Artificial Intelligence, M.S. (Beginning Fall 2020)
Saint Louis University’s master’s program in artificial intelligence prepares students to apply artificial intelligence methods, both efficiently and ethically, in order to solve difficult problems and impact the well-being of society.
This graduate program provides students with depth of knowledge regarding the models and technologies used to make advances in underlying artificial intelligence and machine learning and, through a partnership with faculty across the university students may choose to apply these techniques in specialized areas of application such as autonomous systems, bioinformatics, data science, health outcomes, image processing, and natural language processing.
Students will engage in both the theory of Artificial Intelligence and Machine Learning and in applying AI/ML in practice, including a culminating team-based capstone project. Students will also consider important questions regarding the impact of AI on society, implicit bias that may result from AI systems, and the ethical development and deployment of technologies.
Fieldwork and Research Opportunities
With our location in the midtown area of St. Louis, our students have access to a strong technology community, with operations for many Fortune 500 companies and a vibrant start-up community. This provides outstanding opportunities for summer internships, for part-time work during the academic year, and for future jobs after graduation.
Employers in St. Louis who show great interest in computer science students include Boeing, Centene, Citi, Deloitte, Enterprise, Express Scripts, KPMG, Maritz, MasterCard, Microsoft, Monsanto, and World Wide Technologies. Other students have worked for smaller companies or even started their own companies.
Our campus is within walking distance of the Cortex Innovation Community, a vibrant 200-acre (and growing) innovation hub and technology district. Cortex is home to SLU's Research Innovation Group which works on technology transfer and commercial partnerships. Cortex is also home to the weekly Venture Cafe (every Thursday from 3-8pm), which is a great place for students to connect with members of the tech community in a friendly and informal setting. Also in downtown St. Louis is the T-REX Technology Entrepreneur Center, a coworking space and technology incubator.
Careers related to artificial intelligence and computer science are routinely found on various "best jobs" lists because of their wonderful combination of excellent pay, satisfying work-life balance, and personal reward in seeing the great impact that computing can have throughout society. As a sample of such listings:
Indeed.com's Best Jobs of 2019 named Machine Learning Engineer as #1. Also included were Full-stack Developer #3, Computer Vision Engineering #13, and Data Scientist #22.
Glassdoor's 50 Best Jobs in America list for 2020 named Front End Engineer as #1, Java Developer #2, Data Scientist #3, DevOps Engineering #35, Data Engineer #6, and Software Engineer #7. Also listed were Mobile Developer #8, Applications Engineer #18, Systems Engineer #27, Scrum Master #29, Software Developer #32, Cloud Engineer #33, UX Designer #38, QA Engineer #39, and Network Engineer #49.
U.S. News 100 Best Jobs list for 2019 named Software Developer as #1 (as well as IT Manager #12, Web Developer #23, Database Administrator #30, Information Security Analyst #38, Computer Systems Analyst #53, Computer Network Architect #60, Computer Systems Administrator #63)
A bachelor's degree in a science, technology, engineering or math major (STEM) is typical. Most successful applicants have an undergraduate grade point average of 3.00 or better on a 4.00 scale. Applicants should have evidence of strong computational skills (generally through prior coursework in programming and data structures) as well as evidence of strong mathematical skills (generally through prior coursework in calculus and statistics).
- Application completion and fee
- One letter of recommendation is required; two more are optional
- Statement of professional goals
- GRE general scores recommended
Requirements for International Students
All admission policies and requirements for domestic students apply to international students along with the following:
- Demonstrate English Language Proficiency
- Proof of financial support must include:
- A letter of financial support from the person(s) or sponsoring agency funding the time at Saint Louis University
- A letter from the sponsor's bank verifying that the funds are available and will be so for the duration of study at the University
- Academic records, in English translation, of students who have undertaken postsecondary studies outside the United States must include the courses taken and/or lectures attended, practical laboratory work, the maximum and minimum grades attainable, the grades earned or the results of all end-of-term examinations, and any honors or degrees received. WES and ECE transcripts are accepted.
Applications for January admission must be completed by the preceding November 1, while applications for August admission must be completed by June 1. Applicants seeking scholarships or graduate assistantships are encouraged to apply earlier.
Applications will be reviewed as they are completed. A panel of faculty members from the Department of Computer Science will decide on acceptance, and all applicants will be evaluated for potential scholarships or assistantships.
Scholarships, Assistantships and Financial Aid
The Computer Science Department offers several forms of merit-based financial support for graduate students. These include possible tuition scholarships, and graduate assistantships that may include full or partial tuition, health insurance, and a stipend for living expenses in exchange for the assistant’s contributions to the teaching or research mission of the department. Students may also seek their own scholarships from a variety of independent organizations that support graduate education in STEM fields.
For more information, visit the student financial services office online at http://www.slu.edu/financial-aid.
- Graduates will be able to select the most appropriate choice among artificial intelligence methods for solving a given problem.
- Graduates will be able to design an experiment to evaluate the quality of a machine learning model and predict its accuracy in a solution environment.
- Graduates will be able to apply techniques from artificial intelligence to solve complex problems in an application domain.
- Graduates will be able to design and implement a software solution that meets a given set of computing requirements.
- Graduates will be able to make informed and ethical decisions regarding the impact of artificial intelligence technologies.
- Graduates will be able to assess literature and technical documents in the fields of artificial intelligence and machine learning.
- Graduates will be able to effectively communicate methods and results to both professional and general audiences in both oral and written form.
|CSCI 5030||Principles of Software Development||3|
|CSCI 5050||Computing and Society||3|
|CSCI 5740||Introduction to Artificial Intelligence||3|
|CSCI 5750||Machine Learning||3|
|CSCI 5961||Artificial Intelligence Capstone||3|
|Artificial Intelligence Foundations course||3|
|Artificial Intelligence Applications course||3|
|Artificial Intelligence Electives||9|
These courses have a primary focus on techniques in artificial intelligence and/or machine learning that have wide application to a variety of domain areas. Students must take at least one such course. The full list of approved courses is maintained by the Computer Science Department and includes
|CSCI 5730||Evolutionary Computation||3|
|CSCI 5745||Adv. Techniques in Artificial Intelligence||3|
|CSCI 5760||Deep Learning||3|
|STAT 5087||Applied Regression||3|
|STAT 5088||Bayesian Statistics and Statistical Computing||3|
These courses explore how tools or techniques from artificial intelligence are applied to solve problems in a specific domain area. Students must take at least one such course. The full list of approved courses is maintained by the Computer Science Department and includes
|BCB 5350||Machine Learning for Bioinformatics||3|
|BME 5150||Brain Computer Interface||3|
|CSCI 5070||Algorithmic Fairness|
|CSCI 5755||Natural Language Processing|
|CSCI 5570||Learning and Inference in Networking|
|CSCI 5830||Image Processing||3|
|GIS 5092||Machine Learning for GIS and Remote Sensing||3|
|HDS 5330||Predictive Modeling and Machine Learning||3|
Artificial Intelligence Supporting Courses
AI Supporting courses must serve one of three purposes: (1) provide knowledge in a specific domain area that prepares students to apply artificial intelligence or machine learning to solve problems in that particular domain; (2) provide richer foundational knowledge in a supporting area (e.g. algorithms, statistics) that prepares students to understand, enhance, or implement artificial intelligence techniques; (3) provide exploration of the broader impacts of artificial intelligence. Students may apply at most 6 credit hours of such courses to the degree. The full list of approved courses is maintained by the Computer Science Department and includes
|AENG 5800||Autonomous Systems Design||3|
|BCB 5200||Introduction Bioinformatics I||3|
|BCB 5250||Introduction Bioinformatics II||3|
|CSCI 5850||High-Performance Computing||3|
|ECE 5120||Modern Control Theory||3|
|ECE 5153||Image Processing||3|
|ECE 5226||Mobile Robotics||3|
|GIS 5060||Geospatial Methods in Environmental Studies||3|
|LAW 8235||Information Privacy Law||3|
|PSY 5120||Memory & Cognition||3|
|SOC 5670||Spatial Demography: Applied Statistics for Spatial Data||3|
|STAT 5084||Time Series||3|
The remaining electives can be taken from any of the Foundations, Applications, or Supporting categories, yet with a limit of at most six credit hours of Supporting courses.
Students must maintain a cumulative grade point average (GPA) of 3.00 in all graduate/professional courses.
Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.
Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.
This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.
|Critical course: CSCI 5050||Computing and Society||3|
|Critical course: CSCI 5740||Introduction to Artificial Intelligence||3|
|Critical course: CSCI 5750||Machine Learning||3|
|CSCI 5760||Deep Learning †||3|
|CSCI 5830||Image Processing ‡||3|
|CSCI 5100||Algorithms §||3|
|CSCI 5850||High-Performance Computing (selet an Artificial Intelligence Elective) §||3|
|CSCI 5050||Computing and Society||3|
|CSCI 5961||Artificial Intelligence Capstone||3|
|Artificial Intelligence Elective||3|
Select an Artificial Intelligence Foundations course
Select an Artificial Intelligence Applications course
Artificial Intelligence Elective: Select from any of the Foundational, Applications, or Supporting categories, yet with a limit of at most six credit of Supporting courses.