Introduction to Artificial Intelligence

Preface 

Foundations of Artificial Intelligence 

• Understanding Artificial Intelligence (AI) 
• History and Evolution of Artificial Intelligence 
• Types and Approaches to Artificial Intelligence 
• Computational Foundations of AI 
• AI Applications, Careers, and Professional Skills 
• Applied Activity: Identifying an AI Opportunity 

Intelligent Agents and AI Problem Solving 

• Foundations of Intelligent Agents 
• Designing Agents with the PEAS Framework 
• AI Environment Characteristics 
• Problem Formulation and State-Space Representation 
• Agent Architectures and Behaviors 
• Applied Activity: Designing an Intelligent Agent 

Knowledge Representation, Reasoning, and Uncertainty 

• Representing Knowledge in AI Systems 
• Logic-Based Reasoning 
• Expert Systems and Rule-Based AI 
• Reasoning Under Uncertainty 
• Sequential and Approximate Reasoning 
• Applied Activity: Building a Knowledge-Based Decision System 

Search, Optimization, Planning, and Decision Making 

• Foundations of AI Search 
• Uninformed Search Strategies 
• Informed and Local Search 
• Optimization and Constraint Satisfaction 
• Planning, Games, and Sequential Decisions 
• Applied Activity: Comparing Search and Optimization Methods 

Data and Machine Learning Foundations 

• Data, Mathematics, and Statistics for Machine Learning 
• Machine Learning Concepts and Paradigms 
• The Machine Learning Workflow 
• Data Preparation and Feature Engineering 
• Foundational Machine Learning Algorithms 
• Model Evaluation and Improvement 
• Applied Activity: Building a Foundational Machine Learning Model 

Neural Networks, Deep Learning, and Reinforcement Learning 

• Neural Network Fundamentals 
• Training Deep Learning Models 
• Deep Learning Architectures 
• Transfer Learning and Generative Deep Learning 
• Reinforcement Learning 
• Applied Activity: Training and Evaluating a Learning Agent 

Computer Vision, Speech, Robotics, and Multimodal AI 

• Foundations of Machine Perception 
• Computer Vision 
• Speech and Audio AI 
• Robotics and Autonomous Systems 
• Multimodal and Edge AI 
• Applied Activity: Building a Perception Prototype 

Natural Language Processing and Generative AI 

• Natural Language Processing Foundations 
• Transformers, Foundation Models, and Large Language Models 
• Generative AI Models and Capabilities 
• Prompt Engineering 
• Retrieval-Augmented Generation and AI Agents 
• Evaluating Generative AI Solutions 
• Applied Activity: Designing a Workplace AI Assistant 

Responsible, Ethical, Secure, and Governed AI 

• Responsible AI Principles and Social Impact 
• Bias, Fairness, and Inclusion 
• Transparency, Explainability, and Accountability 
• AI Privacy, Security, and Safety 
• AI Governance, Regulation, and Standards 
• Responsible Use of Generative AI 
• Applied Activity: Conducting an AI Ethics and Risk Assessment 

AI Engineering Lifecycle, Tools, and MLOps 

• Identifying and Defining AI Opportunities 
• The AI Development Lifecycle 
• AI Programming Tools and Platforms 
• MLOps and AI Operations 
• Monitoring, Testing, and Maintenance 
• Project Management and Documentation 
• Applied Activity: Designing an End-to-End AI Engineering Workflow 

AI Applications Across Industries and the Workforce 

• AI in Healthcare and Life Sciences 
• AI in Business, Finance, and Retail 
• AI in Manufacturing, Supply Chain, and Robotics 
• AI in Transportation and Smart Mobility 
• AI in Education and Research 
• AI in Cybersecurity and Public Services 
• AI in Media and Creative Industries 
• AI in Workplace Productivity and Automation 
• Applied Activity: Solving an Industry Problem with AI 

Future Horizons and Capstone Project 

• Emerging AI Trends 
• Human-AI Collaboration and the Future of Work 
• Academic, Career, and Portfolio Pathways 
• Capstone Planning and Requirements 
• Capstone Design and Prototype Development 
• Capstone Evaluation, Presentation, and Reflection