## CS 250: Error Correcting Codes: Theory and Applications (EE 387)

Introduction to the theory of error correcting codes, emphasizing diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; constructions like Reed-Solomon, Reed-Muller, and expander codes; list-decoding, list-recovery and locality. Applications include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and com-pressed 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: Aut
| Units: 3

Instructors:
Wootters, M. (PI)
;
Deligiannis, P. (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:DB-EngrAppSci

## EE 257: Applied Optimization Laboratory (Geophys 258) (GEOPHYS 258)

Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of large-scale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.

Last offered: Winter 2013

## 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

Instructors:
Ozgur Aydin, A. (PI)
;
Reshetova, D. (PI)
;
Tse, D. (PI)
...
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Instructors:
Ozgur Aydin, A. (PI)
;
Reshetova, D. (PI)
;
Tse, D. (PI)
;
Barnes, L. (TA)
;
Fisher, J. (TA)
;
Zhang, M. (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

## EE 387: Error Correcting Codes: Theory and Applications (CS 250)

Introduction to the theory of error correcting codes, emphasizing diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; constructions like Reed-Solomon, Reed-Muller, and expander codes; list-decoding, list-recovery and locality. Applications include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and com-pressed 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: Aut
| Units: 3

Instructors:
Wootters, M. (PI)
;
Deligiannis, P. (TA)

## GEOPHYS 258: Applied Optimization Laboratory (Geophys 258) (EE 257)

Application of optimization and estimation methods to the analysis and modeling of large observational data sets. Laboratory exercises using inverse theory and applied linear algebra to solve problems of indirect and noisy measurements. Emphasis on practical solution of scientific and engineering problems, especially those requiring large amounts of data, on digital computers using scientific languages. Also addresses advantages of large-scale computing, including hardware architectures, input/output and data bus bandwidth, programming efficiency, parallel programming techniques. Student projects involve analyzing real data by implementing observational systems such as tomography for medical and Earth observation uses, radar and matched filtering, multispectral/multitemporal studies, or migration processing. Prequisites: Programming with high level language. Recommended:
EE261,
EE263,
EE178, ME300 or equivalent.

Last offered: Winter 2013

## 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

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