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
13 reviews
Average rating:
4.46
2 reviews
Average rating:
1.00
3 reviews
Average rating:
5.00
14 reviews
Average rating:
2.93
Spring 2024
3 reviews
Average rating:
5.00
Past Semesters
13 reviews
Average rating:
4.46
2 reviews
Average rating:
1.00