Information and Data Sciences (IDS) Graduate Courses (2023-24)
EE/Ma/CS/IDS 127.
Error-Correcting Codes.
9 units (3-0-6):
third term.
Prerequisites: EE 55 or equivalent.
This course develops from first principles the theory and practical implementation of the most important techniques for combating errors in digital transmission and storage systems. Topics include highly symmetric linear codes, such as Hamming, Reed-Muller, and Polar codes; algebraic block codes, such as Reed-Solomon and BCH codes, including a self-contained introduction to the theory of finite fields; and low-density parity-check codes. Students will become acquainted with encoding and decoding algorithms, design principles and performance evaluation of codes.
Instructor: Kostina.
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 establishes the fundamental importance of those measures in data compression, statistical inference, and error control. The course does not require a prior exposure to information theory; it is complementary to EE 126a.
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.
Part b not offered 2023-24.
Instructor: Vigneaux.