CS 4700: Foundations of Artificial Intelligence

Fall 2019

Course Information

Course Schedule

Date Topic/Slides Readings
9/4 Introduction to AI and the course Skim Chapters 1 and 2
9/9 Search Sections 3.1-3.4
9/11 Uninformed Search Sections 3.4-3.5
9/16 Informed Search Sections1 3.3.1, 3.4.4, 3.5.1-3.5.3
9/23 Informed Search Sections 3.5.1-3.5.3, 3.6.1, 4.1
9/25 Adversarial Search Sections 5.1, 5.2.1, 5.2.3
10/2 Adversarial Search, Markov Decision Processes Sections 5.2.3, 16.1
10/7 Markov Decision Processes Sections 16.1, 16.2.2
10/16 Markov Decision Processes Sections 16.1, 16.2.2
10/21 Markov Decision Processes, Reinforcement Learning Sections 16.2.2, 21.1, 21.3.1-21.3.2
10/23 Reinforcement Learning, Multi-Armed Bandits Sections 21.3.2, 16.3
10/28 Multi-Armed Bandits, Monte Carlo Tree Search Sections 16.3, 5.4
10/30 Introduction to Machine Learning, Supervised Learning Sections 18.2, 18.6.4
11/4 Neural Networks Section 18.6.4
11/6 Neural Networks and Linear Regression Section 18.6
11/11 Neural Networks, Naive Bayes Sections 18.6, 12.6
11/13 Naive Bayes Sections 12.6, 20.2.2, 22.1.1
Additional resource: Wikipedia's article on Naive Bayes
11/18 Naive Bayes, Propositional Logic Sections 12.6, 20.2.2, 22.1.1, 7.1-7.4
Additional resource: Wikipedia article on Additive Smoothing
11/20 Propositional Logic Sections 7.4-5
11/25 Formal Logic Sections 7.4, 7.5.1-3, 7.6, 8.1-2
12/4 Formal Logic Sections 8.3-4, 9.1, 9.2, 9.5

Homeworks

Number Assignment Due Late
Homework 1 Background knowledge assessment 9/16 2:40 PM 9/18 2:40 PM
Homework 2 Search 10/18 2:40 PM None
Homework 3 MDP's and Q-Learning 11/12 2:40 PM 11/14 2:40 PM
Homework 4 Bandits, MCTS, and Linear Classifiers 11/20 2:40 PM 11/22 2:40 PM
Homework 5 Neural Networks 12/3 2:40 PM 12/5 2:40 PM
Homework 6 Naive Bayes and Formal Logic 12/10 2:40 PM 12/12 2:40 PM
Extra Credit (Karma points) Genetic Algorithms 12/10 2:40 PM

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