printer friendly pageEE 364A: Convex Optimization I (CME 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Win, Sum
| Units: 3
Instructors:
Ayazifar, B. (PI)
;
Boyd, S. (PI)
;
Ogut, G. (PI)
;
Bell, L. (TA)
;
Hofgard, J. (TA)
;
Holt, G. (TA)
;
Johansson, K. (TA)
;
Parshakova, T. (TA)
;
Tse, D. (TA)
;
Yang, A. (TA)
EE 364B: Convex Optimization II (CME 364B)
Continuation of 364A. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Applications in areas such as control, circuit design, signal processing, and communications. Course requirements include project. Prerequisite: 364A.
Terms: Spr
| Units: 3
Instructors:
Pilanci, M. (PI)
;
Mishkin, A. (TA)
EE 364M: Mathematics of Convexity
This course covers the elegant mathematical underpinnings of convex optimization, with a focus on those analytic techniques central to the successes of the field. Topics include, but are not limited to, convex sets and functions, separation theorems, duality, set-valued analysis, and the mathematical insights central to the development of modern optimization methods. Pre- or co-requisite:
EE364A, and mathematical analysis at the level of
MATH171.
Terms: Win
| Units: 1
Instructors:
Duchi, J. (PI)
EE 367: Computational Imaging (CS 448I)
Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended:
EE261,
EE263,
EE278.
Terms: Win
| Units: 3
EE 369A: Medical Imaging Systems I (BMP 269A)
Imaging internal structures within the body using high-energy radiation and ultrasound, studied from a systems viewpoint. Modalities covered: x-ray, computed tomography, nuclear medicine, and ultrasound. Review of linear signals and systems, Fourier transforms, random variables, and noise. Analysis of existing and proposed systems in terms of resolution, frequency response, detection sensitivity, noise, and potential for improved diagnosis. This course covers Fourier transform basics and serves as an alternative prerequisite to
EE 261 for
EE 369B. Prerequisite:
EE 102A (undergraduate-level signals and systems) or similar.
Terms: Win
| Units: 3
EE 369B: Medical Imaging Systems II (BMP 269B)
Imaging internal structures within the body using magnetic resonance studied from a systems viewpoint. Analysis of magnetic resonance imaging systems including physics, Fourier properties of image formation, effects of system imperfections, image contrast, and noise. Pre- or corequisite:
EE 261 or equivalent
Terms: Spr
| Units: 3
EE 370: Reinforcement Learning: Behaviors and Applications (MS&E 237B)
This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in
EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites:
EE277.
Terms: Win
| Units: 3
Instructors:
Van Roy, B. (PI)
;
Kagrecha, A. (TA)
EE 377: Information Theory and Statistics (STATS 311)
Information theoretic techniques in probability and statistics. Fano, Assouad,nand Le Cam methods for optimality guarantees in estimation. Large deviationsnand concentration inequalities (Sanov's theorem, hypothesis testing, thenentropy method, concentration of measure). Approximation of (Bayes) optimalnprocedures, surrogate risks, f-divergences. Penalized estimators and minimumndescription length. Online game playing, gambling, no-regret learning. Prerequisites:
EE 276 (or equivalent) or
STATS 300A.
Terms: Aut
| Units: 3
EE 379A: Data Transmission Design
Data Transmission Design is the first of a two-quarter sequence (leading to
EE379B) in MSEE communications depth sequence. Intended students are those interested in research or design of data transmission systems' lower layers. The course includes methods for transmission designs with and without coding and includes basic examples as well as their relationship to modern current/next-generation wireless and wireline transmission systems. The course also develops and uses information measures as generalizations of signal processing and minimum-mean-square-error estimation, developing design intution. Basic phase-locking and synchronization methods also appear. EE379B progresses to multidimensional modulation methods and their use in modern and next-generation multiuser MIMO networks, along with network-design strategies. Prerequisites: EE102B and
EE278 (or equivalents). EE279 is helpful but not required.
Terms: Win
| Units: 3
Instructors:
Cioffi, J. (PI)
EE 379B: Advanced Data Transmission Design
EE 379B follows 379A and focuses on state-of-the-art data communication system theory and design, particularly systems with multiple users and dimensions (MIMO over parallel antennas or wires). The focus is on multi-user physical-layer channels like multiple access, broadcast, and interference channels, their capacity regions and designs to achieve any points therein. Examples include the latest cellular, Wi-Fi, wireline, cable, and other systems that stress fundamental transmission limits. Topics include system design, particularly physical-layer modulation/coding analysis and optimization through various artificial intelligence and optimization methods for multi-dimensional channels. Included are methods to design and adapt both transmitter and receiver to variable channels. Prerequisites:
EE 278, linear algebra,
EE 279 or
EE 379A (
or 379), or instructor consent. Instructor: Cioffi
Terms: Spr
| Units: 3
Instructors:
Cioffi, J. (PI)
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