Artificial Intelligence, M.S.
ÌìÃÀ´«Ã½ÖÆƬ³§â€™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 a depth of knowledge regarding the models and technologies used to make advances in underlying artificial intelligence and machine learning. 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
- Natural language processing
Curriculum Overview
Students in SLU's artificial intelligence degree programÌýengage in the theory of artificial intelligence (AI) and machine learning (ML) and in applying AI/ML in practice, including a culminating research thesis or team-based capstone project. Students 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
From ÌìÃÀ´«Ã½ÖÆƬ³§'s location in the Midtown area of St. Louis, our students have access to a strong technology community, operations of many Fortune 500 companies and a vibrant startup community. This provides outstanding opportunities for summer internships, part-time work during the academic year and 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, Bayer and World Wide Technologies. Other graduates have worked for smaller companies or even started their own companies.
SLU's campus is within walking distance of theÌý, a vibrant 200-acre (and growing) innovation hub and technology district. Cortex is home toÌýSLU's Research Innovation Group,Ìýwhich works on technology transfer and commercial partnerships. Cortex is also home to the weeklyÌý, 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 theÌý, a co-working space and technology incubator.
Careers
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:
- Ìýlist for 2024 named data scientist as #8. Other computing jobs in the top 100 included software developer (#3), IT manager (#4), information security analyst (#7), web developer (#21), computer systems analyst (#61), computer network architect (#77).
- lists data scientist as the occupation with the third-highest projected growth through 2032. Other computing jobs ranked in the top 20 include information security analyst (#5), software developer (#10), computer and information research scientists (#13).
- Ìýlist for 2022 named data scientist as #3 and machine learning engineer as #6. Other computing jobs in the top 25 includeÌýenterprise architect (#1), Java developer (#9), devops engineer (#4), information security engineer (#15), software engineer (#8), back-end engineer (#11), cloud engineer (#12) and UX designer (#24).
Admission Requirements
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).
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Application Requirements
- Transcript(s)
- One letter of recommendation is required; two more are optional
- ¸éé²õ³Ü³¾Ã©
- Statement of professional goals
- GRE general scores recommended
Requirements for International Students
All admission policies and requirements for domestic students apply to international students. International students must also meet the following additional requirements:
- DemonstrateÌýEnglish Language Proficiency
- Financial documents are required to complete an application for admission and be reviewed for admission and merit scholarships.Ìý
- Proof of financial support that must include:
- A letter of financial support from the person(s) or sponsoring agency funding the student's time at ÌìÃÀ´«Ã½ÖÆƬ³§
- A letter from the sponsor's bank verifying that the funds are available and will be so for the duration of the student's study at the University
- Academic records, in English translation, of students who have undertaken postsecondary studies outside the United States must include:
- 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
- Any honors or degrees received.
WES and ECE transcripts are accepted.
Application Deadlines
Applications for January admission must be completed by the preceding Nov. 1, while applications for August admission must be completed by June 1. Applicants seeking scholarships or graduate assistantships are encouraged to apply earlier.
Review Process
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.
Tuition
Tuition | Total Program Cost |
---|---|
MS Artificial Intelligence | $42,000 |
Additional charges may apply. Other resources are listed below:
Information on Tuition and Fees
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, contact the Office of Student Financial Services.
- 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.Ìý
- 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.
Code | Title | Credits |
---|---|---|
CSCIÌý5030 | Principles of Software Development | 3 |
CSCIÌý5050 | Computing and Society | 3 |
CSCIÌý5740 | Introduction to Artificial Intelligence | 3 |
CSCIÌý5750 | Introduction to Machine Learning | 3 |
Artificial Intelligence Foundations course | 3 | |
Artificial Intelligence Applications course | 3 | |
Artificial Intelligence Electives | 6 | |
Choose the non-thesis or thesis Option | 6 | |
Non-thesis Option: | ||
Additional Foundations or Applications course | ||
CSCIÌý5961 | Artificial Intelligence Capstone Project | |
Thesis Option: | ||
CSCIÌý5990 | Thesis Research | |
Total Credits | 30 |
Artificial Intelligence FoundationsÌý
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:
Code | Title | Credits |
---|---|---|
CSCIÌý5730 | Evolutionary Computation | 3 |
CSCIÌý5745 | Advanced Techniques in Artificial Intelligence | 3 |
CSCIÌý5760 | Deep Learning | 3 |
STATÌý5087 | Applied Regression | 3 |
STATÌý5088 | Bayesian Statistics and Statistical Computing | 3 |
Artificial Intelligence Applications
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:
Code | Title | Credits |
---|---|---|
BCBÌý5350 | Machine Learning in Bioinformatics | 3 |
BMEÌý5150 | Brain Computer Interface | 3 |
CSCIÌý5070 | Algorithmic Fairness | 3 |
CSCIÌý5570 | Machine Learning for Networks | 3 |
CSCIÌý5830 | Computer Vision | 3 |
CSCIÌý5845 | Natural Language Processing | 3 |
GISÌý5092 | Machine Learning for GIS and Remote Sensing | 3 |
HDSÌý5330 | Predictive Modeling and Machine Learning | 3 |
Artificial Intelligence Supporting CoursesÌý
AI supporting courses must serve one of three purposes:
- Provide knowledge in a specific domain area that prepares students to apply artificial intelligence or machine learning to solve problems in that particular domain.
- Provide richer foundational knowledge in a supporting area (e.g. algorithms, statistics) that prepares students to understand, enhance, or implement artificial intelligence techniques.
- Provide exploration of the broader impacts of artificial intelligence. Students may apply at most six credits of such courses to the degree.
The full list of approved courses is maintained by the computer science department and includes:
Code | Title | Credits |
---|---|---|
BCBÌý5200 | Introduction Bioinformatics I | 3 |
BCBÌý5250 | Introduction Bioinformatics II | 3 |
CSCIÌý5100 | Algorithms | 3 |
CSCIÌý5530 | Computer Security | 3 |
CSCIÌý5550 | Computer Networks | 3 |
CSCIÌý5610 | Concurrent and Parallel Programming | 3 |
CSCIÌý5620 | Distributed Computing | 3 |
CSCIÌý5710 | Databases | 3 |
CSCIÌý5910 | Internship with Industry | 1-3 |
CSCIÌý5970 | Research Topics | 1-3 |
CSCIÌý5980 | Graduate Reading Course | 1-3 |
ECEÌý5153 | Image Processing | 3 |
ECEÌý5226 | Mobile Robotics | 3 |
LAWÌý8235 | Information Privacy Law | 2-3 |
PSYÌý5120 | Memory & Cognition | 3 |
SOCÌý5670 | Spatial Demography – Applied Spatial Statistics | 3 |
Artificial Intelligence Electives
The remaining electives can be taken from any of the foundations, applications or supporting categories.
Foundational Coursework
Students without a previous degree in Computer Science or a closely related field may be required to take additional courses to satisfy pre-requisites. Typically, this will not impact time to degree.
Non-Course Requirements
All graduate degree candidates must complete an exit survey with the department during their final semester.
Continuation Standards
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 enrollmentÌýunless otherwise noted. Ìý
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.
Year One | ||
---|---|---|
Fall | Credits | |
CSCIÌý5030 | Principles of Software Development | 3 |
CSCIÌý5740 | Introduction to Artificial Intelligence | 3 |
CSCIÌý5750 | Introduction to Machine Learning | 3 |
Ìý | Credits | 9 |
Spring | ||
CSCIÌý5050 | Computing and Society | 3 |
Artificial Intelligence Foundations | 3 | |
Artificial Intelligence Applications | 3 | |
Ìý | Credits | 9 |
Year Two | ||
Fall | ||
Additional course in either Artificial Intelligence Foundations or Applications | 3 | |
Artificial Intelligence Elective | 3 | |
Ìý | Credits | 6 |
Spring | ||
CSCIÌý5961 | Artificial Intelligence Capstone Project | 3 |
CSCIÌý5750 | Introduction to Machine Learning | 3 |
Artificial Intelligence Elective | 3 | |
Ìý | Credits | 9 |
Ìý | Total Credits | 33 |
For questions about admissions, applicants currently in the United States should contact graduate@slu.edu and applicants elsewhere should contactÌýglobalgrad@slu.edu. ÌýÌý
For other questions about the program or curriculum, contact the computer science department at cs@slu.edu.