Special Topics

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

Fall 2024

EEC 289Q: Practical AI

• CRN: 30220, Units: 4
• Professor Houman Homayoun
• Lecture – 4 hours/week
• Graduate Student Status
• Course Description: The recent advancements in artificial intelligence (AI) have significantly impacted a wide range of applications, from natural language processing and computer vision to autonomous systems and smart environments. This course provides a comprehensive introduction to practical AI, focusing on the application of AI techniques in real-world scenarios. It aims to equip students with the knowledge to apply AI techniques effectively and understand the current state-of-the-art methods in AI, including large language models (LLMs) and natural language processing (NLP). The course is designed for students and practitioners familiar with application requirements, helping them select and apply appropriate AI techniques to solve specific problems.  
• Through the course, the students will gain a deeper understanding of both classic and recent techniques used in discrete optimization, and will grow into informed users of optimization tools and methodologies that may find application in their research area. The material will be motivated and discussed in the context of a large number of example problems that arise in different subareas of computer engineering, such as electronic design automation, compilers and operating systems.

EEC 289Q: Deep Learning Hardware

• CRN: 50201, Units: 4
• Professor Avesta Sasan
• Lecture – 4 hours/week
• Pre-Requisites: EEC196 {can be concurrent}; and Consent of Instructor
• Course Description: This course is designed to equip students with a comprehensive understanding of the hardware aspects of machine learning. The lecture-style course will cover fundamental concepts in deep learning, hardware accelerators, hardware co-optimization, and techniques for improving hardware efficiency when executing inference on deep learning networks. Students will also assigned and conduct individual research on a given topic related to hardware for machine learning, which they will present in the form of a survey and a 20-minute lecture. By the end of the semester, students will be expected to present their research findings to the class.