Artificial Intelligence - Course Details
Explore the foundations of Artificial Intelligence. Learn about intelligent agents, search algorithms, knowledge representation, and reasoning. Understand how AI systems make decisions.
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What you'll learn?
- AI fundamentals and history
- Search and optimization algorithms
- Knowledge representation
- Machine learning basics
- AI ethics and future trends
Requirements
- Basic programming knowledge
- Understanding of mathematics
- Understand fundamental AI concepts and applications
- History and evolution of AI
- Difference between AI, Machine Learning (ML), and Deep Learning (DL)
- Types of AI: Narrow, General, and Super intelligent AI
- Applications of AI in healthcare, education, and industry
- Python fundamentals for AI development
- Types of agents and environments
- PEAS (Performance measure, Environment, Actuators, Sensors) model
- State space representation
- Uninformed search: Breadth-First Search (BFS), Depth-First Search (DFS), Depth-Limited Search, Iterative Deepening Search
- Informed (heuristic) search: Greedy Best-First Search, A* Algorithm, Hill Climbing
- Local search and optimization: Genetic Algorithms, Constraint Satisfaction Problems (CSP)
- Advanced search & planning: Adversarial Search (Minimax Algorithm), Alpha-Beta Pruning, Real-Time Search, Game Trees and Decision Trees
- Propositional logic
- First-order logic
- Supervised, Unsupervised, and Reinforcement Learning
- Regression, Classification, Clustering algorithms
- Introduction to neural networks and deep learning
- Fundamentals of reinforcement learning
- Bias in AI
- Data privacy and security
- Responsible AI practices
- Develop rule-based systems and chatbots
- Implement small AI projects
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