M.S. in Artificial Intelligence Systems

M.S. in Artificial Intelligence Systems

ABOUT THE PROGRAM

The Master of Science (M.S.) program in Artificial Intelligence Systems (MSAIS) addresses the increasing significance of artificial intelligence (AI) across diverse industries and the growing demand for skilled professionals in this field. Through its interdisciplinary approach, the program equips students with a comprehensive skillset, merging knowledge from diverse fields such as computer science, mathematics, engineering, and ethics.

Industry careers looking for students with a MS degree in AI Systems include technological and IT services, healthcare, finance and banking, robotics and manufacturing, telecommunications, energy and utilities, education and research, automotive, aerospace and defense.

The MS program is a non-thesis major in Artificial Intelligence Systems with a total of 30 credits hours. The program is designed for students or working professionals with a B.S. degree with a strong analytical and computing background such as computer engineering or science, industrial and systems engineering, or physics would qualify to pursue this degree.

The curriculum will consist of a set of 6 core courses (18 credit hours), 3 elective courses (9 credit hours), and a capstone project (3 credit hours) in AI systems. Throughout the curriculum you will interact with HWCOE faculty with a diverse research portfolio, carry hands-on software experiments, access to the HiPerGator supercomputer resources, design, and develop and deploy full-stack AI system via a capstone project.

Key Features

The projected benefits of the program include versatile skillset, real-world applicability, industry relevance, adaptability to change, customization and specialization, enhanced problem-solving abilities, global perspectives.

You can customize the curriculum by choosing 3 elective courses (9 credit hours). The MSAIS currently offers the following areas:

  • Advanced Machine Learning
  • Data-Driven Modeling
  • Autonomy
  • Robotics
  • Human-Centered Computing
  • High-Performance Computing

A MESSAGE FROM THE PROGRAM Coordinator

Catia Silva, Ph.D.

Greetings and welcome to the Master of Science (M.S.) in Artificial Intelligence at the University of Florida!

As the program coordinator, I am excited to lead a team of dedicated faculty and staff committed to your academic and professional success. The MSAIS program aims to meet the demand for skilled professionals who can navigate the complexities of the artificial intelligence (AI) field. Through an interdisciplinary approach, we provide students with a comprehensive skillset, merging knowledge from diverse fields such as computer science, mathematics, engineering, and ethics.

A unique feature of our program is the flexibility it offers through elective courses. You have the opportunity to customize your curriculum by choosing three elective courses from a variety of concentration areas.

I’m thrilled to guide you through this educational journey and help you achieve your academic and professional aspirations.

GENERAL ADMISSION REQUIREMENTS

The minimum admission requirements include:

  • B.S. in a computing engineering field.
  • GPA requirements as defined by the Graduate School (recommended GPA at 3.0 or higher).
  • 3 Letters of Recommendations.
  • 1 Statement of Purpose (SOP).
  • English proficiency test, if applicable. (Check if you need an English proficient test here).
  • GRE is not required.

Note to applicants: The Admissions Committee reviews packets on a rolling basis for all packets that include all items from the list above. Your application packet will be labeled as incomplete until you submit all the items listed above.

APPLY NOW!

DEGREE REQUIREMENTS

At least 30 credit hours beyond bachelor’s degree are required. These hours include MSAIS master’s degree work taken at the University of Florida or, if approved, up to 6 hours of master’s degree work earned at another approved university outside UF. Course substitutions must be petitioned and are considered on a case-by-case basis.

Requirements include completion of the following:

  • MSAIS core courses: 18 credits
  • MSAIS specialization courses: 9 credits
  • MSAIS Capstone Project: 3 credits

GRADUATE HANDBOOK

EED Graduate Handbook 2022-2024

COURSES

Course descriptions are available in the Graduate Catalog.

MSAIS Core Courses (18 cr)

Choose one course from each core:

CORE 1: Machine Learning

  • EGN 5216 Machine Learning for AI Systems (3 credits)

CORE 2: AI Systems

  • EGN 6930 Artificial Intelligent Systems (3 credits)

CORE 3: Sensing & Analysis (select 1 of these 3 options)

  • CAP 5416 Computer Vision (3 credits)
  • EEE 6512 Image Processing and Computer Vision (3 credits)
  • EEL 5406 Computational Photography (3 credits)

CORE 4: Security (select 1 of these 3 options)

  • CIS 6930 Trustworthy Machine Learning (3 credits)
  • EEE 6561 Fundamentals of Biometric Identification (3 credits)
  • EL 5729 IoT Security and Privacy (3 credits)

CORE 5: Deep Learning (select 1 of these 2 options)

  • CAP 6615 Neural Networks for Computing (3 credits)
  • EGN 6217 Applied Deep Learning (3 credits)

CORE 6: Ethics

  • LAW 6930 Legal, Policy, and Ethical Dimensions (3 credits)


MSAIS Concentration Courses (9 cr)

Choose three elective courses, at least one in AML-DDM and one in AR-HCC:

Advanced Machine Learning and Data Driven Modeling (AML-DDM)

  • BME 6928 Biomedical Data Science (3 credits)
  • EEL 5840 Fundamentals of Machine Learning (3 credits) or STA 6703 Statistical Machine Learning (3 credits)
  • CAP 6617 Advanced Machine Learning (3 credits)
  • EEL 6814 Deep Learning (3 credits)
  • EEL 6825 Pattern Recognition and Intelligent Systems (3 credits)
  • ESI 6492 Global Optimization (3 credits)
  • EEE 6504 Machine Learning for Time Series (3 credits)
  • ESI 6355 Decision Support Systems for ISE (3 credits)

Autonomy, Robotics, and Human-Centered Computing (AR-HCC)

  • ABE 6005 Applied Control for Automation and Robotics (3 credits)
  • CAP 5108 Research Methods for Human Centered Computing (3 credits)
  • CEN 5726 Natural User Interaction (3 credits)
  • EML 6351 Adaptive Control (3 credits)

Unrestricted Technical Electives (UT)

This group allows the students to take a technical elective course for greater curriculum flexibility. The technical elective courses in this group must be chosen in coordination with the graduate advisor to ensure prerequisite fulfillment and to optimize for achieving student career goals (e.g., courses related to entrepreneurship).

MSAIS Capstone Project (3 cr)

  • EGN 6933 Project in AI Systems (Non-Thesis Project) (3 credits)

    A typical curriculum will look like the following:

    Semester Spring Fall
    Year 1 Core 1: Machine Learning (3 cr)
    Core 2: AI Systems (3 cr)
    Core 3: Sensing & Analysis (3 cr)    		 Core 4: Security (3 cr)
    Core 5: Deep Learning (3 cr)
    Core 6: Ethics (3 cr)
    Year 2 Elective I (3 cr)
    Elective II (3 cr)
    Elective III (3 cr)    		 EGN 6933 Project in AI Systems (3 cr)

    Contact Information

    For inquiries about the M.S. in Artificial Intelligence Systems, please contact:

    Catia S. Silva, Ph.D.

    catiaspsilva@ece.ufl.edu | (352) 392-6502 | MALA 3122
    Instructional Assistant Professor, Department of Electrical and Computer Engineering
    Program Coordinator, M.S. in AI Systems, Herbert Wertheim College of Engineering
    GitHub Campus Advisor
    University of Florida
    Gainesville, FL 32611
    Webpage: faculty.eng.ufl.edu/catia-silva
    GitHub: github.com/catiaspsilva

    —

    Pamela Simon

    phs@ufl.edu | (352) 392-9672
    Academic Assistant II, Department of Engineering Education
    University of Florida
    Gainesville, FL 32611