Course Code:
MSAI 7350
Course Name: Advanced Data Science
Credit Hours: 3
Prerequisites: NONE
Course Description:
This course offers an accessible and engaging introduction to data science, designed for students from all academic backgrounds. Using the STAR framework (State, Transform, Analyze, Report), students will explore the full data science process from understanding a question to communicating data-driven insights. Through real-world case studies, hands-on exercises, and ethical reflections, students will learn fundamental skills in data wrangling, analysis, visualization, and responsible data use. The course emphasizes practical problem-solving and empowers learners to apply data science across various domains, regardless of prior technical experience.
Course Code:
MSAI 7330
Course Name: Artificial Intelligence
Credit Hours: 3
Prerequisites: none
Course Description:
This course introduces students to the fundamental concepts, techniques, and applications of Artificial Intelligence (AI). It covers core AI topics such as intelligent agents, search strategies, knowledge representation, reasoning, machine learning, and problem-solving under uncertainty. Students will also explore practical AI applications in areas such as natural language processing, computer vision, and robotics. Emphasis is placed on both theoretical understanding and hands-on experience through projects and assignments using modern AI tools and platforms.
Course Code: MSAI 7381
Course Name: Artificial Intelligence for Cybersecurity
Credit Hours: 3
Prerequisites: none
Course Description:
This course explores the integration of artificial intelligence (AI) and machine learning (ML) techniques in modern cybersecurity practices. Students will examine how AI can enhance threat detection, automate incident response, and support proactive defense strategies. The course covers both theoretical foundations and practical applications, including anomaly detection, phishing identification, intrusion detection systems (IDS), and botnet analysis. Learners will design and implement AI models and tools to solve real-world cybersecurity challenges.
Course Code:
MSAI 7332
Course Name: Game AI
Credit Hours: 3
Prerequisites: MSAI 7330
Course Description:
This course offers a thorough exploration of artificial intelligence in the context of digital games, emphasizing the development of intelligent agents using both traditional AI methods and machine learning techniques. Students will examine the Game AI design space, addressing factors such as observability, randomness, and timing resolution. Key topics include agent navigation, decision-making processes, and strategic behavior in gameplay. The course also delves into unconventional uses of Game AI, including character animation, evolutionary systems, and social behavior modeling. With a strong focus on practical, project-based learning, students will engage with real-world applications of Game AI.
Course Code:
MSAI 7360
Course Name: Capstone Project I
Credit Hours: 3
Prerequisites: MSAI 7340
Course Description:
Capstone Project I is the first phase of a two-part capstone sequence designed to guide students through the early stages of developing a substantial and original AI-based solution to a real-world problem. In this course, students identify a problem area, conduct a detailed literature review, define project objectives, and develop a comprehensive project proposal. Emphasis is placed on problem formulation, research methodology, ethical considerations, and project planning.
Course Code:
MSAI 7361
Course Name: Capstone Project II
Credit Hours: 3
Prerequisites: MSAI 7360
Course Description:
Capstone Project II is the continuation of the research and development work initiated in Capstone Project I. In this course, students implement, evaluate, and refine their AI-based solutions using appropriate methodologies, tools, and technologies. The focus is on delivering a working prototype or system, conducting performance analysis, and validating results against project objectives. The course culminates in a formal written report and an oral presentation.
Course Code:
MSAI 7310
Course Name: Project Management for AI systems
Credit Hours: 3
Prerequisites: MSCY 7330 Cybersecurity Concepts
Course Description:
This course covers a comprehensive approach to project management, offering a broad understanding of key processes along with practical skills in using essential project management tools. Mastery of these tools and concepts, including the integration of Artificial Intelligence (AI), can provide a strong competitive edge in the marketplace. AI's role in project management such as automating scheduling, enhancing risk prediction, optimizing resource allocation, and supporting decision-making will also be explored. The learning experience will be enriched through the discussion of case studies, relevant current articles, AI applications, and your own project management experiences. careers in cybersecurity, ethical hacking, and security consulting.
Course Code: MSAI 7320
Course Name: Programming for AI
Credit Hours: 3
Prerequisites: none
Course Description:
This course provides graduate students with the programming skills essential for implementing artificial intelligence (AI) models and solutions. Emphasis is placed on practical experience using Python and popular AI libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Students will develop, test, and evaluate code for data preprocessing, machine learning, and neural network applications. Through hands-on exercises and projects, learners will gain the ability to write efficient, readable, and reusable AI-related code and develop confidence in applying AI programming tools in real-world scenarios.
Course Code:
MSAI 7340
Course Name: Machine Learning
Credit Hours: 3
Prerequisites: MSAI 7310
Course Description:
This course provides a comprehensive overview of Machine Learning (ML) algorithms and practices, covering supervised, unsupervised, and reinforcement learning. Students will learn to preprocess data, train and evaluate ML models, and apply them to real-world problems.
Course Code:
MSAI 7351
Course Name: Big Data Technologies
Credit Hours: 3
Prerequisites: none
Course Description:
This course provides fundamental and advance concepts of big data and its analytics. It includes the lifecycle of a big data and data analytics to address business challenges that leverage big data. The course provides grounding in basic and advanced analytic methods, technology, and tools, including MapReduce and Hadoop, Spark, NoSQL databases, and streaming platforms which will be used to develop a range of big data applications. By the end of this course, student will learn analytical skills to study big data and to provide a solid foundation for developing solutions and applications that need to manipulate big data. The course also Emphasis will be placed on scalable architectures for data-driven AI applications.
Course Code:
MSAI 7341
Course Name: Computer Vision
Credit Hours: 3
Prerequisites: MSAI 7330
Course Description:
This course introduces students to computer vision, including techniques for acquiring, processing, analyzing, and understanding digital images. It integrates machine learning with image understanding and builds foundational skills for practical computer vision applications.
Course Code:
MSAI 7331
Course Name: Natural Language Processing
Credit Hours: 3
Prerequisites: MSAI 7310
Course Description:
This course introduces the fundamental concepts and techniques of Natural Language Processing (NLP), a key domain in Artificial Intelligence. It covers linguistic essentials, syntactic and semantic analysis, machine learning for NLP, and modern deep learning methods for language modeling and text analysis. Students will explore applications such as sentiment analysis, machine translation, and chatbots.
Course Code:
MSAI 7380
Course Name: Bioinformatics
Credit Hours: 3
Prerequisites: MSAI 7330 7330 Artificial Intelligence
Course Description:
This course introduces bioinformatics as an interdisciplinary field that applies computational methods, statistical reasoning, and AI-enabled analytics to biological data. Students will study core bioinformatics concepts (biological databases, sequence analysis, and comparative genomics) and gain hands-on experience building reproducible pipelines for analyzing high-throughput omics data (e.g., genomics and transcriptomics). The course also addresses professional and ethical considerations, including data stewardship, privacy of genomic information, and responsible use of computational models in life-science contexts.
Course Code:
MSAI 7382
Course Name: Selected topics
Credit Hours: 3
Prerequisites: none
Course Description:
This course provides students with the opportunity to explore advanced, emerging, or specialized topics in the field of artificial intelligence that are not covered in the core curriculum. The selected topic may vary each semester based on recent research trends, technological advancements, or industry needs. Students will engage with current literature, case studies, and practical applications to deepen their understanding of the selected area.
Course Code:
MSAI 7371
Course Name: Artificial Intelligence in Smart Grid
Credit Hours: 3
Prerequisites: MSAI 7330
Course Description:
This course explores the integration of artificial intelligence (AI) techniques within smart grid systems to enhance the efficiency, reliability, and sustainability of modern power networks. Students will learn how AI methods—such as machine learning, deep learning, optimization algorithms, and intelligent control—are applied to smart grid components including demand forecasting, load balancing, fault detection, and renewable energy integration. The course covers the architecture of smart grids, data-driven decision-making, and real-time monitoring using intelligent systems.
Course Code:
MSAI 7370
Course Name: Cloud Computing
Credit Hours: 3
Prerequisites: none
Course Description:
This course explores the fundamentals and applications of cloud computing with a focus on its role in enabling large-scale artificial intelligence (AI) systems. Students will learn about cloud service models (IaaS, PaaS, SaaS), deployment models (public, private, hybrid), and key technologies such as virtualization, containerization, and orchestration. The course emphasizes scalable data storage, distributed computing, and cloud-based AI/ML development platforms.
Course Code:
MSAI 7372
Course Name: Cognitive Robotics
Credit Hours 3
Prerequisites: MSAI 7330
Course Description:
Cognitive Robotics explores how robots can perceive, reason, learn, and act in real-world environments using AI. The course covers core cognitive robotics concepts such as perception-to-action pipelines, planning and decision-making under uncertainty, knowledge representation, learning from interaction, human–robot interaction, and responsible deployment. Students will design and evaluate cognitive robotic behaviors through simulations and/or robotic frameworks, and communicate results in professional formats.
