PHYSICS 223
Machine Learning and Statistics
Theory and practice of machine learning and statistics for physics and astronomy. Topics include: clustering, dimensionality reduction, Bayesian statistics, Markovchains, variational inference, supervised and unsupervised learning, neural networks and modern deep learning architectures. Familiarity with python numerical programing assumed.
Topics
- clustering
- dimensionality reduction
- Bayesian statistics
- Markovchains
- variational inference
- supervised and unsupervised learning
- neural networks and modern deep learning architectures
Topics explicitly listed in official catalogue description
Prerequisites & course information
Theory and practice of machine learning and statistics for physics and astronomy. Topics include: clustering, dimensionality reduction, Bayesian statistics, Markovchains, variational inference, supervised and unsupervised learning, neural networks and modern deep learning architectures. Familiarity with python numerical programing assumed.
Sources
- Course catalogue ↗2026-2027