Courses

Introduction to electrostatics and magnetostatics; properties of conductive, dielectric, and magnetic materials; solutions of Maxwell's equations; frequency- and time-domain analysis transmission lines; uniform plane wave applications.

Probability, random variables, statistics, and random processes including counting, independence, conditioning, expectation, density functions, distributions, law of large numbers, central limit theorem, confidence intervals, hypothesis testing, statistical estimation, stationary processes, Markov chains, and ergodicity. In most sections, examinations are given on Wednesday nights; see the Course Schedule for more information.

Communication channels and their impairments; modulation; demodulation; probability of error analysis; source coding; error control coding; link budget analysis; equalization; synchronization and multiple access; spread spectrum; applications in wireline and wireless communication systems.

Circuit and packet-switched networks; local area networks; protocol stacks; ATM and broadband ISDN; Internet; routing, congestion control, and performance evaluation; multimedia applications.

Local, metropolitan, and wide-area operations; telecommunication common carrier organization and services; economic, administrative, and political considerations; premise distribution systems; name resolution, address assignment, and mail; datagrams, packets, frames, and cells; addressing and network-level interconnection; inter-network architecture; TCP/IP protocol suite (version 4 and 6); Ethernet and IEEE 802.3 standards; repeaters, hubs, bridges, routers; local area network emulation; public switched network access through POTS and ISDN; intra-domain and inter-domain routing; routing pr

Analyzing large data sets for interesting and useful information. Includes online analytical processing, finding association rules, clustering, classification, and function approximations. Scalability of algorithms and real-life applications.

Higher-level languages for engineering design and problem solving; object-oriented programming in C++ and Unix systems programming.

Pattern recognition topics, including Bayesian decision theory, maximum likelihood and estimation, nonparametric techniques, and linear discriminant functions. Computer vision topics, including geometric camera models and calibration, geometry of multiple views and stereopsis, structure from motion, and tracking. Emphasis varies each semester.

Feed-forward networks, distributed associative memory, recurrent networks, self-organization, parallel implementation, and applications. 

Stochastic and deterministic traffic and queueing models. Techniques for call admission, routing, flow control, network optimization, estimation, and decision making in uncertain environments. Additional prerequisite: Electrical Engineering 381J and 382N (Topic 5: Communication Networks: Technology, Architectures, and Protocols). 

Characterization of communication signals and systems (bandpass signals and systems, signal space representation, digitally modulated signals, and spectral characteristics), optimum receivers for additive white Gaussian noise (correlation demodulator, matched-filter demodulator, performance for binary and M-ary modulation, and noncoherent receivers), error control codes (block and convolutional), and bandlimited channels (ISI and equalization, MLSE, and OFDM). Additional prerequisite: Electrical Engineering 351K, 351M, and 360K. 

Introduction to the fundamentals of estimation theory, with applications to stochastic and adaptive signal processing. Topics include deterministic and stochastic least-squares estimation; the innovation process; spectral factorization and Wiener filtering; state-space structure and Kalman filters; array and fast array algorithms; LMS and RLS adaptive filters; Markov chain Monte Carlo methods; Bayesian filtering and particle filters; parameter estimation; expectation-maximization algorithm; Cramer-Rao bounds.

Signals and systems; generalized functions; z-transforms; Fourier series and transforms; fast Fourier transform; sampling, quantization, and aliasing; digital filter design; discrete-time random processes; multirate processing; filter banks and subband decomposition; nonlinear digital filters. Additional prerequisite: Electrical Engineering 351K and 351M.

Multiple-input multiple-output (MIMO) wireless communication, including discrete-time signal models, equalization, and channel estimation; channel models; channel capacity; average probability of error in fading channels; channel coding; transmit and receive diversity; space-time codes; spatial multiplexing; precoding and limited feedback; space-time adaptation; multiuser communication; multiuser information theory; practical multiuser algorithms; and applications in recent standards. Three lecture hours a week for one semester.

This is the second course in a two-course sequence on Large-Scale Optimization and Learning. While the first course (EE381V-11a) focused on convex optimization, with an emphasis on methods for large-scale problems, this course will focus on drawing inference from data - machine learning techniques, with a focus on methods for problems of large size and high dimensionality. Intended audience: This class is structured to be interesting and relevant to students who are using or plan to use machine learning in their research, and are interested in solving large-scale problems.

This is a research-level graduate class geared towards advanced graduate students. The topics change from year to year, to reflect latest developments in the field, exciting topics, and topics of most interest. In the past, the first half of the course has focused on Convex Analysis, developing the geometry of convexity and proving the basic duality results of convex sets and then convex functions. The goal is to impart an appreciation of the questions, tools, and techniques central to convex analysis, to enable the student to go on to read the foundational texts on the topic.

Concurrent programming languages, distributed algorithms, distributed operating systems, distributed data, formal models of concurrency, protection and security in computer networks. 

Design of computer arithmetic units: fast adders, fast multipliers, dividers, and floating-point arithmetic units.

Hardware and software parallelism and locality mechanisms, and their impact on processor performance, bandwidth, and power requirements; architectures and microarchitectures of throughput-oriented processors that rely on parallelism, locality, and hierarchical control; parallel memory systems; and streaming and bulk execution and programming models. Includes programming and measuring performance on massively parallel processors. Electrical Engineering 382N (Topic 20) and 382V (Topic: Principles of Computer Architecture) may not both be counted.

Network services and techniques, layered architectures, circuit and packet-switching networks, internetworking, switch architectures, control mechanisms, and economic issues.