Material for "Introduction to Artificial Intelligence" Classes

Slides, Handouts, Code, Problems and Sample Solutions

- Lecture 01: Intelligent Agents (Problems, Solutions)
- Lecture 02: Propositional Logic (Problems,, Solutions)
- Lecture 03: First Order Logic (Handout, Problems), Solutions)
- Lecture 04: Rule-Based (Expert) Systems (Problems, Solutions)
- Lecture 05: Ontologies and Semantic Networks (Problems, Solutions)
- Lecture 06: Decision Tree Learning (Problems, Solutions)
- Lecture 07: Perceptron Learning (Handout, C Code)
- Lecture 08: (Artificial) Neural Network Learning (Handout, Problems, Solutions)
- Lecture 09: Function Optimization with Local Search (Problems, Solutions)
- Lecture 10: Probabilities (Problems, Solutions)
- Lecture 11: Bayesian Networks (= Belief Networks) (Problems, Solutions)
- Lecture 12: Naive Bayesian Learning (Problems, Solutions)
- Lecture 13: Planning Agents
- Lecture 14: STRIPS (Handout, Problems, Solutions)
- Lecture 15: SAT-Based Planning (Problems, Solutions)
- Lecture 16: Uninfomed Search (Problems, Solutions)
- Lecture 17: Constraint Satisfaction (Problems, Solutions)
- Lecture 18: Heuristic Search (Problems, Solutions)
- Lecture 19: Search-Based Planning (Problems, Solutions)
- Lecture 20: Decision Theory (Problems, Solutions)
- Lecture 21: Markov Decision Processes (C Code, C Code, Problems, Solutions)
- Lecture 22: Adversarial Search (Problems, Solutions)
- Lecture 23: Artificial Intelligence Ethics

Note: For the slides, click on "Lecture **" on the left. A few of the about 100 practice problems given above come from previous exams, homeworks and similar sources from other universities, including from Vincent Conitzer (Duke), Andrew Moore (CMU), Peter Norvig (Google) and Stuart Russell (UC Berkeley). In a few cases, the originators are no longer known but we appreciate any leads to be able to list them here.

Projects