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1 - 6 of 6 results for: EE178

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; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery 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.
Last offered: Winter 2022

EE 178: Probabilistic Systems Analysis

Introduction to probability and its role in modeling and analyzing real world phenomena and systems, including topics in statistics, machine learning, and statistical signal processing. Elements of probability, conditional probability, Bayes rule, independence. Discrete and continuous random variables. Signal detection. Functions of random variables. Expectation; mean, variance and covariance, linear MSE estimation. Conditional expectation; iterated expectation, MSE estimation, quantization and clustering. Parameter estimation. Classification. Sample averages. Inequalities and limit theorems. Confidence intervals. Prerequisites: Calculus at the level of MATH 51, CME 100 or equivalent and basic knowledge of computing at the level of CS106A.
Terms: Aut, Spr | Units: 3-4 | UG Reqs: GER:DB-EngrAppSci, WAY-AQR, WAY-FR

EE 274: Data Compression: Theory and Applications

The course focuses on the theory and algorithms underlying modern data compression. The first part of the course introduces techniques for entropy coding and for lossless compression. The second part covers lossy compression including techniques for multimedia compression. The last part of the course will cover advanced theoretical topics and applications, such as neural network based compression, distributed compression, and computation over compressed data. Prerequisites: basic probability and programming background ( EE178, CS106B or equivalent), a course in signals and systems ( EE102A), or instructor's permission.
Terms: Aut | Units: 3

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 | Units: 3

EE 374: Blockchain Foundations

A detailed exploration of the foundations of blockchains, What blockchains are, how they work, and why they are secure. Transactions, blocks, chains, proof-of-work and stake, wallets, the UTXO model, accounts model, light clients. Throughout the course, students build their own nodes from scratch. Security is defined and rigorously proved. The course is heavy on both engineering and theory. This course is a deeper investigation into the consensus layer of blockchains while CS 251 is a broader investigation, and it can be taken with or without having taken CS 251. Prerequisites: CS106 or equivalent, significant programming experience; CS103 or equivalent; CS109 or EE178 or equivalent.
Terms: Win | Units: 3

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; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery 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.
Last offered: Winter 2022
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