Schedule

Date Topic Notes Reading Assignment out/due
1/11/2017 Introduction, Intelligent Agents Course Introduction, What is AI?, A scattered history of AI, AI today, AI Agents, In addition to the Python/autograder tutorial linked to the right, feel free to have a look at the official Python Tutorial. Ch 2 Python/autograder Tutorial (optional, but recommended). This introduces you to Python and the autograder.
1/18/2017 Uninformed search Classical search Ch 3, Ch 4.1 -- 4.3 PA 1 out
1/25/2017 Informed search Heuristic search Ch 3, Ch 4.1 -- 4.3
2/1/2017 Adversarial search Adversarial search Ch 5.1 -- 5.4, Ch 6 PA 1 due; PA 2 out
2/8/2017 Constraint Satisfaction Constraint Satisfaction Ch 6
2/15/2017 Markov Decision Processes Markov Decision Processes SB 3.1--3.3, 3.5--3.6, SB 4.1--4.4 PA 2 due
2/22/2017 Markov Decision Processes, Reinforcement Learning Markov Decision Processes, Reinforcement Learning SB 6.1 SB 6.2 SB 6.3 SB 6.5 PA 3 out
3/1/2017 Reinforcement Learning Reinforcement Learning SB 6.1 SB 6.2 SB 6.3 SB 6.5 Project out. Proposal due on 3/14/2017. Project due on 4/19/2017.
3/15/2017 Probability, Hidden Markov Models Probability, Hidden Markov Models Ch 13, 14 PA 3 due, Project proposal due
3/22/2017 Hidden Markov Models Hidden Markov Models Ch 13, 14 PA 4 out
3/29/2017 MIDTERM EXAM Cumulative up to and including HMMs.
4/5/2017 Bayes Networks Bayes Networks
4/6/2017 PA 4 due
4/19/2017 Intro to Machine Learning Naive Bayes, Regression, Neural Networks Intro Project due.
4/28/2017 Extra Credit Assignment due.


Important note: unless noted otherwise, all readings and assignments are due on the day that they appear in the schedule.

Unless noted otherwise, all readings are from Artificial Intelligence: A Modern Approach, 3rd Ed., Russell and Norvig.