printer friendly pageEE 260A: Principles of Robot Autonomy I (AA 274A, CS 237A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3
Instructors:
Schwager, M. (PI)
;
Agha-Oko, M. (TA)
;
Manhas, A. (TA)
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Instructors:
Schwager, M. (PI)
;
Agha-Oko, M. (TA)
;
Manhas, A. (TA)
;
Nagami, K. (TA)
;
Singh, S. (TA)
;
Vincent, J. (TA)
;
Yu, J. (TA)
EE 263: Introduction to Linear Dynamical Systems (CME 263)
Applied linear algebra and linear dynamical systems with applications to circuits, signal processing, communications, and control systems. Topics: least-squares approximations of over-determined equations, and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm, and singular-value decomposition. Eigenvalues, left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices; convolution and transfer-matrix descriptions. Control, reachability, and state transfer; observability and least-squares state estimation. Prerequisites: Linear algebra and matrices as in
ENGR 108 or
MATH 104; ordinary differential equations and Laplace transforms as in
EE 102B or
CME 102.
Terms: Aut, Sum
| Units: 3
EE 268: The Engineering Economics of Electricity Markets (ECON 261)
This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performanc
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This course presents the power system engineering and economic concepts necessary to understand the costs and benefits of transitioning to a low carbon electricity supply industry. The technical characteristics of generation units and transmission and distribution networks as well as the mechanisms used to operate the electricity supply industries will be studied. The fundamental economics of wholesale markets and how intermittent renewables impact the price and quantity of physical and financial products traded in these markets (e.g., energy, capacity, ancillary services, and financial contracts) will be analyzed. Long-term resource adequacy mechanisms will be introduced and their properties analyzed. The role of both short-duration and seasonal energy storage will be analyzed. Mechanisms for determining the engineering and economic need for transmission network expansions in a wholesale market will be discussed. The impact of distributed versus grid-scale generation on the performance of electricity supply industries will be studied. A detailed treatment of electricity retailing will focus on the importance of active demand-side participation in a low carbon energy sector. This course uses knowledge of probability at the level of
Stats 116, optimization at the level of MS&E 111, statistical analysis at the level of Economics 102B, microeconomics at the level of Economics 51 and computer programming in R.
Terms: Aut
| Units: 3
Instructors:
Wolak, F. (PI)
;
Naumann, R. (TA)
EE 269: Signal Processing for Machine Learning
This course will introduce you to fundamental signal processing concepts and tools needed to apply machine learning to discrete signals. You will learn about commonly used techniques for capturing, processing, manipulating, learning and classifying signals. The topics include: mathematical models for discrete-time signals, vector spaces, Fourier analysis, time-frequency analysis, Z-transforms and filters, signal classification and prediction, basic image processing, compressed sensing and deep learning. This class will culminate in a final project. Prerequisites:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab).
ENGR 108 and
EE 178 are recommended.
Terms: Aut
| Units: 3
EE 271: Introduction to VLSI Systems
Provides a quick introduction to MOS transistors and IC fabrication and then creates abstractions to allow you to create and reason about complex digital systems. It uses a switch resistor model of a transistor, uses it to model gates, and then shows how gates and physical layout can be synthesized from Verilog or SystemVerilog descriptions. Most of the class will be spent on providing techniques to create designs that can be validated, are low power, provide good performance, and can be completed in finite time. Prerequisites: 101A and 108; familiarity with transistors, logic design, Verilog and digital system organization
Terms: Aut
| Units: 3
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 277: Bandit Learning: Behaviors and Applications (MS&E 237A)
The subject of reinforcement learning addresses the design of agents that improve decisions over time while operating within complex and uncertain environments. This first course of the sequence restricts attention to the special case of bandit learning, which focuses on environments in which all consequences of an action are realized immediately. This course covers desired agent behaviors and principled scalable approaches to realizing such behavior. Topics include learning from trial and error, exploration, contextualization, generalization, and representation learning. Motivating examples will be drawn from recommendation systems, crowdsourcing, education, and generative artificial intelligence. Homework assignments primarily involve programming exercises carried out in Colab, using the python programming language and standard libraries for numerical computation and machine learning. Prerequisites: programming (e.g.,
CS106B), probability (e.g., MS&E 121,
EE 178 or
CS 109), machine learning (e.g.,
EE 104/
CME 107, MS&E 226 or
CS 229).
Terms: Aut
| Units: 3
Instructors:
Van Roy, B. (PI)
;
Kagrecha, A. (TA)
EE 278: Probability and Statistical Inference
Many engineering applications require efficient methods to process, analyze, and infer signals, data and models of interest that are best described probabilistically. Building on a first course in probability (such as EE178 or equivalent), this course introduces more advanced topics in probability such as concentration inequalities, random vectors and random processes, and explores their applications in statistics, machine learning and signal processing. Specific applications include hypothesis testing and classification; dimensionality reduction and generalization in machine learning, minimum mean square error estimation and Kalman filtering. Prerequisites: EE178 or equivalent
Terms: Aut
| Units: 3
Instructors:
Ozgur, A. (PI)
;
Song, D. (TA)
EE 284: Introduction to Computer Networks
Structure and components of computer networks; functions and services; packet switching; layered architectures; OSI reference model; physical layer; data link layer; error control; window flow control; media access control protocols used in local area networks (Ethernet, Token Ring, FDDI) and satellite networks; network layer (datagram service, virtual circuit service, routing, congestion control, Internet Protocol); transport layer (UDP, TCP); application layer.
Terms: Aut
| Units: 3
Instructors:
Tobagi, F. (PI)
EE 290A: Curricular Practical Training for Electrical Engineers
For EE majors who need work experience as part of their program of study. Final report required. Prerequisites: for 290B, EE MS and PhD students who have received a Satisfactory ("S") grade in
EE290A; for 290C, EE PhD degree candidacy and an "S" grade in
EE 290B; for 290D, EE PhD degree candidacy, an "S" grade in
EE 290C and instructor consent.
Terms: Aut, Win, Spr, Sum
| Units: 1
Instructors:
Osgood, B. (PI)
;
Solgaard, O. (PI)
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