Information and Data Sciences (IDS) Graduate Courses (2022-23)
EE/Ma/CS/IDS 127.
Error-Correcting Codes.
9 units (3-0-6):
third term.
Prerequisites: EE 55 or Ma 3.
This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission or storage systems. Topics include highly symmetric linear codes, such as Hamming, Reed-Muller, and Polar codes; algebraic block codes, e.g., BCH, Reed-Solomon (including a self-contained introduction to the theory of finite fields); and sparse graph codes with iterative decoding, i.e., LDPC code and turbo codes. Students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes.
Not offered 2022-23.
EE/Ma/CS/IDS 136.
Information Theory and Applications.
9 units (3-0-6):
third term.
Prerequisites: EE 55 or equivalent.
This class introduces information measures such as entropy, information divergence, mutual information, information density, and discusses the relations of those quantities to problems in data compression and transmission, statistical inference, and control. The course does not require a prior exposure to information theory; it is complementary to EE 126 a.
Instructor: Kostina.
Ma/ACM/IDS 140 ab.
Probability.
9 units (3-0-6):
second, third terms.
Prerequisites: For 140 a, Ma 108 b is strongly recommended.
Overview of measure theory. Random walks and the Strong law of large numbers via the theory of martingales and Markov chains. Characteristic functions and the central limit theorem. Poisson process and Brownian motion. Topics in statistics.
Instructors: Russkikh, Tamuz.