This course is taught using the textbook Artificial Intellgience: A Modern Approach (4th edition) by S. Russell and P. Norvig, with exercises drawn from its accompanying AIMA website at Berkeley.   Below are my lecture notes primarily based on the textbook and the AIMA website, with supplementary materials from other resources.  

1. AI agents 
   • Introduction to AI (pptx, pdf)
   • Intelligent agents (pptx, pdf)
2. Search
   • Problem solving (pptx, pdf)
   • Uninformed and informed search (pptx, pdf)
   • Heuristic functions (pptx, pdf)
   • Local search (pptx, pdf)
   • Search continuity and nondeterminism (pptx, pdf)
   • Partial percepts (pptx, pdf)
3. Games 
   • Introduction to games (pptx, pdf)
   • Alpha-beta pruning (pptx, pdf)
   • Monte Carlo tree search (pptx, pdf)
4. Constraint satisfaction
   • Introduction to constraint satisfaction problems (CSPs) (pptx, pdf)
   • Solution of CSPs (pptx, pdf)
   • Backtracking & CSP structure (pptx, pdf)
5. Knowledge and reasoning
   • Logical agents and syntax of propositional logic (PL) (pptx, pdf)
   • PL semantics and inference rules (pptx, pdf)
   • Resolution in PL (pptx, pdf)
   • Propositional model checking (pptx, pdf)
   • First order logic (FOL): syntax and semantics (pptx, pdf)
   • Representation and inference in FOL (pptx, pdf)
   • Forward and backward chaining (pptx, pdf)
   • Resolution in FOL (pptx, pdf)
6. Reasoning about uncertainties
   • Basics of probability (pptx, pdf)
   • Probabilistic inference (pptx, pdf)
   • Naive Bayes models (pptx, pdf)
   • Bayes networks (pptx, pdf)
   • Exact inference (pptx, pdf)
   • Approximate inference (pptx, pdf)
   • Markov chain simulation (pptx, pdf)
7. Machine learning
   • Supervised learning (pptx, pdf)
   • Decision trees and model selection (pptx, pdf)