UW Bothell Course Descriptions
UW Tacoma Course Descriptions
Glossary
Detailed course offerings (Time Schedule) are available for
EE P 500 Graduate Seminar (1-3, max. 9)
Weekly seminars on current topics in electrical engineering. Credit/no-credit only. Offered: AWSp.
View course details in MyPlan: EE P 500
EE P 502 Analytical Methods for Electrical Engineering (4)
Applications of analytical and mathematical methods for electrical engineering, including: MATLAB, continuous-time signals and linear systems, Fourier series and Fourier transform, discrete-time signals and linear systems, linear algebra, and probability and stochastic processes.
View course details in MyPlan: EE P 502
EE P 504 Introduction to Microelectro Mechanical Systems (4)
Theoretical and practical aspects in design, analysis, and fabrication of MEMS devices. Fabrication processes, including bulk and surface micromachining. MEMS design and layout. MEMS CAD tools. Mechanical and electrical design. Applications such as micro sensors and actuators, or chemical and thermal transducers, recent advances. Cannot be taken for credit if credit received for E E 504.
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EE P 518 Digital Signal Processing (4)
Discrete-time processing of continuous-time signals; sampling rate conversion; frequency magnitude, phase delay, and group delay; design techniques for non-recursive (FIR) filters; multirate signal processing; all-pass/minimum phase decompositions; discrete Fourier transforms, fast Fourier transforms; overlap-add; short-time Fourier analysis; and filter banks. Includes applications such as machine learning. Recommended: Cannot be taken for credit if credit received for E E 518.
View course details in MyPlan: EE P 518
EE P 520 Software Engineering for Embedded Applications (4)
Fundamentals of programming languages and software engineering common to all levels of embedded systems programming. Reviews C++ and similar languages commonly used for embedded systems, and how to use build tools, version control, and advanced editors. Data structures and algorithms common to embedded systems, such as schedulers, event loops, finite state machines, sensor models, real-time constraints, and power management will be explored.
View course details in MyPlan: EE P 520
EE P 522 Embedded and Real Time Systems (4)
Characterization of embedded hardware and software through practical exploration. Covers a specific hardware platform, system software, computation limits, architecture analysis, and physical world interaction. Introduces power management, reliability, safety-critical systems and simulation.
View course details in MyPlan: EE P 522
EE P 523 Mobile Applications for Sensing and Control (4)
Development of mobile applications that make use of the sensing and control capabilities of modern smartphones; programming concepts for mobile application development; extraction and interpretation of sensor data from sensors on and off the phone; simple control based on sensor data.
View course details in MyPlan: EE P 523
EE P 524 Applied High-Performance GPU Computing (4)
The efficient formulation of complex math/scientific/engineering problems using the parallel language(s)/API(s) of GPU compute code and their performance analysis. Covers design considerations including basic GPU kernel design, memory and cache optimization and analysis, work efficiency, and floating-point considerations. Includes applied topics such as hands-on kernel debugging, timing and profiling, and error handling techniques.
View course details in MyPlan: EE P 524
EE P 527 Microfabrication (4)
Principles and techniques for the fabrication of microelectronics devices and integrated circuits. Includes clean room laboratory practices and chemical safety, photolithography, wet and dry etching, oxidation and diffusion, metallization and dielectric deposition, compressed gas systems, vacuum systems, thermal processing systems, plasma systems, and metrology. Extensive laboratory with limited enrollment. Recommended: Cannot be taken for credit if credit received for E E 527.
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EE P 532 Design of Digital and Analog Electronic Circuits (4)
The design of (a) CMOS digital logic circuits, both combinational and sequential, from gate level to shift-register level, and (b) MOS analog circuits from simple amplifiers to differential and more complex amplifiers. Emphasizes design methods and simulation tools, using simplistic device models for design understanding and circuit performance characterization.
View course details in MyPlan: EE P 532
EE P 545 The Self Driving Car: Introduction to AI for Mobile Robots (4)
State estimation (particle filters, motion models, sensor models), planning/control (search based planners, lattice based planners, trajectory following techniques), and perception and learning (object detection, learning from demonstrations) for mobile robots. Implementation of algorithms that allow robots to autonomously navigate through their environment. Applies concepts to a mini race car platform.
View course details in MyPlan: EE P 545
EE P 547 Linear Systems Theory (4)
Transfer function and state-space models, linearization, causality, time invariance, LTV and LTI systems, impulse response, step response, frequency response, Bode Plots, stability, controllability and observability, LQR controllers, state-variable feedback, state observers, and PID control. Cannot be taken for credit if credit received for E E 547.
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EE P 553 Power Systems Economics (4)
Economic structure of power systems. Problem formulation, optimization methods and programming for economic analysis of power system operation and planning. Economic dispatch, load forecasting, unit commitment, interchange, planning and reliability analysis. Provides background to pursue advanced work in planning and operation. Cannot be taken for credit if credit received for E E 553.
View course details in MyPlan: EE P 553
EE P 555 Data Science for Power Systems (4)
Data science applications for power systems operations and control. Management and analytics of multi-domain multi-resolution data and integration of data science tools with physical operations.
View course details in MyPlan: EE P 555
EE P 560 Advanced Electric Machines and Drives (4)
Fundamentals of electric machines and drives, including brush DC, brushless DC, PM synchronous, induction machines, inverter topologies, and control techniques. Evaluation of torque production and control in machines with electronic drives (motor controllers). Offered: A.
View course details in MyPlan: EE P 560
EE P 567 Machine Learning for Cybersecurity (4)
Hands-on course on the use of machine learning for cybersecurity applications. Identification of machine learning algorithms that are useful for specific security applications. Defense against attacks and anticipation of future attack variants. Anomaly detection. Supervised and unsupervised approaches, clustering techniques, ensemble learning, decision trees, and time series modeling applied to cybersecurity data. Adversarial machine learning. Recommended: foundational knowledge of probability and random processes; signal processing; and Python programming.
View course details in MyPlan: EE P 567
EE P 568 Deep Learning for Big Visual Data (4)
Provides students with fundamental skills and hands-on experience in applying deep learning theories to various image, video, radar, and lidar processing applications based on cloud-based CPU/GPU computing resources. Includes experience using real world training and testing data, and analytical derivation. Recommended: experience with programming; and background knowledge in linear algebra, image processing, computer vision, or statistical data analysis.
View course details in MyPlan: EE P 568
EE P 569 Wireless Networks for 4G/5G (4)
Introduction to selected topics in 4G/5G oriented wireless communication networks. Reviews principles and design fundamentals of two major broadband wireless network technology standard families: 802.11 WLANs and LTE/LTE-Advanced. Utilizes the open-source ns-3 network simulator via a set of experiments using existing basic wireless, 802.11 and LTE protocol stack implementations in ns-3.
View course details in MyPlan: EE P 569
EE P 590 Advanced Topics in Digital Computers (1-5, max. 16)
Topics of current interest in the field of digital systems.
View course details in MyPlan: EE P 590
EE P 592 Advanced Topics in Electromagnetics, Optics, and Acoustics (1-5, max. 16)
Topics of current interest in electromagnetics, optics, and acoustics.
View course details in MyPlan: EE P 592
EE P 595 Advanced Topics in Communication Theory (1-5, max. 16)
Topics of current interest in Communication Theory.
View course details in MyPlan: EE P 595
EE P 596 Advanced Topics in Signal and Image Processing (1-5, max. 16)
Topics of current interest in signal and image processing.
View course details in MyPlan: EE P 596
EE P 598 Advanced Topics in Electrical and Computer Engineering (4, max. 16)
Topics of current interest in electrical and computer engineering.
View course details in MyPlan: EE P 598
EE P 599 Research in Electrical Engineering (1-4, max. 8)
Prerequisite: permission of instructor. Offered: AWSpS.
View course details in MyPlan: EE P 599
