Information and Data Sciences (IDS) Undergraduate Courses (2024-25)
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.
not offered 2024-25.
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 abc.
Probability.
9 units (3-0-6):
first, second, third terms.
Prerequisites: For 140 a, Ma 108 b is strongly recommended.
This course begins with an overview of measure theory, followed by topics that include random walks, the strong law of large numbers, the central limit theorem, martingales, Markov chains, characteristic functions, Poisson processes, and Brownian motion. Towards the end, some further topics may be covered, such as stochastic calculus, stochastic differential equations, Gaussian processes, random graphs, Markov chain mixing, random matrix theory, and interacting particle systems.
Instructors: Tamuz, El-Maazouz, Zhang.