Artificial Intelligence Engineering is a fast-growing field in the STEM industry essential to create more robust, high-performance digital elements. The program is designed to prepare students with advanced knowledge and skills in artificial intelligence, machine learning, and deep learning in the engineering domain. The AI program consists of core courses training students to become highly skilled AI engineers who can develop and apply AI-based solutions within the engineering discipline. This STEM program focuses on various AI engineering frameworks and representations for inventing, tuning, and specializing AI structures and algorithms. The engineering topics include various AI aspects such as pattern recognition, machine learning, deep learning, natural language processing, computer vision, etc., fundamental to building systems that can intelligently interact with humans and other digital processes. The program will prepare students for career positions at the entry level such as AI Engineer, Machine Learning Engineer, Analytics Research Scientist, Data Scientist Engineer, etc. The market for the workforce in the industry, commercially and academically, is continually growing worldwide, which places our graduates in very high demand. BAU is located in one of the largest areas for a collection of technology companies, which graduates will tremendously benefit from while they pursue their degrees for experiences and after graduation for job opportunities.
The mission of this program is to prepare students with advanced knowledge and skills in artificial intelligence, machine learning, and deep learning in the engineering domain. The AI program consists of core courses training students to become highly skilled AI engineers who can develop and apply AI-based solutions within the engineering discipline. The program also prepares students to become computing technicians in applying AI and Machine Learning to their fields of expertise. This STEM program focuses on various AI engineering frameworks and representations for inventing, tuning, and specializing AI structures and algorithms. Students learn various AI aspects such as pattern recognition, machine learning, deep learning, natural language processing, computer vision, etc., fundamental to building systems that can intelligently interact with humans and other digital processes. The program will provide students with the opportunity to attain an undergraduate certificate that will enhance their opportunities in various positions within the AI engineering field. | ||||
Current Learning Goals | Proposed Learning Goals | Bloom Taxonomy | Course(s) | Assessment Methods
|
Understand the scientific theories and methodologies of AI and Machine Learning trends used in designing and implementing AI-based processes and products. | Knowledge and comprehend | CMPS 310 CMPS 322 | Graded assignments (Written and Hands-on assignments assignments)
Exams Final Project | |
Apply the foundation and models of machine learning and deep learning to create AI solutions that can overcome digital challenges in various domains. | Application and analysis | CMPS 322 CMPS 411
| Graded assignments (Written and Hands-on assignments assignments)
Exams Final Project | |
Utilize various AI and Machine Learning tools for analyzing, inventing, and tuning AI Algorithms for new and existing digital products. | Application and analysis | CMPS 122 CMPS 322 CMPS 411
| Graded assignments (Written and Hands-on assignments assignments)
Exams Final Project | |
Develop reliable and scalable AI-based applications using the latest methods and technologies to ensure usability, availability, integrity, and security. | Synthesis | CMPS 122 CMPS 202 CMPS 205 CMPS 310 CMPS 322 CMPS 411 | Graded assignments (Written and Hands-on assignments)
Exams
AI Development Projects |
Pipop Nuangpookka, PhD
Dr. Pipop Nuangpookka is the Chair of Information Management Science Program and Director of Distance Education at Bay Atlantic University.
Dr. Pipop Nuangpookka has been working as an Analyst/Programmer, Infrastructure Technologies, and Infrastructure Security since the year 2005. While working in the IT industry as a technical person, He started teaching in Computer Science, Information Technology, and Cybersecurity disciplines at several universities in Washington, DC, and Northern Virginia area in 2007. The various courses include Introduction to Computer Science, Computer Network, Computer Programming Languages (i.e., Java, Python, C, C++, ML, and Prolog), Structure of Programming Languages, Software Engineering, Data Structures & Algorithms, Database Management System (i.e., MS SQL Server, Oracle, MySQL, and SQL/PLSQL), Web Application Development and Security, Technology Management, and Technical Capstone Project. Dr. Nuangpookka graduated with a Bachelor’s Degree in Business Administration from Payap University, Thailand, in 1999, and a Master’s Degree in Computer Science in 2004 from Marymount University, Arlington, Virginia. He also earned a Doctor of Science Degree in Cybersecurity at Marymount University in 2020. His research interests are Non-Destructive Method of Detecting Hardware Attack, Personally Identifiable Information (PII) Security Controls, Web Server Protection, and Bilingual Translator Programming. He is a co-author of scholarly papers published in the Journal of Computing Sciences in Colleges/ACM digital library, including “Hardware-Tampering Security Risks in the Supply Chain” and “Fileless Malware and Programmatic Method of Detection.”
Core Requirements: 18 Credits (6 courses) A minimum grade of C required for all courses including the prerequisites.
Admission Prerequisite: Fundamental of Algebra or Take MATH 104 College Algebra at BAU in the first semester.
Course Code | Course | Pre-requisites | Credits |
CMPS 122 | Introduction to Programming I | 3 | |
CMPS 202 | Data Structures and Algorithms I | CMPS 122 | 3 |
CMPS 205 | Data Structures and Algorithms II | CMPS 202 | 3 |
CMPS 310 | Introduction to Artificial Intelligence | CMPS 202 | 3 |
CMPS 322 | Machine Learning and Pattern Recognition | CMPS 202 | 3 |
CMPS 411 | Fundamentals of Deep Learning | CMPS 202 | 3 |
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