CS 250: Algebraic Error Correcting Codes (EE 387)
Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; ReedSolomon and ReedMuller codes; listdecoding, listrecovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178) and "mathematical maturity" (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wootters, M. (PI)
;
Hulett, R. (TA)
EE 178: Probabilistic Systems Analysis
Introduction to probability and statistics and their role in modeling and analyzing real world phenomena. Events, sample space, and probability. Discrete random variables, probability mass functions, independence and conditional probability, expectation and conditional expectation. Continuous random variables, probability density functions, independence and expectation, derived densities. Transforms, moments, sums of independent random variables. Simple random processes. Limit theorems. Introduction to statistics: significance, estimation and detection. Prerequisites: basic calculus.
Terms: Aut, Spr

Units: 4

UG Reqs: GER:DBEngrAppSci

Grading: Letter or Credit/No Credit
Instructors:
Ozgur Aydin, A. (PI)
;
Weissman, T. (PI)
;
Chandak, S. (TA)
...
more instructors for EE 178 »
Instructors:
Ozgur Aydin, A. (PI)
;
Weissman, T. (PI)
;
Chandak, S. (TA)
;
Kalkanli, C. (TA)
;
Reshetova, D. (TA)
EE 278: Introduction to Statistical Signal Processing
Review of basic probability and random variables. Random vectors and processes; convergence and limit theorems; IID, independent increment, Markov, and Gaussian random processes; stationary random processes; autocorrelation and power spectral density; mean square error estimation, detection, and linear estimation. Formerly
EE 278B. Prerequisites: EE178 and linear systems and Fourier transforms at the level of
EE102A,B or
EE261.
Terms: Aut, Spr, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Ghalayini, A. (PI)
;
Prabhakar, B. (PI)
;
Weissman, T. (PI)
...
more instructors for EE 278 »
Instructors:
Ghalayini, A. (PI)
;
Prabhakar, B. (PI)
;
Weissman, T. (PI)
;
FischerHwang, I. (TA)
;
Teamangkornpan, P. (TA)
EE 376A: Information Theory (STATS 376A)
The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178 or
STATS 116, or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
EE 387: Algebraic Error Correcting Codes (CS 250)
Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; ReedSolomon and ReedMuller codes; listdecoding, listrecovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing. Prerequisites: Linear algebra, basic probability (at the level of, say,
CS109, CME106 or
EE178) and "mathematical maturity" (students will be asked to write proofs). Familiarity with finite fields will be helpful but not required.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Wootters, M. (PI)
;
Hulett, R. (TA)
STATS 376A: Information Theory (EE 376A)
The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and Kelly gambling. Applications to communication and data compression. Prerequisite: EE178 or
STATS 116, or equivalent.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
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