Techniques in Artificial Intelligence (SMA 5504)

Lecture Notes

Notes from lectures 6 and 21 are not available.

Lecture 1: What is Artificial Intelligence (AI)? (PDF)

Lecture 2: Problem Solving and Search (PDF)

Lecture 3: Logic (PDF)

Lecture 4.: Satisfiability and Validity (PDF - 1.2 MB)

Lecture 5.: First-Order Logic (PDF)

Lecture 7.: Resolution Theorem Proving: Propositional Logic (PDF)

Lecture 8.: Resolution Theorem Proving: First Order Logic (PDF)

Lecture 9: Logic Miscellanea (PDF)

Lecture 10: Planning (PDF)

Lecture 11: Partial-Order Planning Algorithms (PDF)

Lecture 12: Graph Plan (PDF)

Lecture 13: Planning Miscellany (PDF)

Lecture 14: Probability (PDF)

Lecture 15: Bayesian Networks (PDF)

Lecture 16: Inference in Bayesian Networks (PDF)

Lecture 17: Where do Bayesian Networks Come From? (PDF)

Lecture 18: Learning With Hidden Variables (PDF)

Lecture 19: Decision Making under Uncertainty (PDF)

Lecture 20: Markov Decision Processes (PDF)

Lecture 22: Reinforcement Learning (PDF)

Assignments

In addition to the assignments, this section has links to excerpts of code, documentation, and downloads used to complete the assignments.

Exams

This section contains exams from previous offerings of the course, as well as practice exams, both of which are provided to students as study aids.