Syllabus • Schedule • Academic Integrity • Piazza Page

Please note:

 • All readings refer to chapter sections in the textbook.
 • Pre-readings should be done before class.
 • All the reading material is covered on exams, so please make sure to bring questions about it to class.
 • Due dates are the night before the listed class. For example, homework 1 is due on 9/18 at 11:59, the night before the 9/19 class.

The schedule:

This is a tentative schedule.  Topics, reading assignments, homeworks, and exam dates are subject to change.

class date topic / slides pre-readings readings homework handouts / notes / links
1 8/31 Introduction and overview Class web page
Integrity policy
 Ch. 1 Intro survey out
Intro & URLs
2 9/5 Agents 2.1, 2.2 intro, 2.2.1;
skim 2.3.1–2.3.2		 Ch. 2 Survey due
By 11:59 9/4—see above
HW1 out
3 9/7 Problem solving as search 3.1 intro, 3.1.1, skim 3.3 Ch. 3.1–3.3
4 9/12 Uninformed search 3.4 intro, 3.4.1–3.4.3 Ch. 3.4
5 9/14 Informed search 3.5 intro, 3.5.1, skim 3.5.2 Ch. 3.5–3.7
6 9/19 Python for AI
Unit Testing in Python
50 Years in AI 		HW1 due
7 9/21 Local search, genetic algorithms 4.1 intro, 4.1.1 Ch. 4.1–4.2 HW2 out
8 9/26 Constraint Satisfaction 6 intro, 6.1 intro, 6.1.1 Ch. 6.1–6.4 (skip 6.3.3)
supplement:
Vipin Kumar Survey
9 9/28 Game playing 5 intro, 5.1 Ch. 5.1–5.3, 5.4.1, 5.5
10 10/3 Probabilistic Reasoning 13.2.1-13.2.2 Ch. 13 Be sure that you understand the concepts: random variables, prior probabilities, conditional probabilities, the product rule, and the joint probability distribution. It is essential that you understand the math in Ch. 13!
11 10/5 Bayesian networks Really understand Ch. 13 Ch. 14.1–14.4.2; skim 14.3 HW2 due
HW3 out
Corrected instructions
12 10/10 Decision making under uncertainty 15.1 Ch. 15.1–15.2.1, 16.1–16.3
13 10/12 Multi-agent systems Ch. 17.5–17.6
14 10/17 Midterm review, homeworks, project overview Project teams form
15 10/19
Midterm: through multi-agent systems
16 10/24 Project prep and New Eleusis HW3 due
HW4 out
 Project description ← updated
17 10/26 ML 1: Concepts, decision trees 18.2 Ch. 18.1–18.3
18 10/31 Midterm review, ML 2 20.1 Ch. 20.1–20.2 new_eleusis.py ← ← updated
new_eleusis_test.py ← updated
A short README
19 11/2 ML 3: Model evaluation, Bayes learning
11/5
11/5 (Sunday) at 11:59pm: Project design due
20 11/7 ML 4: Bayes nets; Knowledge-based agents 7.4.1–7.4.2 Ch. 7
21 11/9 Propositional Logic, First-order logic 8.2 Ch. 8.1–8.3 HW4 due
HW5 out
22 11/14 Logical agents, Logical inference 9.5 Ch. 9
23 11/16 Planning & Partial-order planning Ch. 10.1–10.2, 10.4.2–10.4.4 Project Phase I due
24 11/21 Applications: Robotics HW5 due No HW6, to give you time to work on the project. Please make sure to study planning, planning state spaces, partial-order planning, MDPs, and reinforcement learning.

game.py ← updated
(includes adversary)
11/23 Thanksgiving Day
25 11/28 Probabilistic planning Ch. 17.1–17.2.2, 17.4.1 Ch. 17.1–17.3
26 11/30 Guest Lecture: Applications
of Machine Learning
12/1
12/1 (Friday) at 11:59pm: Phase II due
27 12/5 Reinforcement learning Ch. 21.1–21.3
28 12/7 Clustering and EM
Ethics and AI
29 12/12 New Eleusis tournament (tentative) Project final writeup due
Final 12/19 Final exam, December 19th, 1:00-3:00 PM
Final exam review slides (BIG)
Final exam review slides (normal)

This class is closely patterned after Dr. Marie desJardin's excellent AI class, with thanks.