Electrical and Computer Engineering Advising


It is strongly recommended new students seek early advising with our department pre-major advisor, because the program has a strong prerequisite structure and courses are only offered once per year. Students must initially declare as a pre-major in order to register for courses in the Engineering & Design department. Students interested in pursuing the EECE program must complete a set of prerequisite coursework (see Electrical and Computer Engineering Admissions) as a pre-major before they can apply for acceptance as a full major in the program. EECE faculty are also available for general program and career advising.

Students who do not complete MATH 124 and PHYS 161 by the end of fall quarter in year one, might not complete the program in four years and should seek advising. 

Pre-major Advisor

Lisa Ochs



Call or email with advising questions!  

Advising Materials

specific to year of graduation

Students graduating 2023

Yes!  All EECE students with the appropriate pre-requisites can take any of these three courses toward their 6 credits of required technical electives.  In addition, students in the Electronics concentration can also take any of these courses in place of EECE 333, EECE 321, and EECE 361.  Students in the Energy concentration can take any of these courses in place of EECE 361.

This course introduces fundamental concepts of wireless networks. The course starts with the introduction of networking basics such as loss, delay, throughput at the network edge/core. Then it takes a top-down approach beginning at the application layer of a network and working its way down to the wireless physical layer. It also covers a broad range of wireless networking standards, and reviews important wireless network application areas (e.g., Network Security, Internet of Things and Connected Vehicles). 

Pre-reqs: EECE 244. 

Provides an introduction to machine learning with particular attention on real-world engineering applications. Theoretical foundation and application of supervised learning techniques such as regression and classification as well as unsupervised learning techniques such as clustering and dimensionality reduction. State-of-the-art deep learning algorithms as well as their implementation and use in solving engineering problems. Applications may include object detection and identification in images/videos, pattern recognition in speech/audio, and traffic prediction.  

Pre-reqs: EECE 244; MATH 204; MATH 341 or MATH 345.

This course introduces the fundamental principles, techniques, and applications of artificial intelligence (AI) and reinforcement learning (RL). Topics include knowledge representation, heuristic and algorithmic search methods, reasoning and planning in uncertain environments, and algorithmic learning techniques in unknown environments. The course also includes a discussion of the ethical and societal implications of AI and RL. Students completing this course will have a working knowledge of the design and implementation of various algorithms that will allow an intelligent agent to accomplish tasks in complex environments. Applications of topics covered in this course may include robotics, autonomous driving, and computer games. 

Pre-reqs: EECE 244; MATH 204; MATH 341 or MATH 345.

Students graduating 2024 or later

Concentration Change Request

Change requests will only be granted if there is room in the required courses for the desired concentration. While students may request concentration changes at any time, changes after the start of fall quarter of the 3rd year may require an additional year of study to complete the coursework for the newly chosen concentration.

Request form

Reach Out:


Department Office 
ET 204



Lisa Ochs
Pre-major Advisor




Xichen Jiang
Associate Professor, Energy Concentration



Andy Klein
Professor and EECE Program Director