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.