What You Will Learn
Artificial Intelligence is rapidly reshaping standard organizational systems and workflows across every major sector. To remain competitive, modern professionals must move past marketing buzzwords and establish a grounded, structural understanding of how these powerful tools function under the hood. This comprehensive, intensive workshop strips away the fluff to explore the true mechanics of modern AI systems. Participants will dive deep into the foundations of machine learning and neural networks, investigate large language model (LLM) architectures, and unpack the emerging ecosystem of autonomous software agents. Bridging technical execution with corporate governance, the session also addresses critical framework tools like the Model Context Protocol (MCP) alongside essential ethical guardrails.
Course Objectives
- Differentiate between fundamental types of AI architectures, including narrow, general, reactive, and agentic variations.
- Explain the mechanics behind machine learning and deep learning, including foundational model training and structural evaluations.
- Understand how large language models (LLMs) are constructed and safely manipulated via strategic prompt design.
- Describe the function of the Model Context Protocol (MCP) and its critical role in multi-tool enterprise orchestration.
- Grasp the core operational traits, enterprise use cases, and deployment risks associated with agentic AI pipelines.
- Identify and mitigate key ethical challenges within live systems, focusing heavily on bias, transparency, and structural accountability.
Methodology
This program will be conducted via highly interactive conceptual lectures, real-world industry case studies, targeted structural deep dives, and an operational collaborative proposal synthesis activity.
Course Contents
- Module 1: Types of AI (Foundations)
- Module 2: Machine Learning & Deep Learning
- Module 3: Large Language Models (LLMs) & Prompting
- Module 4: Model Context Protocol (MCP)
- Module 5: Agentic AI (Autonomous Agents)
- Module 6: AI Ethics (Governance & Responsibility)
- Module 7: Synthesis & Discussion