University at Buffalo (UB)
Engineering Science (Artificial Intelligence) MS
Artificial Intelligence (AI) is a term used to describe machines or software that are capable of addressing problems that one would typically say require some amount of human intelligence to solve.
AI is more widely used in academics to describe computers and computer software that are capable of intelligent behavior. At the University at Buffalo, we are focused on preparing students for a future filled with AI technology by offering an Engineering Science MS program with a focus on AI.
This Engineering Sciences MS with a course focus on Artificial Intelligence (AI) is a year and a half (30 credit hour) multidisciplinary program that trains students in the areas of machine learning, AI programming languages, deep learning algorithms, and advanced artificial neural networks that use predictive analytics to solve real-world problems. The program features a set of foundational core courses in AI and the flexibility to choose from elective concentration areas that include data analytics, computational linguistics and information retrieval, machine learning and computer vision, knowledge representation, and robotics.
Over the next 10 years, employment demand is expected to grow more than 22% in AI related career paths, including AI engineers, machine learning engineers, data scientists, research computer scientists, modeling analysts, business intelligence developers and AI managers.
A master’s degree in this area provides students with advanced coursework, research opportunities, and leadership training that opens doors to more career opportunities.
This program is STEM approved, allowing international students the opportunity to apply for the 24-month STEM OPT extension.
Program Director
Sreyasee Das Bhattacharjee
349 Davis Hall
sreyasee@buffalo.edu
(716) 645-4769
To apply to the Engineering Science (Artificial Intelligence) MS program, it is recommended that students hold a bachelor's degree in engineering, computer science, mathematics, physical sciences or a related field. Students should have a solid programming background as indicated by formal coursework or by a comprehensive online (MOOC) study. Applicants with non-engineering degrees are welcome to apply and will be evaluated on an individual basis.
Applicants must also provide the following application materials:
Students can apply online. New applicants must create an account. If you have applied in the past, you will be asked to log in to your existing account to start a new application.
The application includes sections that will ask you to provide us with personal/biographical information, contact information, citizenship details, your past or current college/university, and degree details. You will be able to enter the names and contact information of your recommenders and upload supporting materials to your application. You may upload supporting documents while completing the application form or after you have submitted your application in your Application Status Portal.
You do not need official transcripts or test scores to apply. Unofficial copies are sufficient for application review, but students are encouraged to provide official copies if they have them.
A nonrefundable fee of $85 is required to apply. After you submit your application form, you can pay the application fee online in your Application Status Portal with a credit card or e-check.
Copies of transcript(s) for all post-secondary schoolwork must be uploaded with your application for initial review. Upon an offer of admission, accepted applicants will be required to submit official transcripts and proof of degree(s).
GRE scores are optional for this program. If you would like to take the GRE, you can arrange to take it through the Educational Testing Service (ETS). Official scores should come directly from ETS.
Note: the University at Buffalo Institutional Code is 2925.
Upload a resume or curriculum vitae (CV) to your application. Your resume or CV should include details about your education, employment, and internship history. Pertinent research experience should be included as well.
Two letters of recommendation are required to apply to this program. Letters are automatically requested when you enter recommenders' names and email addresses in your application. While we will accept letters from professional sources, we strongly prefer letters from professors acquainted with your academic interests, achievements, and abilities.
International applicants are required to provide proof of English proficiency. The chart below outlines acceptable test types and the university's minimum score required for admission. All applicants whose native language is not English will be required to provide proof of English proficiency.
Copies may be submitted for initial review. Official scores must be sent directly to UB from the testing agency.
| Test Type | University Minimum Score | To Order... |
|---|---|---|
| TOEFL (IBT) (including MyBest scores) TOEFL Home Edition | 79 | Use Institution Code 2925 |
| TOEFL Essentials | 8.5 | Use Institution Code 2925 |
| TOEFL ITP Plus | 550 | Use Institution Code 2925 |
| IELTS and IELTS Indicator | 6.5 | Select "University at Buffalo, State University of New York (SUNY)" |
| 55 | Select "University at Buffalo, State University of New York (SUNY)" | |
| Cambridge English Proficiency (CPE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Cambridge English Advanced (CAE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Duolingo English Test (DET) | 120 | ADA Department of Testing will report your official scores to central application services |
Exam results must be dated within two years from your proposed date of admission and remain valid upon entering the term for which you applied.
It is strongly recommended to make test arrangements early in the year so sufficient time can be allowed for the results to be reported before application deadlines.
Exemptions to English Language Proficiency requirements can be found on The Graduate School website.
Application Deadline
We accept applications on a rolling basis throughout the year, but encourage all prospective students to submit their applications by February 15.
This program is primarily course-based, but students do have the option to complete a masters project, which can include an internship in industry.
Please note this program is NOT a pathway to further education in AI such as a PhD. Students with a Computer Science background, or that intend to one day apply for a PhD program to research AI would be best served by an MS degree from Computer Science and Engineering.
For students who enrolled Falll 2022 and earlier, the curriculumn is comprised of 5 core courses, and either 4 electives and a 3-credit masters project or 5 electives and a capstone essay and presentation in the final semester. The program is flexible and students can enroll either full-time or part-time (if elgible). Students can tailor the program to their own interests and careers via elective courses and degree project.
Effective Fall 2023, the curriculumn is moving to a fixed cohort-style model, comprised of 8 core classes (24 credits), 1 elective, and 1 capstone experience (3-credit project or internship, or a second 3-credit elective).
Core Courses
- EAS595 Fundamentals of Artificial Intelligence
- CSE 555 Introduction to Pattern Recognition
- CSE 574 Intro Machine Learning
- EAS 501 Intro to Numerical Mathematics for Computing and Data Science
- CSE 568 Robotics Algorithms
Electives Groupings
Data Analytics Group
- CSE 601 Data Mining and Bioinformatics
- EE 539 Principles of Information Theory and Coding
- EE 559 Big Data Analytics
- MAE 509 Probability and Stochastic Process
Machine Learning and Computer Vision Group
- CSE 674 Advanced Machine Learning
- CSE 676 Deep Learning
- MAE 600 Deep Learning for Mechanical Engineering
- CSE 573 Computer Vision and Image Processing
Human/Machine Interaction Group
- MAE 527 Intelligent Machine Interfaces
- MAE 502 Human-Robot Interaction
- IE 535 Centered Design for Automation Interactions
Computational Linguistics and Information Retrieval Group
- CSE 567 Computational Linguistics
- CSE 535 Information Retrieval
- CSE 635 Natural Language Processing and Text Mining
Knowledge Representation Group
- CSE 563 Knowledge Representation
- EAS 524 Ontological Engineering (Cross-Listed with Philosophy)
Other
- MAE 593 Robotics 1
- CE 551 Computer-Aided Research in the Chemical and Materials Sciences
- CSE 526 Blockchain Application Development
Core Classes
- EAS 510 Basics of Artificial Intelligence (Formerly 595 Special Topics: Fundamentals of Artificial Intelligence)
- EAS 501 Numerical Math for Computing and Data Science
- CSE 574 Intro to Machine Learning
- CSE 531 Algorithms Analysis and Design
- CSE 587 Data Intensive Computing
- CSE 573 Computer Vision OR CSE 635 NLP and Text Mining
- CSE 546 Reinforcement Learning
- CSE 555 Pattern Recognition
Electives
- CSE 601 Data Mining and Bioinformatics
- CSE 568 Robotics Algorithms
- CSE 674 Advanced Machine Learning
- CSE 676 Deep Learning
- CSE 567 Computational Linguistics
- CSE 535 Information Retrieval
- CSE 563 Knowledge Representation
- CSE 526 Blockchain Application Development
- EE 539 Principles of Information Theory and Coding
- EE 559 Big Data Analytics
- MAE 509 Probability and Stochastic Process
- MAE 600 Deep Learning for Mechanical Engineering
- MAE 527 Intelligent Machine Interfaces
- MAE 502 Human-Robot Interaction
- MAE 593 Robotics 1
- IE 535 Centered Design for Automation Interactions
- CE 551 Computer-Aided Research in the Chemical and Materials Sciences
Have questions?
For degree-specific related questions, please contact the Graduate Coordinator at engsci@buffalo.edu.
For admissions-related questions, plesae contact gradeng@buffalo.edu.