Master of Science in Artificial Intelligence and Machine Learning

Master of Science in Artificial Intelligence
and
Machine Learning

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Master of Science in Artificial Intelligence and Machine Learning

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Overview

In support of the university’s mission, the Master of Science in Artificial Intelligence and Machine Learning (MSAIML) is designed to appeal to a broad range of individuals. The program balances theory with practice, offers an extensive set of traditional and state-of-the-art courses, and provides the necessary flexibility to accommodate students with various backgrounds, including computer professionals who want to expand their understanding of AI & ML, as well as individuals whose undergraduate degrees are not in Computer Science but wish to broaden their knowledge in AI & ML.

PROGRAM OUTCOMES:

Apply AI and ML algorithms to draw inferences, design smart application to solve real-world problems, and to automate the development of AI systems and components.
Model human behaviors to develop Human-AI systems and evaluate their performance.
Improve overall integrated system performance to influence human performance and learning.
Apply social, ethical, and legal principles of technologies and their applications in the AI and ML field.
Communicate effectively individually or in cross-functional teams.

CAREER PATH:

AI Specialist
Applied Artificial Intelligence and Machine Learning-Scientist
AWS Machine Learning Engineer
Robotic Process Automation Programmer
Artificial Intelligence Engineer
Robotics Programmer
Machine Learning Engineer
Instructor at a college or university teaching AI and ML in addition to Computer Science courses

ASSOCIATED MICROCREDENTIALS :

Artificial Intelligence/Machine Learning Engineer: (ALMLE)
AI Specialist (AISP)
AWS Machine Learning Engineer (AWSMLE)
Robotic Process Automation Programmer (RPAP)

CURRICULUM OVERVIEW

The Master of Science in AI and ML requires completion of 36 credits. Students will take 12 credits of core courses, 6 credits for career application, and 18 credits in AI and ML content area.

Area	Number of Courses	Credits
Core Courses	4	12
Career Application Courses	2	6
Specialization Courses	6	18
Total	12	36

Program Prerequisites

All new AI and ML program students need certain basic skills to prepare them for success in the AI and ML Program. The AI and ML degree provides a broad understanding of the theory and technology of this field. Students who do not have the required background need to take some or all of the prerequisites before taking the core Courses. Thus, to be successful, students must have a background in the following courses.

Code	Course Title	Prerequisite	Microcredentials	Credits
COMP 109	Computer Algorithm and Programming Logic Using Python	None		3
COMP 260	Introduction to Operating Systems	COMP 109		3
COMP 270	Essentials of Networking	COMP 109		3
COMP 329	Data Structures and Algorithm Analysis	None		3
COMP 350	Database Concepts	None		3

Core Courses (4 Courses – 12 Credits)

These courses provide a breadth of foundational knowledge to implement computer interfaces, software design, communication between systems, and how to manage IT systems. These are all crucial elements for IT professionals to apply these building blocks to any given system or project.

Code	Course Title	Prerequisite	Microcredentials	Credits
COMP 501	Advanced Operating Systems	COMP 260		3
COMP 502	Design and Analysis of Algorithms	COMP 329		3
COMP 503	Networking and Telecommunications	COMP 270		3
COMP 504	Database Management Systems	COMP 350		3

Application Courses (2 Courses – 6 Credits)

These courses offer an opportunity for students to apply what they have learned throughout the program to a practical project or to a master’s thesis. While the practical project provides for application of knowledge acquired throughout the program and would be represent work that could demonstrate career-readiness to potential employers, the thesis would generally serve to demonstrate a student’s research potential and could be used to demonstrate readiness for doctoral work. Regardless of the option, students will demonstrate basic research knowledge and abilities, which would be used toward completion of either the project or thesis.

Code	Course Title	Prerequisite	Microcredentials	Credits
COMP 505	Research Methods	None		3
Choose One
COMP 681	AI and ML Capstone Project	COMP 505		3
COMP 698	Master Thesis	COMP 505		3

Specialization Courses (Any 6 Courses – 18 Credits)

These advanced courses cover the depth of topics related to AI and ML and allow students to develop their knowledge based upon their intended professional trajectories.

Code	Course Title	Prerequisite	Microcredentials	Credits
COMP 513	Robotics Design and Programming	COMP 329	ALMLE/ RPAP	3
COMP 514	Neural Networks	None		3
COMP 515	Pattern Recognition	None	AISP/ RPAP	3
COMP 516	Deep Learning	None	ALMLE/ AISP	3
COMP 517	Special Topics in AI	None		3
COMP 518	Special Topics in ML	None	AWSMLE	3
COMP 521	Smart Devices Design and applications	None	AISP/ RPAP	3
COMP 522	Data Mining	COMP 504	AWSMLE	3
COMP 593	Internship I in AI and Machine Learning	Completion of core courses and 50% of the program courses		3
COMP 610	Cognitive Computing	None	ALMLE/ AWSMLE	3
COMP 611	Data Warehousing	COMP 504	DWE	3
COMP 613	Game Design	COMP 502	RPAP	3
COMP 614	Speech Recognition	None	AISP/ RPAP	3
COMP 617	AWS Certified Machine Learning	None	AWS Certificate/ AWSMLE	3
COMP 618	10 Google Machine Learning	None	Google Certificate	3
COMP 693	Internship II in AI and Machine Learning	COMP 593		3

NOTE: Students who wish to take a course that is offered by in another program may petition to do so to their advisor by providing justification for the relevance of the addition as part of their professional trajectory, their intended consulting project, and/or personal interest. A maximum of 2 courses from can be applied from another program.

Graduate Administration Requirements

Interested in Our MASTER OF SCIENCE IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Program?

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