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CS 156ab

Learning Systems

9 units (3-1-5) | first, third terms

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
    Yaser S. Abu-Mostafa et al. · Recommended textbook; no required textbook

Prerequisites & course information

Prerequisites: Ma 2 and CS 2, or equivalent.

Sources
Catalogue variants