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.
Classes will be held every Tuesday and Thursday from 1:00 to 2:20 PM in Smith-Buonanno Hall & 168.
- Agents and Agenthood
- Mini-Max for Game Playing
- Knowledge Representation and Reasoning
- Propositional Logic
- First-Order Logic
- Reasoning and Logical Inference
- Uncertain Knowledge
- Bayes' Rule
- Probabilistic Reasoning
- Bayes Nets
- Task Planning
- Robot Motion Planning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Philosophy of AI
We take academic honesty very seriously. You may discuss any ideas and concepts with your classmates. You MAY NOT hand in ANYTHING - code, graphs, or writing - that is not your own original work. Write your own code, generate your own graphs, prove your own theorems, write your own reports. NO EXCEPTIONS. You should check with Professor Konidaris if you have any confusion about what is permitted.
I expect all Brown students to conduct themselves with the highest integrity, according to the Brown Academic Code.
Students will have three late days per semester, to use at their discretion. Late assignments (not covered by a late day, or an acceptable reason (illness, etc)) will be penalized 10% per day. Assignments will not be accepted more than 5 days after the due date.
CS16, CS18, or CS19, and one of CS22 or CS45.
Graded components will be the assignments (60%), the midterm (20%), and the final exam (20%).