CP322
Machine Learning
Fall 2023
Topics
- Supervised and unsupervised learning
- regression and classification
- clustering
- dimensionality reduction
- neural networks and deep learning
- reinforcement learning.
Readings
- Pattern Recognition and Machine Learning — Christopher M. Bishop; 2006.
- Machine Learning: A Probabilistic Perspective — Kevin P. Murphy; 2012.
- The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani and Jerome Friedman; 2nd edition.
- Reinforcement Learning: An Introduction — Richard S. Sutton and Andrew G. Barto; 2nd edition, 2018.
- Python, scikit-learn and PyTorch; lecture slides, notes and videos.
Sources
- Course catalogue ↗2026–2027
- Public syllabus copy ↗Fall 2023
- Books are suggested readings. Host category says Data Visualization, but the syllabus itself identifies CP322.