T E A C H I N G
CS/CNS/EE 156 - Learning Systems
This course covers the theory, algorithms, and applications of machine learning (a.k.a. computational learning or statistical learning, with significant overlap with data mining and pattern recognition). It is a subject that combines mathematical theory with heuristic techniques, and it is one of the most widely applicable subjects in engineering and scientific research as well as in practical applications from computational finance to recommender systems to medical applications to robotics, among other fields.
The technical topics covered include linear models, theory of generalization, regularization and validation, Occam's razor and data snooping, neural networks, support vector machines, as well as specialized techniques and a term-long project with huge dataset.
This course has more than 800 alumni from 15 different majors
CS/EE/Ma 129 - Information and Complexity
This novel course covers information theory and computational complexity in a unified way. It develops the subject from first principles, building up from the basic premise of information to Shannon's information theory, and from the basic premise of computation to Turing's theory of computation. The duality between the two theories leads naturally to the theory of Kolmogorov complexity.
The technical topics covered include source coding, channel coding, rate-distortion theory, Turning machines, computability, computational complexity, and algorithmic entropy, as well as specialized topics and projects.
The course emphasizes the basic understanding of the subject that enables the students to use the notions of information and complexity in their own research work. There are complete notes for the course that are made available to registered students only. No other text book is needed.
This course has more than 1,000 alumni
- Here is the most recent magazine article about Professor Abu-Mostafa's teaching among other Feynman awardees, which appeared in Engineering & Science in the Fall of 2011.
- Here is an older, more comprehensive article in Caltech News.