• Engineering Sciences MS: Focus on Artificial Intelligence

    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.

    Computer chip in the shape of a human brain.

    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.

    About the Program

    This Engineering Sciences MS with a course focus on Artificial Intelligence (AI) is a 1 and 1/2 year (30 credit hour) multidisciplinary program. It is designed to train students in the areas of machine learning, programming languages that are needed to design intelligent agents, deep learning algorithms, and advanced artificial neural networks that use predictive analytics to solve real-world problems. Students in this program are offered 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.

    The U.S. Bureau of Labor Statistics (BLS) projects “much faster than average” job growth in AI related career paths, including machine learning engineer, data scientist, research scientist, research and development engineering (R&D), business intelligence developer and computer vision engineer. A master’s degree in this area provides students with advanced coursework, research opportunities, and leadership training that opens doors to more career opportunities.

    Admissions Requirements

    A baccalaureate degree in engineering, computer science, mathematics, physical sciences or a related field is recommended. 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. All applications will be reviewed based on the innovative Mastermind Europe “best-fit” philosophy examining each applicant’s abilities on an individual long-term “potential” basis.

    The GRE is required for this program.

    Earning the Degree

    This program is primarily course-based, but thesis, project and portfolios options are also required as part of a capstone project.

    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.

    Students must complete 9 courses (27 credits) and a capstone project (3 credits). The program is flexible and students can enroll either full-time or part-time. Students can tailor the program to their own interests and careers via elective courses and degree project.

    The program will accept its first class in the spring of 2020, and then annually starting in the fall of 2020.

    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 
    • Additional courses TBD

    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

    Computational Linguistics and Information Retrieval Group

    • CSE 567 Computational Linguistics
    • CSE 535 Information Retrieval
    • CSE 635 Natural Language Processing and Text Mining

    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

    Knowledge Representation Group

    • CSE 563 Knowledge Representation
    • EAS 524 Ontological Engineering (Cross-Listed with Philosophy)

    Human/Machine Interaction Group

    • MAE 527 Intelligent Machine Interfaces
    • MAE 502 Human-Robot Interaction
    • IE 535 Centered Design for Automation Interactions

    Other

    • MAE 593 Robotics 1
    • CE 551 Computer-Aided Research in the Chemical and Materials Sciences

    Capstone Project

    Students engage in a hands-on capstone projects with faculty, industry, and other partners that centers on the application of specific principles that were learned through their coursework.