Course Objectives
This course provides a comprehensive, full-stack journey through modern Artificial Intelligence, covering every component from foundational data science to ethical, autonomous system deployment.
Course Outline
Part I: The Foundations of Intelligent Systems (AI & ML)
Chapter 1: Defining Artificial Intelligence: History, Types, and Ethical Debates
- What is AI? Distinguishing Intelligence from Learning
- The Historical Journey: From Dartmouth to Deep Learning
- Categorizing AI Capabilities
- Philosophical Cornerstones: Intentionality and the Chinese Room
Chapter 2: Machine Learning Fundamentals: Data, Features, and Model Training
- The Role of Data and the Machine Learning Pipeline
- Data Preprocessing: Making Data Machine-Ready
- Feature Engineering: The Art of Data Transformation
- Model Generalization: The Bias-Variance Tradeoff
Chapter 3: The Traditional Machine Learning Toolkit and Learning Paradigms
- Supervised Learning: Predicting with Labeled Data
- Ensemble Learning: Strength in Numbers
- Unsupervised Learning: Structure and Simplification
- Reinforcement Learning (RL): The Agent’s Foundation
Part II: Deep Learning and the Generative AI Revolution
Chapter 4: Neural Networks: The Engine of Complex Pattern Recognition
- The Structure and Function of the Artificial Neural Network
- Scaling to Deep Learning: Automated Feature Extraction
- Training the Network: The Backpropagation Challenge
- Specialized Network Architectures
Chapter 5: Introducing Transformers: The Architecture Powering Modern AI
- The Architectural Leap: From Recurrent to Parallel
- Self-Attention: Focus and Context
- Transfer Learning and Model Adaptation
- Architecture Variants: Encoder versus Decoder
Chapter 6: Generative AI and Large Language Models (LLMs)
- The Creative Engine: Generation at Scale
- Effective Communication: Mastering Prompt Engineering
- The Reliability Challenge: Hallucination and Knowledge Gaps
- Connecting to the World: The Need for External Tools
Part III: The Autonomous Future: AI Agents and System Automation
Chapter 7: Operationalizing Generative AI and Ensuring Reliability
- The Agent’s Hands: Tool Use and Orchestration
- The Agent’s Voice: Runtime and Interfaces
- The Agent’s Quality Control: Validation and Output Refinement
Chapter 8: Understanding AI Agents: Structure, Memory, and Execution
- The Agent Structure: Maintaining Situational Awareness
- Layered Memory Systems for Persistence
- Learning and Recovery: Self-Improvement
Chapter 9: Agent Planning and Orchestration: Executing Complex Tasks
- The Planning Imperative: From Goal to Action
- Advanced Reasoning and Planning Frameworks
- Safety by Design: Governing Autonomous Execution
Chapter 10: The Agentic Enterprise: Collaboration and Long-Term Goals
- System-Wide Automation and Persistence
- Collaboration, Protocols, and Market Structure
- Resilience and Financial Control: The Enterprise Mandate
Chapter 11: Governance, Risk, and the Future of Responsible AI
- Defining Responsible AI: Bias, Fairness, and Alignment
- The Imperative of Transparency and Accountability
- MLOps and Continuous Governance
- The Future Landscape: Regulation and Transformation
Target Audience
This course is designed for a broad, professional audience seeking deep, practical expertise in the full lifecycle of AI systems:
- Aspiring ML/AI Engineers and Data Scientists: Individuals looking to build job-ready skills that span traditional ML through cutting-edge Generative AI and production MLOps.
- Technology Leaders and Business Strategists: Executives and managers who need to understand the architecture, risks, and deployment strategy of autonomous systems and Generative AI for enterprise automation.
- Developers and Researchers: Professionals looking to master the Transformer architecture, advanced prompting techniques, and responsible AI governance (AI Ethics and Compliance).
Prerequisites
A strong foundation is essential for maximizing learning from this advanced curriculum. Recommended prerequisites include:
- Programming: Foundational knowledge of Python programming.
- Mathematics: Familiarity with basic college-level Linear Algebra, Probability, and Statistics.
- Concepts: A general understanding of data structures and introductory computer science concepts.
Recommended Readings
To complement the course modules, these texts are recommended for deeper study:
| Topic | Book Title | Author(s) |
| Foundational Theory | Artificial Intelligence: A Modern Approach | Stuart Russell & Peter Norvig |
| Deep Learning Core | Deep Learning | Ian Goodfellow, Yoshua Bengio, & Aaron Courville |
| Machine Learning Practice | Hands-On Machine Learning with Scikit-Learn and TensorFlow | Aurélien Géron |
| Reinforcement Learning | Reinforcement Learning: An Introduction | Richard S. Sutton & Andrew G. Barto |
| Generative AI & LLMs | The LLM Engineering Handbook | Paul Iusztin & Maxime Labonne |
| Ethics and Safety | The Alignment Problem: Machine Learning and Human Values | Brian Christian |
Conclusion
The “Fundamentals of Generative AI” course delivers the comprehensive knowledge necessary to navigate and lead the current AI ecosystem. By covering the evolution from basic ML algorithms to the deployment of autonomous, governed agents, this curriculum ensures graduates possess the topical depth, technical acumen, and ethical awareness required to build and deploy trustworthy, enterprise-scale AI solutions.
