Master of Science in Applied Machine Learning and Artificial Intelligence
MSMA 5003 – Foundations of Python and Machine Learning – 3 credit hours
This course introduces students to Python programming with a focus on foundational skills for AI and
machine learning. Topics include basic syntax, control structures, data types, and introductory data
handling. Students will explore core ML concepts such as supervised vs. unsupervised learning, model
evaluation, and ethical considerations. The course emphasizes conceptual understanding and the
societal impact of AI, including generative AI applications across industries.
MSMA 5013 – Responsible AI: Ethics and Governance – 3 credit hours
This course examines the ethical, societal, and legal implications of artificial intelligence through real-
world case studies and current regulatory frameworks. Students will explore bias detection, fairness
metrics, algorithmic transparency, privacy, and governance models while learning to apply ethical
frameworks to AI development and deployment strategies.
MSMA 5023 – Programming and Algorithmic Thinking for Applied AI – 3 credit hours
This course builds on foundational programming skills to develop algorithmic thinking and problem-
solving strategies essential for scalable AI systems. Students will work with Python libraries such as
NumPy, pandas, Matplotlib, and scikit-learn to implement efficient data processing and model training
workflows. Topics include algorithmic complexity, recursion, sorting/searching, and optimization
techniques, with hands-on projects focused on real-world AI applications.
MSMA 5033 – Human-Computer Interaction – 3 credit hours
Students will study user interface (UI) design principles specifically for AI systems, usability testing, and
interaction patterns including voice interfaces and conversational design. The course explores the full
UI/UX design cycle, accessibility best practices, and emerging technologies in AI-driven interfaces with
explainable AI components.
MSMA 5043 – Applied Data Analytics and Pattern Discovery – 3 credit hours
This course introduces students to practical techniques for discovering patterns and insights from
structured and unstructured data. This project-driven course equips students with foundational skills in
exploratory data analysis, pattern recognition, and business problem-solving using real-world datasets.
Students apply classical and modern analytics techniques, including clustering, association rule mining,
predictive modeling, and anomaly detection, to extract actionable insights.
MSMA 6013 – Machine Learning Applications and Operations – 3 credit hours
This course explores the theory, algorithms, and real-world applications of machine learning, including
supervised, unsupervised, and reinforcement learning. Students gain hands-on experience implementing
algorithms using datasets from healthcare, finance, and technology sectors, evaluating models, and
addressing deployment challenges. In addition to model development, the course introduces core MLOps principles, including model versioning, reproducibility, monitoring, and deployment pipelines. Students will work with tools such as MLflow, Docker, and cloud-based platforms to understand how
machine learning systems are built, deployed, and maintained in production environments. The course
builds a strong foundation for tackling complex AI problems and prepares students for real-world ML
engineering roles.
MSMA 6023 – Natural Language Processing – 3 credit hours
This course introduces methods and techniques for enabling machine learning algorithms to
understand, interpret, and generate human language using modern transformer architectures. Students
will gain understanding of computational linguistics and deep learning to process text and speech,
implementing applications like sentiment analysis, translation, question answering, and text generation.
MSMA 6033 – Deep Learning & Neural Networks – 3 credit hours
This course explores deep learning architectures, including convolutional neural networks (CNNs),
recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Students
will learn to structure, train, and optimize deep learning models while addressing challenges in
deployment, scalability, and model interpretability. Special emphasis is placed on computer vision
applications, including image classification, object detection, and segmentation using CNNs, Vision
Transformers, and diffusion models. Students will also explore how deep learning powers modern visual
AI systems across industries.
MSMA 6043 – Advanced Generative AI – 3 credit hours
Students will explore cutting-edge advancements in generative AI, including diffusion models, large
language models, and multimodal systems, focusing on applications in automation, creativity, and
business innovation. The course covers prompt engineering, fine-tuning strategies, AI integration within
existing systems, and real-world case studies across industries.
MSMA 6090 – Advanced Topics in Machine Learning and AI (Special Topics) – 3 credit hours
This course provides an in-depth exploration of emerging trends, methodologies, and innovations in
machine learning and artificial intelligence. Topics may vary each semester to reflect current
advancements and faculty expertise, such as explainable AI, multimodal deep learning, reinforcement
learning in autonomous systems, generative model optimization, federated learning, AI for
cybersecurity, and edge AI deployment. Through research-based projects, students will evaluate recent
literature, design experimental frameworks, and create innovative AI solutions that address complex,
real-world problems while integrating ethical and societal considerations.
MSMA 6903 – Machine Learning Applied Project – 3 credit hours
A comprehensive project-based capstone course combining theoretical knowledge with practical
implementation. Students will design and apply RAG (Retrieval-Augmented Generation) systems and
LangChain frameworks to optimize large language model (LLM) outputs, enhancing the accuracy and
reliability of generative AI applications. Students will implement supervised and unsupervised learning
models using Python, developing regression, classification, and probabilistic models to analyze and
interpret real-world datasets. Through hands-on projects, students gain practical experience in end-to-
end machine learning solution deployment and model evaluation.