CS 156ab
Learning Systems
Introduction to the theory, algorithms, and applications of automated learning. How much information is needed to learn a task, how much computation is involved, and how it can be accomplished. Special emphasis will be given to unifying the different approaches to the subject coming from statistics, function approximation, optimization, pattern recognition, and neural networks.
Public instructor course description, accessed 2026-11-05
Topics
- Linear models
- Theory of generalization
- Regularization and validation
- Occam's razor and data snooping
- Neural networks
- Support vector machines
Fall 2023
Readings
- Learning From Data - a Short Course
Prerequisites & course information
Prerequisites: Ma 2 and CS 2, or equivalent.
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
- Catalogue entry ↗
- Official public course page or syllabus ↗Public instructor course description, accessed 2026-11-05
- Official public course page or syllabus ↗Fall 2023
Catalogue variants
- Learning Systems · 9 units (3-1-5) | first, third terms