Note: Register for the number of units based on what is stated on the syllabus for the course. You may edit units on Schedule Builder by pressing the "edit" button by units.
Undergraduate Courses
Winter 2024
EEC189L: Quantum Computing
- CRN: 22425 and 22426, Units: 4
- Professor Marina Radulaski
- Date/Time: M 2:10 - 3:30 PM & 6:10 - 8:00 PM, W 2:10 - 3:30 PM
- Prerequisites: MAT 22A (may be concurrent), ENG 06, or another programming course
- Course Description: Learn the principles of and get hands-on experience with quantum computing! This course is aimed at sophomore and junior students with interest in quantum computing who are familiar with the basics of linear algebra such as vector spaces and matrix manipulations. The course learning goals aim for students to:
• Understand how quantum information is represented and how it differs from classical information,
• Become familiar with the unintuitive concepts of quantum mechanics such as the superposition and entanglement,
• Become familiar with the physical implementations of quantum hardware,
• Learn to design a quantum circuit,
• Learn to program in Qiskit open-source quantum computing software development framework,
• Learn basics of quantum algorithms,
• Apply physical concepts to quantum information hands-on demos,
• Develop interdisciplinary communication and presentation skills.
Graduate EEC 289 Courses
Winter 2026
EEC 289A: Introduction to Unsupervised Learning
- CRN: 40580, Units: 4
- Professor Yubei Chen
- Lecture – 4 hours/week MW 2:10-4:00
- Pre Requisites: ECS 171, or ECS 174, or EEC 174AY, or equivalent; All students need to know python programming. (or instructor consent)
- Recommended: Proficiency in linear algebra, calculus or mathematical analysis, applied probability, optimization, and deep learning.
- Course Description: Humans and other animals exhibit learning abilities and understanding of the world that are far
beyond the capabilities of current AI and machine learning systems. Such capabilities are largely
driven by intrinsic objectives without external supervision. Unsupervised learning (also known as
self-supervised learning) aims to mimic the natural intelligence, build models that find patterns in
data, and model the world automatically. Two fundamental goals in unsupervised learning are to
model environments structures and to model biological sensory systems. These are intertwined
because many properties of the sensory system are adapted to the statistical and geometrical
structure of the environments.
EEC 289L: Quantum Information Technologies
- CRN: 40581, 4 units
- Professor Marina Radulaski
- Lecture - MW 11:10-12:30
- Prerequisites: EEC189U (Quantum Mechanics for Engineers), MAT 022A (Linear Algebra), desired - any class in PHY 009 series
Course Description: This course is aimed at graduate students with interest in quantum technologies who have a solid background in linear algebra. The course learning goals aim for students to:
• Become familiar with the unintuitive concepts of quantum mechanics such as the superposition, entanglement, and the no-cloning theorem,
• Command the basics of the Dirac notation (i.e. the mathematical formalism of quantum information),
• Learn the concepts of quantum computing, quantum communication and quantum sensing,
• Understand the operating principles of some of the most prevalent physical implementations of quantum information systems,
• Learn to program in Qiskit open-source quantum computing software development framework,
• Develop interdisciplinary communication and presentation skills.
The lectures will incorporate active learning and student discussions, while the weekly homework will help solidify the understanding of concepts and provide practice in quantum programming. The midterm exam will assess the mastery of the technical material, while the group project will provide an opportunity to delve into a topic of interest, practice teamwork, science communication and presentation skills. The final presentation will consist of a short video and an in-class Q&A session.
EEC 289O: Integrated Circuits Design and Tapeout
- CRN: 40962, Units: 2
- Professor Omeed Momeni
- Lecture – TR 1:10-2:30
- Prerequisites: EEC 210
- Course Description: This course series takes the students on a journey of designing an integrated circuit
(IC) from scratch all the way to making it ready for fabrication and tapeout and
finally preparing the measurement setup and packaging when the chip is back from
the foundry. Enrolling in the Spring quarter is mandatory. Enrolling in the Fall
quarter is also mandatory for the students who want their chips to be fabricated
and characterized. It is highly recommended to enroll in this class if the student
can enroll in all 3 quarters. The course starts in the Winter quarter by covering the basics of chip design flow
including process technology overview, layout process, design rules, simulation and
verification, packaging, and measurement. Students will use Cadence Virtuoso and
the TSMC 180 nm CMOS PDK to perform schematic capture, run simulations,
create transistor-level layouts, extract parasitics, and verify DRC/LVS compliance.
There will be 5-6 homework assignments to design, simulate and prepare the layout
for a few analog and digital circuit blocks such as an inverter chain, a ring oscillator,
a divide by two circuit, and an amplifier in the TSMC’s 180 nm CMOS process.
Toward the end of the quarter the students propose the circuit they would like to
design and fabricate in the Spring quarter.
- Syllabus 289O IC Design and Tapeout V3.pdf
EEC 289Q: Neurally Inspired Algorithms and Architectures
- CRN: 41617, Units: 4
- Professor Anthony Thomas
- Lecture – MW 4-5:30
- Prerequisites: EEC 161 (or equivalent background in probability), MAT 22A (linear algebra), helpful, but not required – background in machine learning, signal processing, or optimization.
- Course Description: This course provides an overview of topics at the intersection of neural computation and algorithm design and is intended for students with a solid background in linear algebra and probability. Prior exposure to the basic principles of statistical estimation and optimization will be helpful, but not strictly required. The goals for the course are for students to learn about:
- Mathematically rigorous tools for understanding and analyzing algorithms in neural computation.
- The use of randomized methods in neural computation and algorithms.
- Other paradigms for learning in addition to the currently dominant statistical one.
- Connections between topics in the analysis of algorithms and neuroscience/neural computation.
- Applications of some of the above to developing new kinds of computer hardware.
- Course grades will be based on a mix of problem sets, in-class presentations, paper-readings, and a final project.