Chapter 8: Artificial Intelligence

Program Advisor: Dr. Yi Fang

Overview

Artificial Intelligence (AI) has emerged as a transformative force with the potential to reshape every aspect of our society. Organizations increasingly rely on AI to solve complex problems, optimize operations, and unlock new opportunities. The Master of Science in Artificial Intelligence is a cutting-edge graduate program designed to meet the rapidly growing demand for AI expertise. The major components of the curriculum include mathematical essentials, foundational AI courses, advanced AI electives, and an AI Practicum that provides real-world projects and research opportunities. The program also emphasizes the responsible application of AI so that students not only acquire technical expertise but also cultivate a holistic understanding of the ethical implications associated with AI technologies.

The program offers two concentrations to provide tailored pathways for varied career goals and interests.

MSAI – Computer Science and Engineering: Designed for students with a background in computer science and engineering, this concentration provides a comprehensive and in-depth study of the software, algorithmic, and computational aspects of AI.

MSAI – Electrical Computer and Engineering: Designed for students with a background in electrical and computer engineering, this concentration focuses on the hardware, architectural, and computational aspects of AI.

Students are expected to maintain a minimum grade point average of 3.000 while enrolled in the program. The degree requires completion of a minimum of 46 graduate units.

The admission requirements for the MSAI program align with those for the current CSEN and ECEN programs. Applicants must hold an undergraduate degree in computer science, electrical engineering, mathematics, physics, engineering, or a closely related discipline from an accredited institution. In addition, they must demonstrate completion of college-level coursework or knowledge in foundational areas relevant to the program, including:

  • programming languages (e.g., Python, Java, or C/C++),
  • data structures, algorithms, and
  • mathematics covering undergraduate-level calculus, linear algebra, probability, and statistics

Degree Requirements (46 units minimum)

The MSAI program consists of three major components (A, B, and D below) that are common to all students in both concentrations, along with a distinct component (C) of coursework tailored to the specific objectives of each concentration:  

(A) Essential Fundamentals (8 units): This component provides all MSAI students with the fundamental technical knowledge necessary to build proficiency in AI while fostering an understanding of its ethical responsibilities. Common to all M.S. degree-seeking students, students must also complete the Graduate Engineering Core requirements, which are designed to expand their overall knowledge base and develop essential professional skills. The courses include:

  • Graduate Engineering Core - Engineering and Society-ENGR 344 : Artificial Intelligence and Ethics (2 units)
  • Graduate Engineering Core - one course from the  Professional Development list. (2 units)
  • Additionally, the program requires fundamentals is mathematics related to AI: A.I Core class - AMTH 250 Fundamental Mathematics for Artificial Intelligence (4 units)

(B) AI Foundational Courses (11 units minimum): A suite of courses designed to equip all MSAI students with a comprehensive understanding of AI. This component covers the full breadth of key topics across the field, including foundational AI principles, machine learning, deep learning, and more, ensuring a solid foundation for advanced study and application in AI. These courses are delivered by various departments, ensuring consistent core principles while incorporating unique perspectives tailored to specific engineering disciplines. Students will receive guidance from their MSAI advisor in selecting the courses that best align with their academic background and professional goals.

  • AI Foundation Courses offered by CSEN department:
            - CSEN 240: Machine Learning (4 units)
            - CSEN 266: Artificial Intelligence (4 units)
            - CSEN 342: Deep Learning (4 units)
  • AI Foundation Courses offered by AMTH and ECEN department:
            - AMTH 370: Optimization Techniques I (2 units)
            - ECEN 520: Machine Learning (3 units)
            - ECEN 521: Deep Learning (3 units)
            - ECEN 522: Reinforcement Learning (3 units)

In the ECEN concentration, ECEN 522 and AMTH 370 are used as equivalent to CSEN 266 within the AI Foundational Courses. Also, ECEN 520 is an equivalent of CSEN 240. Students may substitute other specific AI Foundation Courses with equivalent courses taught in other departments or universities with approval from their MSAI advisor.

Beyond the above shared components, students in each concentration are required to take a distinct component of advanced AI electives:

(C) Advanced AI Electives (16 units):
Students select from a range of AI elective courses to deepen their technical expertise and proficiency in AI.

  • AI Electives offered by CSEN department:
    - CSEN 241: Cloud Computing (4 units)
    - CSEN 242: Big Data (4 units)
    - CSEN 245: Parallel Computing (4 units)
    - CSEN 272: Web Search and Information Retrieval (4 units)
    - CSEN 277: Human Computer Interaction (4 units)
    - CSEN 281: Pattern Recognition and Data Mining (4 units)
    - CSEN 291: Computational Creativity (4 units)
    - CSEN 338: Image and Video Compression (4 units)
    - CSEN 344: Computer Vision I (2 units)
    - CSEN 345: Computer Vision II (2 units)
    - CSEN 346: Natural Language Processing (4 units)
    - CSEN 353: Trust and Privacy in Online Social Network (4 units)
    - CSEN 354: Social Networks Analysis and Risk (4 units)
    - CSEN 377: Data Visualization (4 units)
    -     Select CSEN 296 courses (Topics in Computer Science and Engineering) focused on AI topics may also count as AI electives, subject to approval by the MSAI program advisor.
  • AI Electives offered by ECEN department:
    -     ECEN 226: Machine Learning and Signal Processing using FPGA’s (2 units)
    - ECEN 337: Robotics I (2 units)
    - ECEN 338: Robotics II (2 units)
    - ECEN 523: Natural Language Processing (2 units)
    - ECEN 524: Robot Learning (2 units)
    - ECEN 529: Hardware Acceleration for Machine Learning on FPGA’s (2 units)
    - ECEN 532: Design of Assistive Technologies (4 units)
    - ECEN 644/CSEN 344: Computer Vision I (2 units)
    - ECEN 645/CSEN 345: Computer Vision II (2 units)
    - ECEN 809: Special Topics in Human Machine Interaction (2-4 units)

The final component, the AI Practicum, serves as a culminating experience for all students in the MSAI program:


(D) AI Practicum (minimum 6 units): The AI Practicum provides students with hands-on experience in applying their AI knowledge to industrial or research challenges. This culminating experience can be achieved through the completion of a thesis, practical project, or other research opportunities.

For the CSEN concentration, students can choose CSEN 493 for industry-mentored projects or CSEN 497 for a master’s thesis. For the ECEN concentration, students can select a design project, or non-thesis supervised research ECEN 299, or thesis- based research ECEN 297 (after ECEN 299 is completed).