Engineering Artificial Intelligence - MS 

The Master of Science in engineering of artificial intelligence (EAI) prepares specialists with comprehensive knowledge in all areas of this new disruptive and revolutionary technology. Gain knowledge in interdisciplinary foundations and practical experience in algorithms, sensors, hardware, control, and applications.

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Overview This program consists of a three-semester course sequence which covers the fundamentals of artificial intelligence, probabilistic reasoning, machine learning, deep learning algorithms, sensor electronics, digital systems design and acceleration hardware, control theory and practice, convex optimization, natural language processing, and computer vision and applications in mobile, health, and other domains. Students will obtain a critical computer engineering background, which includes knowledge in sensors, hardware, control, and applications, enabling them to solve real world AI problems.

Admission requirements

• Bachelor degree or equivalent in Electrical or Computer Engineering or in a related discipline.
• GPA above 3.3
• GRE V150, Q159, WA3 (if required by the graduate school; waived for Fall 2024 for now).
• TOEFL 80, IELTS 6.5
• 3 recommendation letters.

These are the minimum requirements, similar to the CE and EE M.S. programs.

Degree RequirementsThe EAI M.S. program offers both thesis and non-thesis options. A non-thesis option is expected to be finished in three semesters, while a thesis one typically takes four semesters. In general, at least 30 graduate credits with a cumulative and departmental grade point average of 3.0 or better are needed. The details of the program structures and course/credit requirements in different subareas are given below:

• Core Courses
  Subarea	Minimum # of credits needed	List of courses in the subarea
  Subarea Foundations	Minimum # of credits needed 6 credits	List of courses in the subarea ESE 503 (Stochastic Systems, 3 credits); ESE 561 (Theory of Artificial Intelligence, 3 credits);
  Subarea Methods	Minimum # of credits needed 6 credits	List of courses in the subarea ESE 577 (Deep Learning Algorithms and Software, 3 credits); ESE 588 (Fundamentals of Machine Learning, 3 credits);
  Subarea Applications	Minimum # of credits needed 3 credits	List of courses in the subarea ESE 564 (Artificial Intelligence for Robotics, 3 credits); ESE 589 (Learning Systems for Eng. Appl., 3 credits); ESE 590 (Practical Machine Learning & Artificial Intelligence, 3 credits);
  Subarea Hardware	Minimum # of credits needed 3 credits	List of courses in the subarea ESE 507 (Advanced Dig. Sys. Design & Generation, 3 credits); ESE 525 (Modern Sensors in Artificial Intelligence Applications, 3 credits); ESE 587 (Hardware Architectures for Deep Learning, 3 credits);
  Subarea Elective	Minimum # of credits needed 6 credits for non-Thesis option       3 credits for Thesis option	List of courses in the subarea ESE 502 (Linear Systems, 3 credits);     ESE 507 (Advanced Dig. Sys. Design & Generation, 3 credits); ESE 525 (Modern Sensors in Artificial Intelligence Appl., 3 credits); ESE 533 (Convex Optimization & Eng. Appl., 3 credits); ESE 537 (Mobile Sensing Systems & Appl., 3 credits); ESE 543 (Mobile Cloud Computing, 3 credits); ESE 558 (Digital Image Processing, 3 credits); ESE 562 (AI Driven Smart Grids, 3 credits); ESE 564 (Artificial Intelligence for Robotics, 3 credits); ESE 568 (Computer and Robot Vision, 3 credits); ESE 587 (Hardware Architectures for Deep Learning, 3 credits); ESE 589 (Learning Systems for Eng. Appl., 3 credits); ESE 590 (Practical Machine Learning & Artificial Intelligence, 3 credits); ESE 592 (Distributed Computation, Control & Learning over Networks, 3 credits); ESE 670* (Topics in Electrical Sciences, 3 credits) AMS 580 (Statistical Learning, 3 credits); MEC 529 (Introduction to Robotics, 3 credits); CSE 538 (Natural Language Processing, 3 credits). Any ESE or non-ESE course not listed above but approved by Graduate Program Director (3 credit max.; approval must be obtained before enrollment).

• Thesis Option
  Thesis Option
  Thesis Option Industrial experience	at least 1 but not more than 3 credits	ESE 597 (Practicum in Engineering [Internship], variable credit);   In exceptional circumstances, the Graduate Program Director can approve a replacement of ESE 597 credits by ESE 599 (Research for MS students) or ESE 698 (Practicum in teaching) credits.   Students who choose to take one credit of industrial experience may consult with their advisor on how to complete the remaining two credits for their degree.
  Thesis Option Research experience	at least 6 credits	ESE 599 (Research for M.S. students, variable and repetitive credit);
  Thesis Option Teaching experience	not required but can be used (maximum 3 credits)	ESE 698 (Practicum in teaching, variable credits);

• Non-Thesis Option

  • Non-Thesis Option
    Industrial experience	3 credits	ESE 597 (Practicum in Engineering [Internship], variable credit);   In exceptional circumstances, the Graduate Program Director can approve a replacement of ESE 597 credits by ESE 599 (Research for MS students) or ESE 698 (Practicum in teaching) credits.
    To meet the 30-credit minimum for the program, non-thesis students may take one of the following:
    Research experience	Maximum 3 credits	ESE 599 (Research for M.S. students, variable and repetitive credit);
    Teaching experience	Maximum 3 credits	ESE 698 (Practicum in teaching, variable credits);
    Additional Elective	3 credits	Any course listed above not already used to fulfill another program requirement OR Any ESE or non-ESE course not listed above but approved by Graduate Program Director (3 credit max.; approval must be obtained before enrollment).

    * ESE 670 – Topics in Electrical Sciences (3 credits) is the course with variable content, and it can be approved by the Graduate Program Director as an elective course for the EAI MS degree when the course topic corresponds to the program area.

Explore Your Future Career Opportunities

AI knowledge and skills are hotly pursued in industry and business these days. Many students with basic algorithms and software backgrounds in deep learning can secure high-paying job offers in finance and high-tech industries around the globe.

AI Product Manager AI Researcher Cybersecurity Vision Engineer Data Scientist