Courses
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.
2024-2025 Course Offerings
Autumn 2024 (TENTATIVE)
- The Self-Driving Car: Intro to AI for Mobile Robots (Smith)
- Data Structures and Algorithms for ECE Applications (Slaughter)
- Deep Learning for Big Visual Data (Hwang)
- Radar Signals and Systems (Reynolds)
- Analytical Methods for Electrical Engineering (Bonaci)
- Digital Systems Design with FPGA (Makhsous)
- Applied High Performance GPU Computing (Reinhardt)
- Computer Vision: Deep and Classical Methods (Birchfield)
- Wireless Networks for 4G/ 5G (Yin)
- Network and Communication Security (TBD)
2024-2025 Course Offerings
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2023-2024 Course Offerings
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Autumn 2023
- The Self-Driving Car: Intro to AI for Mobile Robots (Smith)
- Data Structures and Algorithms for ECE Applications (Slaughter)
- Machines and Drives (Nagel)
- Deep Learning for Big Visual Data (Hwang)
- Microwave Engineering (Reynolds)
- Analytical Methods for Electrical Engineering (Bonaci)
- Digital Systems Design with FPGA (Makhsous)
- Optical Sensors and Devices (Li)
- Computer Vision: Deep and Classical Methods (Birchfield)
- Business Leadership for Engineers (Hamilton)
Winter 2024
- Large Language Models: From Transformers to ChatGPT (Mohan)
- Machine Learning for Cyber Security (Poovendran)
- Embedded and Real Time Systems (Sloss)
- Applied EM: How the Force of Maxwell’s Equations Drives Circuit Theory and the Rest of Life (Goldstein)
- Software Engineering for Embedded Applications (Makhsous)
- Analog Circuits for Sensor Systems (Hameed)
- Wireless Power (Smith)
- Electric Vehicle Development and Design (Cheng)
Spring 2024
- CHIPS Revolution: Semiconductor-based Diodes, Transistors, and Memory Devices (Anantram)
- Linear Systems Theory (Bushnell)
- Mobile Applications for Sensing and Control (Makhsous)
- Fundamentals of Wireless Communication (Ritcey)
- TinyML (Sahabandu)
- Signal Processing: Deep and Classical Methods (Atlas)
- Privacy-preserving Machine Learning (Bonaci)
- Antennas (Garman)
- Applied Parallel Programming on GPU (Sadasivan)
Summer 2024
- Introduction to Privacy Engineering (Bonaci)
- Engineering Project Management (Mamishev)
- Quantum Mechanics and Quantum Computing Basics for Engineers (Anantram)
- Entrepreneurship for Electrical and Computer Engineers: From Idea to Startup (Makhsous)
2022-2023 Course Offerings
Autumn 2022
- The Self-Driving Car: Intro to AI for Mobile Robots (Smith)
- Dynamics of Controlled Systems (Nagel)
- Deep Learning for Big Visual Data (Hwang)
- Applied High-Performance GPU Computing (Reinhardt)
- Radar Signals and Systems (Reynolds)
- Analytical Methods for Electrical Engineering (Bonaci)
- Computer Vision (Mohan)
- Digital Systems Design with FPGA (Makhsous)
Winter 2023
- Advanced Introduction to Machine Learning (Mohan)
- Machine Learning for Cyber Security (Poovendran)
- Embedded and Real Time Systems (Sloss)
- Applied EM: How the Force of Maxwell’s Equations Drives Circuit Theory
and the Rest of Life (Goldstein) - GPU-Accelerated Interactive Scientific Visualization Techniques
(SciVis) (Reinhardt) - Natural Language Processing (Bhagavatula)
- Model-based Representations for Systems Engineering (Kimberly)
- Software Engineering for Embedded Applications (Makhsous)
- Analog Circuits for Sensor Systems (Hameed)
Spring 2023
- Deep Learning for Embedded Real Time Intelligence (Shi)
- Data Science for Energy Systems (Zhang)
- GPU-Accelerated Computing + Visualization (Reinhardt)
- Microfabrication (Li)
- Neural Devices, Systems, and Computation (Herron)
- Antennas (Garman)
- Mobile Applications for Sensing and Control (Makhsous)
- TinyML (Poovendran)
- Privacy-preserving Machine Learning (Bonaci)
- Digital Signal Processing (Atlas)
Summer 2023
- Introduction to Privacy Engineering (Bonaci)
- Engineering Project Management (Mamishev)
- Quantum Mechanics and Quantum Computing Basics for Engineers (Anantram)
- Entrepreneurship for Electrical and Computer Engineers: From Idea to Startup (Makhsous)
2021-2022 Course Offerings
Autumn 2021
- Deep Learning for Big Visual Data (Hwang)
- The Self-Driving Car: Intro to AI for Mobile Robots (Smith)
- Computer Vision: Deep and Classical Methods (Birchfield)
- Microwave Engineering (Kuga)
- Optical Sensors and Systems (Li)
- Fundamentals of Robot Manipulation (Hannaford)
- Analytical Methods in Electrical Engineering (Bonaci)
- Analog Circuits for Sensor Systems I (Martinez)
Winter 2022
- Advanced Introduction to Machine Learning (Mohan)
- Machine Learning for Cyber Security (Poovendran)
- Antennas for Modern Wireless Devices (Kuga)
- Embedded and Real Time Systems (Sloss)
- Applied EM: How the Force of Maxwell’s Equations Drives Circuit Theory and the Rest of Life (Goldstein)
- Linear Systems Theory
- GPU-Accelerated Interactive Scientific Visualization Techniques (SciVis) (Reinhardt)
- Software Engineering for Embedded Applications (Bonaci)
Spring 2022
- Deep Learning for Embedded Real Time Intelligence (Shi)
- Data Science for Energy Systems (Zhang)
- Radar and Imaging Techniques (Kuga)
- Fundamentals of Wireless Communication (Ritcey)
- Digital Signal Processing (Atlas)
- Analog Circuits for Sensor Systems II (Dai)
- Introduction to Privacy Engineering (Bonaci)
- AI in Healthcare (Mohan)
- Mobile Applications for Sensing and Control (Slaughter)
Summer 2022
- Quantum Mechanics and Quantum Computing Basics for Engineers (Anantram)
- Engineering Project Management (Mamishev)
- Recommender Systems at Scale (Mohan)
2020-2021 Course Offerings
Autumn 2020
- Computer Vision: Classical and Deep Methods (Birchfield)
- Deep Learning for Big Visual Data (Hwang)
- Machines and Drives (Nagel)
- Analytical Methods in Electrical Engineering (Bonaci)
- Analog Circuits for Sensor Systems I (Silver)
- Radar and Imaging Techniques (Kuga)
- COVID Tech (Mamishev)
- The Self-Driving Car: Intro to AI for Mobile Robots (Smith)
Winter 2021
- Applied High-Performance GPU Computing (Reinhardt)
- Analog Integrated Circuit Design I (Silver)
- Embedded and Real-Time Systems (Sloss)
- Microwave Engineering (Kuga)
- Machine Learning for Cybersecurity (Poovendran)
- Convex Optimization (Calderone)
- Advanced Introduction to Machine Learning (Bilmes)
- Modeling of MEMS (Mamishev)
Spring 2021
- Software Engineering for Embedded Applications (Bonaci)
- Deep Learning (Bilmes)
- Digital Image Processing (Sun)
- Microfabrication (Li)
- Quantum Mechanics and Quantum Computing Basics for Engineers (Anantram)
- Model-based Representations for Systems Engineering (Kimberly)
- Analog Circuits for Sensor Systems II (Silver)
- Antennas for Modern Wireless Devices (Kuga)
Summer 2021
- Analog Integrated Circuit Design II (Silver)
- Mobile Applications for Sensing and Control (Bonaci)
- Engineering Project Management (Mamishev)
Watch below: Mobile Applications for Sensing and Control (Arjona)
Upcoming Courses
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.
Machine Learning and Cyber Security
Detecting and preventing cyber-attacks has become critically important as we live in a perpetually connected world. This course will teach the basics of Machine Learning in Cybersecurity using Python and its libraries (Numpy, Scikit-learn). Machine learning algorithms such as clustering, k-means, and Naive Bayes will be covered. The course will also provide an understanding of how to combat malware, and detect spam; and implement real-world cyber examples using machine learning.
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.
Applied Electromagnetics: How the Electromagnetic Force of Maxwell’s Equations Implies Inductance, Capacitance, Classical Circuit Theory, and the Rest of Life
Four tiny equations, written in vector calculus, describe the fundamental force that drives charge through all electronic systems—and also, incidentally, drives virtually every phenomenon we encounter in life. This is a course about the nature of that force; about how it shapes our world; and about the deep, yet rarely told story of how Maxwell’s Equations imply that circuit theory is false, yet is in certain regimes an excellent approximation.