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.
Topics
- Supervised learning: Bayes decision theory
- discriminant functions
- maximum likelihood estimation
- nearest neighbor rule
- linear discriminant analysis
- support vector machines
- neural networks
- deep learning networks
Topics explicitly listed in official catalogue description
Prerequisites & course information
Cross-listed with: DATA603 , BIOI603 , MSQC603 . Restriction: Must be in one of the following in person or online programs: (Data Science Professional Studies Post-Baccalaureate Certificate, Master of Science in Data Science, or Master of Science in Machine Learning). Credit Only Granted for: BIOI603 , DATA603 , MSAI603 , MSML603 , MSQC603 or CMSC643 . Formerly: CMSC643 .
Sources
- Course catalogue ↗2026-2027