30 credit hours
Artificial intelligence is one of the top growth areas in research and the tech industry, and it is projected to continue growing in size and importance for the foreseeable future. The MAS-AI program is designed to meet local, national, and global needs for artificial intelligence professionals. The course of study teaches the foundational concepts, methods, and skills of artificial intelligence, machine learning, and big data analytics, as well as the mathematical foundations, ethics, and AI application areas needed for professional success in the field. The program was designed by a committee of AI experts in the CS department, with reference to the current and projected job market in AI, comparison with other similar programs, and consultation with industry representatives.
The Master of Artificial Intelligence (MAS-AI) is a Professional Master's degree program designed for:
- Computer science professionals currently working in business, government, or industry who want to advance their careers.
- Recent computer science graduates who want to extend and deepen their knowledge of artificial intelligence in order to gain a competitive edge in the job market.
- People without a previous degree in computer science who want to prepare for a career as a working artificial intelligence professional.
The MAS-AI program provides a conceptual and practical education in Artificial Intelligence and its subfields of Machine Learning, Deep Learning, Computer Vision, Natural Language Processing, Probabilistic Reasoning, Data Analytics, and Data Mining. The program has a rigorous core curriculum. In addition it allows you to select courses based on interest. You can choose to do a Master's Project (but not a Master's Thesis) or coursework-only to complete the MAS-AI program. There is no comprehensive exam.
As a full-time student whose bachelor's degree is in computer science, you can complete the MAS-AI program in three semesters plus a summer course. If you are without a bachelor's degree in computer science, it may require extra time to make up prerequisite undergraduate coursework.
Graduate CS classes in this program are offered during the day and evening. Both day-only and evening-only schedules can be accommodated. You can complete the MAS-AI program taking in-demand, online classes as an online distance student without ever visiting the Illinois Tech campus.
In general, for admission into the MAS-AI program, you need a bachelor's degree (not necessarily in computer science) and you must submit a transcript and possibly GRE and TOEFL scores. For more information about this program, contact Dr. Mustafa Bilgic. Admission requirements are listed below.
- GPA 3.0
- GRE 304 (quantitative + verbal), 3.0 (analytical writing)
- TOEFL – the university standards and requirements
- Grades of "B" or better in the Prerequisite Coursework Requirements below, for applicants whose bachelor degrees are not in Computer Science.
Completing the MAS-AI degree program requires:
- A minimum total of 30 credit hours of coursework approved by an adviser.
- A GPA of at least 3.0/4.0 in the plan of study.
Coursework used to meet degree program requirements must meet the following restrictions:
- It must include at least two courses in each of two core areas of AI Foundations and AI Applications, as well as one course in each of two core areas of Data Analytics and Data Processing. It must also include at least one Interdisciplinary Elective.
- Three free electives from any of the core groups or any other 400-500 level CS or CSP course.
- At least 18 total credit hours must be in CS or CS Professional (CSP) courses, from the Illinois Tech CS department (no transfer courses).
- At least 20 total credit hours must be at the 500-level.
- The remaining 10 credit hours may include CS or CSP courses (at the 400-level or above) and transfer credit for coursework from other Illinois Tech departments or for CS courses from other universities.
- At most 6 of total credit hours can come from accelerated courses.
- Co-terminal students can share 9 credits of coursework with their bachelor's studies. (See details.)
- At most 3 total credit hours can come from the optional master's project (CS 597). Credit hours from a master's thesis (CS 591) cannot be used.
- Interprofessional Projects (IPROs) and prerequisite undergraduate courses (CS 201, 401, 402, and 403) cannot be applied toward the credit hour requirement.
- All other relevant university and college requirements (such as time limits) must be met.
- International master's students on F-1 visas may be eligible for Curricular Practical Training (CPT) while studying for a degree, and Optional Practical Training (OPT) after completion of a degree. Information and forms are available from the Career Management Center (CMC) and the International Center.
- The CS department is firm on the requirements of a grade point average (GPA) of at least 3.0 to be eligible for a CPT or OPT. Students who do not meet this criterion should consult with the International Center to discuss their options. Note that a GPA falling below 3.0 will cause eligibility for a CPT to be revoked, even if permission forms have already been signed.
- You must complete 18 credit hours of coursework to be eligible for a CPT. In addition, the CS department requires second degree and transfer students to complete at least 9 credits of Illinois Tech CS coursework before they can be eligible for a CPT.
Course of Study
Required Core Courses for MAS-AI
There are four categories of core courses: AI Foundations, AI Applications, Data Analytics, Data Processing, and one category of Interdisciplinary Electives. You must take two courses in the core areas of AI Foundations and AI Applications. You also are required to take one course the core areas of Data Analytics and Data Processing. You must take at least one Interdisciplinary Electives course.
Note: Only courses taken at Illinois Tech can be used as core courses; courses transferred from other universities can be used only as electives.
Core course groups
AI Core (6 credits)
- CS 480 Introduction to Artificial Intelligence or CS581 Advanced Artificial Intelligence
- CS 584 Machine Learning or MATH 569 Statistical Learning
AI Applications (6 credits)
Data Analytics (3 credits)
- CS 522 Advanced Data Mining
- CS 578 Interactive and Transparent Machine Learning
- CS 583 Probabilistic Graphical Models
- CS 595 Deep Learning
- CSP 554 Big Data Technologies
- CSP/MATH 571 Data Preparation and Analysis
- MATH 564 Applied Statistics
- MATH 574 Bayesian Computational Statistics
Data Processing (3 credits)
- CS 520 Data Integration, Warehousing, Provenance
- CS 525 Advanced Database Organization
- CS 546 Parallel and Distributed Processing
- CS 554 Data-Intensive Computing
Interdisciplinary Electives (3 credits)
- BIOL 440 Neurobiology
- BIOL 550 Bioinformatics
- BME 433 Biomedical Engineering Applications of Statistics
- BME 504 Neurobiology
- COM 501 Introduction to Linguistics
- COM 584 Humanizing Technology
- ECE 563 Computational Intelligence in Engineering
- MMAE 440 Introduction to Robotics
- MMAE 540 Robotics
- MSF 526 Computational Finance
- PHIL 574 Ethics in Computer Science
- PSYC 423 Learning Theory
- PSYC 426 Cognitive Science
- PSYC 503 Learning and Cognition
Additional electives from other departments will be evaluated as relevant graduate level courses become available.