Introduction to AI
1001055 modules · 67 topics · 67 with study notes
Module 1Introduction to Artificial Intelligence (8hrs)19 topics
- What is AI?Notes
- History and evolution of AINotes
- Foundations of AI: philosophyNotes
- mathematicsNotes
- cognitive-science influencesNotes
- Intelligent agents: agent-environment interactionNotes
- PEAS (Performance measure, Environment, Actuators, and Sensors) descriptionNotes
- Types of environments (observable)Notes
- deterministicNotes
- episodicNotes
- staticNotes
- discreteNotes
- single/multi-agent)Notes
- Types of agents: simple reflexNotes
- model-basedNotes
- goal-basedNotes
- utility-basedNotes
- learning agentsNotes
- Overview of AI application domains across engineering disciplinesNotes
Module 2Problem Solving by Search (9hrs)15 topics
- Problem formulation and state-space representationNotes
- Uninformed search: BFSNotes
- DFSNotes
- Uniform Cost SearchNotes
- Depth-LimitedNotes
- Iterative Deepening SearchNotes
- Informed (heuristic) search: Greedy Best-First SearchNotes
- A* algorithmNotes
- Heuristic function design and admissibility/consistencyNotes
- Local search algorithms: Hill ClimbingNotes
- Simulated Annealing (concept)Notes
- Adversarial search: Minimax algorithmNotes
- Alpha-Beta pruningNotes
- Constraint Satisfaction Problems (CSP): conceptNotes
- backtracking (overview)Notes
Module 3Knowledge Representation and Reasoning (8hrs)11 topics
- Knowledge representation issuesNotes
- propositional logic — syntax and semanticsNotes
- Inference in propositional logic: resolutionNotes
- First-order predicate logic — syntaxNotes
- quantifiers (concept)Notes
- Inference in first-order logic: forward chainingNotes
- backward chainingNotes
- resolution (concept)Notes
- Structured knowledge representation: semantic networksNotes
- framesNotes
- Rule-based (production) systemsNotes
Module 4Reasoning Under Uncertainty (7hrs)6 topics
Module 5Introduction to Machine Learning and AI Applications (10hrs)16 topics
- Machine learning paradigms: supervisedNotes
- unsupervisedNotes
- reinforcement learning (concept)Notes
- Overview of regression and classification concepts (linear regression, k-NN, decision trees)Notes
- Overview of clustering concept (k-means)Notes
- Introduction to artificial neural networks: perceptronNotes
- basic architecture (concept)Notes
- Overview of Natural Language Processing (NLP): concept and applicationsNotes
- Overview of Computer Vision: concept and applicationsNotes
- Expert systems: architecture and applicationsNotes
- Robotics and AI: overview of intelligent robotic systemsNotes
- EthicsNotes
- biasNotes
- responsible AINotes
- societal impactNotes
- AI and Sustainable Development Goals (SDGs)Notes
Theory