M.S. in Artificial Intelligence
Degree Requirements
Students in the Master of Science in Artificial Intelligence (MSAI) program must successfully complete 30 credits based on any of the following options:
Courses (30 credits)
Courses (27 credits) + MS Project (3 credits)
Courses (24 credits) + MS Thesis (6 credits)
Independent of the chosen option, 4 out of 7 core courses are required (detailed below).
If a student chooses the MS thesis option, the thesis must be related to Artificial Intelligence and requires approval from the Program Director.
Students may choose an elective outside the list after approval of their respective advisor.
M.S. in Artificial Intelligence
Core Course Requirements
Students are required to take four (4) core courses from the following list.
| Code | Title | Credits |
|---|---|---|
| DS 675 | Machine Learning | 3 |
| DS 680 | Natural Language Processing | 3 |
| DS 669 | Reinforcement Learning | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| DS 677 | Deep Learning | 3 |
| CS 670 | Artificial Intelligence | 3 |
| CS 634 | Data Mining | 3 |
Electives
| Code | Title | Credits |
|---|---|---|
| CS 631 | Data Management System Design | 3 |
| CS 632 | Advanced Database System Design | 3 |
| CS 659 | Image Processing and Analysis | 3 |
| CS 681 | Computer Vision | 3 |
| CS 708 | Advanced Data Security and Privacy | 3 |
| CS 732 | Advanced Machine Learning | 3 |
| CS 735 | High Performance Analytics Dat | 3 |
| CS 744 | Data Mining and Management in Bioinformatics | 3 |
| CS 782 | Pattern Recognition and Applications | 3 |
| CS 786 | Seminar in Computer Science II (Deep Learning on Graphs) | 3 |
| IS 687 | Transaction Mining and Fraud Detection | 3 |
| IS 688 | Web Mining | 3 |
| MATH 644 | Regression Analysis Methods | 3 |
| MATH 665 | Statistical Inference | 3 |
| MATH 678 | Stat Methods in Data Science | 3 |
| MATH 680 | Advanced Statistical Learning | 3 |
| MATH 699 | Design and Analysis of Experiments | 3 |
| ECE 605 | Discrete Event Dynamic Systems | 3 |
| ECE 754 | Statistical Machine Learning for Engineers and Data Scientists | 3 |
| ECE 776 | Information Theory | 3 |
| ECE 788 | Selected Topics in Electrical and Computer Engineering (Computational Intelligence) | 3 |
Sample course sequence M.S. in Artificial Intelligence
Year 1 Fall:
- CS 675 Machine Learning
- CS 634 Data Mining
- CS 670 Artificial Intelligence
Year 1 Spring:
- DS 677 Deep Learning
- DS 680 Natural Language Processing
- DS 669 Reinforcement Learning
Year 2 Fall:
- DS 789: Trustworthy AI
- Free elective or Master project course
- Free elective
Year 2 Spring:
- Free elective or Master thesis course
- Free elective or Master project course
- Free elective
The requirements for the MS in Artificial Intelligence program are as follows:
· 30 credits are required, which can be satisfied by any one of the following approaches:
o Courses only (30 credits)
o Courses (27 credits) + MS Project (3 credits)
o Courses (24 credits) + MS Thesis (6 credits)
· Four out of seven core courses are required
If a student chooses to work on an MS project or an MS thesis, the project or thesis must be related to Artificial Intelligence.
Admission Requirements
To be eligible for admission, a student must have a Bachelor of Science degree with a minimum GPA of 3.0 on a 4.0 scale and have completed the following undergraduate coursework:
· Calculus I and II (equivalent to the NJIT courses Math 111 and Math 112)
o Derivatives, integrals, applications
o Business calculus may suffice and will be considered on a case by case basis
· Introduction to Programming (equivalent to the NJIT CS 113 course)
o Basic programming constructs, writing and debugging programs, iteration, recursion, arrays, lists
· Data Structures and Algorithms (equivalent to the NJIT CS 114 course)
o Basic data structures (lists, arrays, hash tables), search and sort, algorithm analysis
· Probability and Statistics (equivalent to the NJIT Math 333 course)
o Random variables, probability distributions, sample mean and variance
o Basic probability or statistics course separately will also suffice
· Linear Algebra (equivalent to the NJIT Math 337 course)
o Vector spaces, dot products, Euclidean norm, matrices
International students will have to take TOEFL and GRE exams and meet the minimum requirements for admission to graduate programs at NJIT as per the NJIT policy.
Students who do not meet all of the above requirements but hold a BS or BA a degree in a technical scientific subject will be evaluated on a case-by-case basis and may be admitted to the program after they successfully complete a relevant graduate certificate.
Core Course Requirements
Students are required to take four (4) core courses from the following list.
| Code | Title | Credits |
|---|---|---|
| DS 675 | Machine Learning | 3 |
| DS 680 | Natural Language Processing | 3 |
| DS 669 | Reinforcement Learning | 3 |
| DS 789 | Trustworthy Artificial Intelligence | 3 |
| DS 677 | Deep Learning | 3 |
| CS 670 | Artificial Intelligence | 3 |
| CS 634 | Data Mining | 3 |
Electives
Students will have a wide array of Artificial Intelligence-related electives to choose from. Students would have to take required pre-requisites or seek approval of instructor for the elective courses.
