The Master of Science in Artificial Intelligence program is designed to give students a comprehensive framework for AI with specialization in one of five areas: vision, intelligent interaction, robotics and agent-based systems, machine learning, and knowledge management and reasoning. Students may choose from three options: specialization, thesis, or coursework only. Students will engage in an extensive core intended to develop depth in all core concepts that build a foundation for AI theory and practice. Students will also be given the opportunity to build on the core knowledge of AI by taking a variety of elective courses, selected from colleges throughout campus, to explore key contextual areas or more complex technical applications. Program graduates will be well positioned to attain research and development positions in a rapidly growing field or to progress into doctoral-degree-related fields.
The Master of Science in Artificial Intelligence is comprised of eight courses: five core courses, two electives to be chosen from one of five specialization areas or coursework option, and one elective. The core courses are designed and developed by Khoury College faculty. Elective courses consist of graduate courses offered in Khoury and other partner colleges, including College of Arts, Media and Design; College of Engineering; College of Science; and College of Social Sciences and Humanities.
Prerequisites
The Master of Science in Artificial Intelligence curriculum is tailored toward technically or mathematically trained students. To ensure that all students have the foundation necessary to be successful in this program, each incoming student must either complete two introductory courses at Northeastern University or complete two placement exams administered one week prior to the beginning of the semester. The two exams cover fundamentals of computer science and programming skills and basic statistics, probability, and linear algebra. This admission requirement can also be fulfilled by successful completion of Introduction to Programming for Data Science (DS 5010) and Introduction to Linear Algebra and Probability for Data Science (DS 5020). The introductory courses that are completed before the student’s admission to the program are not counted as credit toward the degree but are included in the student’s cumulative grade-point average. Students may have the option of taking the courses before they begin the program or during the first semester of study. Students are required to get a passing grade in each section of the placement exams in order to be admitted to the program. If the student does not get a passing grade in a part of the placement exam, then the student must take the corresponding introductory course. Students that do not achieve a B or better in the introductory courses will be required to retake the courses.
Complete all courses and requirements listed below unless otherwise indicated.
Core Requirements
A cumulative GPA of 3.000 or higher is required in the following core courses:
| Code | Title | Hours |
|---|---|---|
| Intelligence | ||
| CS 5100 | Foundations of Artificial Intelligence | 4 |
| Programing and Algorithms | ||
| CS 5010 | Programming Design Paradigm | 4 |
| CS 5800 | Algorithms | 4 |
| Machine Learning | ||
| CS 6140 | Machine Learning | 4 |
| Interaction | ||
| Complete four semester hours from the following: 1 | 4 | |
| Artificial Intelligence for Human-Computer Interaction | ||
| Computer/Human Interaction | ||
Options
Complete one of the following options:
Specialization Option
| Code | Title | Hours |
|---|---|---|
| Complete two courses from one of the following specializations: | 8 | |
| Vision | ||
| Pattern Recognition and Computer Vision | ||
| Special Topics in Artificial Intelligence | ||
| Computer Vision | ||
| Advanced Computer Vision | ||
| Intelligent Interaction | ||
| Game Artificial Intelligence | ||
| Computer/Human Interaction | ||
| Theory and Methods in Human Computer Interaction | ||
| Human Cognitive Processes | ||
| Robotics and Agent-Based Systems | ||
| Reinforcement Learning and Sequential Decision Making | ||
| Robotic Science and Systems | ||
| Mobile Robotics | ||
| Robotics Sensing and Navigation | ||
| Machine Learning | ||
| Reinforcement Learning and Sequential Decision Making | ||
| Data Mining Techniques | ||
| Advanced Machine Learning | ||
or EECE 7397 | Advanced Machine Learning | |
| Deep Learning | ||
| Unsupervised Machine Learning and Data Mining | ||
| Statistical Inference: An Introduction for Engineers and Data Analysts | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Statistics for Bioinformatics | ||
| Knowledge Management and Reasoning | ||
| Natural Language Processing | ||
| Information Retrieval | ||
| Data Mining Techniques | ||
| Special Topics in Data Science | ||
| Complete one course from the electives list below or an additional course chosen from the specialization area above, outside of the student's selected specialization area. | 4 | |
Coursework Option
| Code | Title | Hours |
|---|---|---|
| Complete 12 semester hours from the electives or specialization course lists. Students can take up to one course from any Khoury College 5000–6000-level course. | 12 | |
Thesis Option
| Code | Title | Hours |
|---|---|---|
| CS 8674 | Master’s Project | 4 |
| CS 7990 | Thesis | 4 |
| Complete 4 semester hours from the electives or specialization course lists. | 4 | |
Electives List
| Code | Title | Hours |
|---|---|---|
| Special Topics in Artificial Intelligence | ||
| Master’s Project | ||
| Information Theory | ||
| Game Design and Analysis | ||
| AI Ethics |
- 1
If students take both interaction core courses, one may count as an elective.
Program Credit/GPA Requirements
32 total semester hours required
Minimum 3.000 GPA required