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

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

APPLIED RESEARCH TOPICS IN DEEP LEARNING THEORY & PRACTICAL APPLICATIONS:

This module introduces critical mathematical concepts used in AI and deep learning, focusing on linear algebra and analytic geometry. Upon successful completion of this module, learners will be able to. Upon successful completion of this course, you will be able to:

• Understand linear algebra, matrices and vector spaces.
• Be introduced to linear independence and mappings.
• Understand and review analytic geometry.
• Understand norms, inner products and angles/orthogonality.
• Review orthogonal complement and projections.

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

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: