Material for "Introduction to Artificial Intelligence" Classes

Slides (and Handouts)

• Lecture 01: Intelligent Agents
• Lecture 02: Propositional Logic
• Lecture 03: First Order Logic (Handout)
• Lecture 04: Rule-Based (Expert) Systems
• Lecture 05: Ontologies and Semantic Networks
• Lecture 06: Decision Tree Learning
• Lecture 07: Perceptron Learning (Handout, C Code for Averaging with Gradient Descent)
• Lecture 08: (Artificial) Neural Network Learning (Handout)
• Lecture 09: Function Optimization with Local Search
• Lecture 10: Probabilities
• Lecture 11: Bayesian Networks (= Belief Networks)
• Lecture 12: Naive Bayesian Learning
• Lecture 13: Planning Agents
• Lecture 14: STRIPS (Handout)
• Lecture 15: SAT-Based Planning
• Lecture 16: Uninfomed Search
• Lecture 17: Constraint Satisfaction
• Lecture 18: Heuristic Search
• Lecture 19: Search-Based Planning
• Lecture 20: Decision Theory
• Lecture 21: Markov Decision Processes (C Code for Value Iteration without Discounting and with Discounting)
• Lecture 22: Adversarial Search

Assignments (and Sample Solutions)

Note: A few of the assignment questions 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.

• Assignment 01: Intelligent Agents, Propositional Logic (Sample Solutions)
• Assignment 02: Propositional Logic, First Order Logic (Sample Solutions)
• Assignment 03: First Order Logic, Rule-Based (Expert) Systems (Sample Solutions)
• Assignment 04: Ontologies and Semantic Networks, Decision Tree Learning (Sample Solutions)
• Assignment 05: Perceptron and (Artificial) Neural Network Learning (Sample Solutions)
• Assignment 06: Function Optimization with Local Search (Sample Solutions)
• Assignment 07: Function Optimization with Local Search, Probabilities (Sample Solutions)
• Assignment 08: Bayesian Networks, Naive Bayesian Learning (Sample Solutions)
• Assignment 09: STRIPS (Sample Solutions)
• Assignment 10: SAT-Based Planning, Uninformed Search (Sample Solutions)
• Assignment 11: Uninformed Search, Constraint Satisfaction (Sample Solutions)
• Assignment 12: Constraint Satisfaction, Heuristic Search (Sample Solutions)
• Assignment 13: Heuristic Search, Search-Based Planning, Decision Theory, Markov Decision Processes (Sample Solutions)
• Assignment 14: Markov Decision Processes, Adversarial Search (Sample Solutions)

Projects

• Computer Games in the Classroom

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