IT 721 : APPLIED RESEARCH TOPICS IN DEEP LEARNING THEORY & PRACTICAL APPLICATIONS: 

In this course, you will master CNNs, RNNs, LSTMs, autoencoders, and state-of the-art generative models like GPT, PaLM, CLIP, and DALL·E and gain the industry-critical skills of transfer learning, prompt engineering, and RAG & LoRA fine-tuning to create domain-specific AI systems ready for real-world impact.Database storage technologies have transformed into complex systems that support knowledge management and decision support systems. This course takes a look at the foundations of database storage technologies. Students will learn about database storage architecture, types of database storage systems (legacy, current and emerging), physical data storage, transaction management, database storage APIs, data warehousing, governance and big data systems. The student will tie this all together to see how database storage technologies apply to data analytics. 

• Introduction to Deep Learning
• Neural Networks & Backpropagation
• CNN, RNN, LSTM
• Autoencoders & Generative Models
• Transfer Learning
• Prompt engineering basics
• Foundation Models GPT, PaLM, CLIP, DALL·E
• RAG, LoRA

BTC 771 : AI STRATEGY FOR LEADERS: 

The course integrates real-world case studies from industry leaders such as Tesla, Amazon, JPMorgan Chase, and Microsoft, providing students with insights into AI successes and challenges. Through case study analyses, discussions, and practical assignments, students will develop leadership strategies for AI integration, ensuring responsible and effective AI adoption in their organizations. 

• AI Fundamentals and Business Applications
• AI Vision and Strategy Development
• Ethical AI Leadership
• Building AI-Ready Teams
• AI Tools and Technologies
• Data Governance and Compliance
• Measuring AI Impact and Risks
• The Future of AI Leadership

QM 625 : Mathematics of Artificial Intelligence & Machine Learning 

This course introduces and explains the fundamental mathematical concepts that form the backbone of artificial intelligence and deep learning. It emphasizes a strong understanding of linear algebra and analytic geometry, which are essential for building and optimizing AI models. Learners will explore how these mathematical principles directly apply to modern algorithms and neural networks. By the end of the course, participants will gain the analytical skills needed to interpret and implement AI techniques effectively. 

Course Learning Outcomes: 

Upon successful completion of this course, students will be able to: 

• Analyze the role of scalars, vector spaces, tensors, matrices, derivatives, and gradient descent in AI and ML.
• Apply fundamental linear algebra and its mathematics to solve problems in AI and ML.
• Evaluate and select the most effective optimization methods and their mathematics for specific AI and ML problems.
• Differentiate between the roles of probability in predicting future events and statistics in analyzing past events for their applications in AI and ML.

QM 625 : CAPSTONE PROJECT:

The Capstone Project provides the opportunity for integrating program learning within a project framework. Each student identifies or defines a professionally relevant need to be addressed that represents an opportunity to assimilate, integrate or extend learning derived through the program. The student will work with the Capstone Project Mentor to develop a proposal. After review and approval by the Capstone Project Mentor, the student will be authorized to complete the project. The student will present the completed project at the end of the semester. Upon successful completion of this course, you will be able to: 

• Demonstrate the knowledge gained from the previous courses in the program.
• Write a formal research paper or conduct a detailed project.
• Apply the objectives of research to a practical information technology problem.
• Create a project plan to successfully present a solution/goal to the stated problem.
• Use research tools for an applied research paper or project.
• Evaluate the validity and reliability of statistics and other forms of research.

IT 720 : APPLIED RESEARCH IN NATURAL LANGUAGE PROCESSING: (Only for Doctorate learners) 

This course is designed to provide students with advanced knowledge and practical skills in natural language processing (NLP) research and applications. Students will delve into cutting-edge techniques, methodologies, and tools used in NLP, with a focus on applied research and real-world use cases. Through a combination of lectures, hands-on projects, and literature review assignments, students will explore topics such as text classification, sentiment analysis, named entity recognition, machine translation, question answering, and more. Emphasis will be placed on understanding the underlying algorithms, evaluating model performance, and conducting empirical studies to address real-world NLP challenges. 

Upon successful completion of this course, students will be able to: 

• Evaluate and apply advanced natural language processing models and architectures to solve domain-specific language tasks.
• Design, implement, and optimize NLP models using state-of-the-art algorithms, evaluation metrics, and experimental design principles.
• Critically analyze the ethical, societal, and fairness implications of NLP technologies, and propose responsible solutions.
• Develop NLP systems that extract, generate, and interpret structured and unstructured data for diverse real-world applications.
• Conduct applied NLP research by formulating hypotheses, implementing models, and interpreting results to advance the field.

RES 712 : QUALITATIVE AND EXPLORATORY RESEARCH METHODS 

This course explores non-statistical forecasting and other qualitative research methods. Qualitative research methodologies have become more prevalent in research as a viable and valid form of inquiry, especially as they pertain to human behavior in organizations. Qualitative research techniques examined include ethno methodology; grounded theory; and phenomenological research. Nonparametric statistical analysis will also be examined. By the conclusion of this class, you will gain a solid foundation regarding the qualitative research approach and its various traditions along with their theoretical and applied constructs. This will allow you to prepare a qualitative problem for research as well as structure a valid qualitative research design for conducting the actual research itself (i.e., your doctoral dissertation or future research problems in your area of interest or specialization).

RES 713 : QUANTITATIVE RESEARCH METHODS I DATA MANAGEMENT AND NON-EXPERIMENTAL: 

This course is a combination of quantitative research methods, multivariate statistics, and forecasting. The course assumes the doctoral student has had a graduate-level statistics/quantitative methods course covering parametric statistics and hypothesis testing. 

DOCTORAL RESIDENCY I: This course is the first of three residencies. The residencies occur simultaneously with coursework throughout the student's doctoral journey. The intent of a residency experience is to provide students with a chance to connect directly with faculty/mentors and fellow students within the doctoral program. Students will attend information sessions, meet with faculty/mentors regarding subject matter and research methodology experts, and present their problem/purpose statement to a review board for feedback and direction. 

• Outcome: Finalisation of project problem and purpose statements.