M.S. in Artificial Intelligence
M.S. in Artificial Intelligence
The M.S. program in Artificial Intelligence acclimates students to the ongoing AI revolution that has already produced computer programs with problem-solving and content-generating abilities that complement and enhance human abilities. The program offers theoretical and practical knowledge in various areas of AI, including Natural Language Understanding and Generation, Image Understanding, Reasoning, and Planning. It empowers students to apply AI techniques in a wide range of application domains.
Prerequisites
Applicants should have a bachelor's degree in the general area of Computing, from an accredited University. Applicants with a bachelor's degree in STEM or related professional experience can start with the graduate certificate and then apply to the M.S. program. Further information can be found in the program's webpage.
Degree Requirements
The program requires the completion of 30 credits. These are satisfied by taking 10 courses, as indicated in the following table.
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| Select at least four of the following: | ||
| Introduction to Big Data | ||
| Reinforcement Learning | ||
| Artificial Intelligence | ||
| Machine Learning | ||
| Deep Learning | ||
| Natural Language Processing | ||
| Trustworthy Artificial Intelligence | ||
| After the 4 core courses are completed, any of the remaining core courses listed can count towards the elective requirements. | ||
| Elective Courses | ||
| Data Mining | ||
| Image Processing and Analysis | ||
| Computer Vision | ||
| Advanced Machine Learning | ||
| High Performance Data Analytics | ||
| Pattern Recognition and Applications | ||
| Python and Mathematics for Machine Learning * | ||
| Selected Topics in Data Science | ||
| Information Theory | ||
| Probability Distributions | ||
| Introduction to Biostatistics | ||
| Statistical Inference | ||
| Statistical Methods in Data Science | ||
| Introduction to Robotics | ||
| Deep Learning in Business | ||
| Project and Thesis Courses | ||
| Master's Project | ||
| Master's Thesis | ||
- *
DS 637 is recommended as an introductory course, offering a review of mathematics for machine learning to students with a limited background in mathematics or programming.
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
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.
