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Principles of Artificial Intelligence (Com S 472/572)

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)

    • Intelligent agents (pptx)

  2. Search

    • Problem solving (pptx)

    • Uninformed search (pptx)

    • Informed search (pptx)

    • Properties of heuristics (pptx)

    • Heuristic functions (pptx)

    • Local search (pptx)

    • Search continuity and non-determinism (pptx)

    • Partial percepts (pptx)

  3. Games 

    • Introduction to games (pptx)

    • Alpha-beta pruning (pptx)

    • Heuristic alpha-beta search (pptx)

    • Monte Carlo tree search (pptx)

    • Stochastic games (pptx)

  4. Constraint satisfaction

    • Introduction to constraint satisfaction problems (CSPs) (pptx)

    • Solution of CSPs (pptx)

    • Backtracking & CSP structure (pptx)

  5. Knowledge representation & reasoning

    • Knowledge-based agents (pptx)

    • Propositional Logic (PL) (pptx)

    • Resolution in PL (pptx)

    • Propositional theorem proving (pptx)

    • PL agents (pptx)

    • First-order logic (FOL) (pptx)

    • FOL models & usage (pptx)

    • Knowledge engineering (pptx)

    • Unification & chaining (pptx)

    • Resolution in FOL (pptx)

  6. Quantifying uncertainties

    • Basics of probability (pptx)

    • Probalistic inference (pptx)

    • Bayes' rule (pptx

    • Bayesian models (pptx)

    • Bayesian networks (pptx)

    • Conditional independence (pptx

  7. Probabilistic reasoning

    • Exact inference in BNs (pptx)

    • Approximate inference in BNs (pptx

    • Importance sampling (pptx)

    • Gibbs sampling (pptx)

    • Markov chains & Metropolis-Hastings sampling (pptx)

    • Temporal models (pptx)

    • Filtering & smoothing (pptx

    • Hidden Markov models (pptx)

  8. Machine learning

    • Supervised learning (pptx)

    • Decision trees (pptx)

    • Model selection (pptx)

    • Linear regression (pptx