Course Curriculum

Module 1: Introduction to AI

• What is Artificial Intelligence?
• History and evolution of AI
• Applications of AI in different industries
• AI vs Human intelligence

Module 2: Problem Solving and Search

• Problem-solving techniques
• Search algorithms: BFS, DFS, A*
• Game playing and decision making

Module 3: Knowledge Representation & Reasoning

• Representing knowledge in AI systems
• Logical reasoning and inference
• Expert systems

Module 4: Machine Learning Basics

• Introduction to machine learning
• Supervised vs unsupervised learning
• Classification, regression, clustering
• Simple ML implementation using Python

Module 5: Neural Networks and AI Concepts

• Introduction to neural networks
• Basics of deep learning
• Applications of neural networks in AI

Module 6: AI in Real-World Applications

• AI in healthcare, finance, robotics, and autonomous vehicles
• Chatbots and virtual assistants
• Computer vision and natural language processing (NLP)

Module 7: Ethics and Future of AI

• AI ethics, fairness, and bias
• Social and economic impact of AI
• Future trends and emerging technologies

Module 8: Final Project

• Build a simple AI-based application (e.g., chatbot, recommendation system, or classification model)
• Implement machine learning and problem-solving techniques learned in the course

Skills Covered

• Fundamentals of AI and machine learning
• Problem-solving and search algorithms
• Knowledge representation and reasoning
• Neural networks basics
• Python for AI projects
• AI ethics and applications