reviews
Average rating:
5.00
13 reviews
Average rating:
2.85
Spring 2024
2 reviews
Average rating:
5.00
Fall 2023
11 reviews
Average rating:
4.36
0 reviews
Average rating:
N/A