The Professional Master’s Program (PMP) is flexible enough to accommodate either part-time and full-time study. Students generally take one or two evening classes per quarter as well as a quarterly seminar. Summer quarter is optional and provides an opportunity for students to accelerate through the program. Course offerings for the current academic year are below. You may also wish to peruse the archive of past years’ offerings.
2018–2019 Course Offerings — TENTATIVE
- EE P 545 — The Self Driving Car: Introduction to AI for Mobile Robots
- EE P 502 — Analytical Methods for Electrical Engineering (Sosnovskaya)
- EE P 524 — Applied High Performance GPU Computing (Reinhardt)
- EE P 547 — Linear Systems Theory (Bushnell)
- EE P 560 — Machines & Drives (Nagel)
- EE 590 — Embedded and Real Time Systems (Sloss)
- EE 596 — Intro to Machine Learning (Bilmes)
- EE 590 — Software Engineering for Embedded Applications (Klavins)
- EE 595 — Wireless Networks for 4G/ 5G (Roy and Henderson)
- EE 579 —Computational Electromagnetics (Sahr)
- EE 553 — Power System Economics (Kirschen)
- EE 590 — Mobile Applications for Sensing and Control (Arjona)
- EE 527 — Microfabrication (Khbeis)
- EE 518 — Digital Signal Processing (Das)
- EE 579 — Antennas for Modern Wireless Devices (Kuga)
- EE 596 — Deep Learning (Bilmes)
- EE 559 — Data Science for Power Systems (Zhang)
- EE 504 — Introduction to Microelectro Mechanical Systems (Chen)
Our PMP courses are at the cutting-edge, positioning graduates for top careers in a variety of areas, including AI, machine learning and security, to name a few. In upcoming academic years we’ll be adding the following courses so that students remain at the forefront of electrical engineering expertise.
Data Science for Power Systems
Covers data science applications for power systems operations and control. Focuses on the management and analytics of multi-domain multi-resolution data (PMUs, SCADA, weather, renewables, customer load), especially on understanding how to integrate advanced data science tools with legacy physical infrastructures.
The Self-Driving Car – Introduction to AI for Mobile Robots
Provides an introduction to control, perception, and state estimation for mobile robots. Reviews the implementation of algorithms that allow robots to autonomously navigate through their environment. Applies concepts learned in lecture to a mini race car platform in order to develop a self-driving vehicle.
Machine Learning for Big Visual Data
Introduces useful features and distance measures associated with big visual data. Covers unsupervised learning and supervised machine learning, neural network and deep learning, as well as the reinforcement learning approaches. Addresses hidden Markov model to address temporal visual data. Explores machine learning techniques with applications to image object detection and recognition, as well as application to video object segmentation and tracking.
Wireless Networking for 5G
Interweaves core network design with its implementation on the most popular open source network simulator ns-3 (www.nsnam.org) hosted at the University of Washington. Reviews basic network operation and optimization for the 2 key wireless standard families: 802.11 WLANs and LTE/LTE-Advanced/LTE-Advanced+ via a set of structured experiments using ns-3. Students will conduct a 5G oriented project involving a emerging 5G design scenario (Internet of Things, Heterogeneous Networking, Vehicular Networks, Mobile Edge Cloud etc.) to be evaluated via ns-3.