1. 01 -1 Lecture 01 Artificial Intelligence Topics –Introduction –Knowledge representation –Knowledge reasoning –Machine learning –Applications

2. 01 -2 Introduction Artificial Intelligence –A discipline which studies how to make a system smarter –Solving problems by reasoning vs. computation –Sustaining service by self-evolution vs. retrofit

3. 01 -3 Introduction Knowledge is power –Knowledge representation and reasoning Learning is powerhouse –Machine learning

4. 01 -4 Knowledge Representation Ontological Engineering –Ontology Basic concepts about a domain Basic logics about a domain –Ontology vs. Knowledge base –Ontological inference Reasoning about basic knowledge of a domain –Ontology Verification & Validation Correctness Completeness

5. 01 -5 Knowledge Reasoning Heuristic search –Heuristics First-order predicate logic –Formal Resolution Rule-based Reasoning –Modus ponens Case-based Reasoning –Cases vs. past experience –Case adaptation

6. 01 -6 Knowledge Reasoning Fuzzy reasoning –Fuzzy sets –Fuzzy logic –Fuzzy knowledge-based systems

7. 01 -7 Machine Learning Classification-based Learning –Classification: Supervised learning –Decision tree –Multi-layer perceptrons –Learning vector quantization –Support vector machine Clustering-based Learning –Clustering: Unsupervised learning –K-Means –Self-organizing feature maps –Adaptive resonant theory

8. 01 -8 Machine Learning Association rules Mining –Associations –Mining frequent patterns –Mining frequent sequential patterns Reinforcement Learning –Rewards –Credit assignment

9. 01 -9 Applications Intelligent Agents –A computing entity (real or virtual) that performs user delegated tasks autonomously Agency –legal to do things Delegation-based computation Intelligence –able to do things Reasoning Learning