Learning Systems Group

Courses

Descriptions from Caltech Catalog

EE 32 ab: Signals, Systems, and Transforms.

9 units (3-0-6); first, second terms. Prerequisites: Ma 1, Ma 2, and EE 20 ab. An introduction to the analysis and synthesis of analog and digital circuits, signals, and systems. Sampling, modulation, and filtering of signals represented as continuous or discrete functions of time. Input-output relations of linear time-invariant systems, state-space representations, and stability analysis. Special emphasis will be placed on transform techniques (Fourier, Laplace, and Z-transforms).

CS/EE/Ma 129 abc: Information and Complexity.

9 units (3-0-6), first and second terms; (1-4-4) third term. Prerequisite: Basic knowledge of probability and discrete mathematics. A basic course in information theory and computational complexity with emphasis on fundamental concepts and tools that equip the student for research and provide a foundation for pattern recognition and learning theory. First term: What is information and what is computation; entropy, source coding, Turing machines, uncomputability. Second term: Topics in information and complexity; Kolmogorov complexity, channel coding, circuit complexity, NP completeness. Third term: Theoretical and experimental projects on current research topics.

CS/CNS/EE 156 ab: Learning Systems.

9 units (3-0-6); first, second terms. Prerequisites: Ma 2 and CS 2, or equivalent. 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.