Overview

Algorithms and representations used in artificial intelligence. Introduction and implementation of algorithms for search, planning, perception, knowledge representation, logic, probabilistic representation and reasoning, robotics and machine learning.

Some of the course will be based on material from Ron Parr's Spring 2014 Intro to AI course at Duke.

Meeting Times

Classes will be held every Tuesday and Thursday from 1:00 to 2:20 PM in Salomon 001.

Syllabus

Agents and Agenthood
Search

Uninformed
Informed
Game Theory and Adversarial Search

Knowledge Representation and Reasoning

Logical Representations: Reasoning and Inference
Uncertain Knowledge

Bayes' Rule
Probabilistic Reasoning
Bayes Nets
Hidden Markov Models

Planning

Classical Planning
Robot Motion Planning
Planning Under Uncertainty: Markov Decision Processes

Learning

Reinforcement Learning
Supervised Learning
Unsupervised Learning

Advanced Topics

Natural Language Processing
Machine Vision
Robot Learning
Algorithmic Game Theory

Philosophy of AI
Social and Ethical Issues

Pre-requisites

CS16, CS18, or CS19, and one of CS22, CS145, APMA1650, or APMA1655.