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 9/17 at 11:59, the night before the 9/18 class.

The schedule:

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

Ch. 21.1–21.3
class date topic / slides pre-readings readings homework handouts / notes / links
1 8/30 Introduction and overview Class web page
Integrity policy
 Ch. 1 Read the syllabus and integrity policy.
Take the Intro survey.		 Handout: Intro & URLs
2 9/4 Guest Lecture: Python for AI Survey due
By 11:59 9/3—see above
HW1		 Just Enough Python
Unit Testing in Python
A Concise Guide to Python 3
3 9/6 Agents 2.1, 2.2 intro, 2.2.1;
skim 2.3.1–2.3.2		 Ch. 2
4 9/11 Problem solving as search 3.1 intro, 3.1.1, skim 3.3 Ch. 3.1–3.3
5 9/13 Uninformed search 3.4 intro, 3.4.1–3.4.3 Ch. 3.4
6 9/18 Informed search 3.5 intro, 3.5.1, skim 3.5.2 Ch. 3.5–3.7 HW1 due
HW2 out ← Updated
7 9/20 Local search, genetic algorithms 4.1 intro, 4.1.1 Ch. 4.1–4.2
8 9/25 Constraint Satisfaction 6 intro, 6.1 intro, 6.1.1 Ch. 6.1–6.4 (skip 6.3.3)
Also read:
Vipin Kumar Survey
9 9/27 Guest Lecture:
102 Years of AI
10 10/2 Constraint Satisfaction 2
Game Playing 1		 5 intro, 5.1 Ch. 5.1–5.3, 5.4.1, 5.5 HW2 due
11 10/4 Game playing 2 Catch up
on reading		 HW3 out Because some people are having trouble with Python, in this one we heavy up on problem sets instead; this way you can get your code in order before HW4. Also, please note updated due date.
12 10/9 Probabilistic Reasoning 13.2.1-13.2.2 Ch. 13 Be sure that you understand: 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.
13 10/11 Bayesian networks Really understand Ch. 13 Ch. 14.1–14.4.2; skim 14.3
14 10/16 Multi-agent systems Ch. 17.5–17.6 Midterm study guide
15 10/18 Decision making under uncertainty 15.1 Ch. 15.1–15.2.1, 16.1–16.3 HW3 due
16 10/23
Midterm: through multi-agent systems
17 10/25 Decision theory 2: Utility Project teams formed
18 10/30 Guest Lecture: Applications of AI
19 11/1 ML 1: Concepts, Decision trees Ch. 18.1–18.3
20 11/6 Decision trees (cont.) 20.1 Ch. 20.1–20.2 HW4 out ← Updated
21 11/8 ML 2: Learning systems, model evaluation
22 11/13 ML 4: Knowledge-based agents 7.4.1–7.4.2 Ch. 7 Project Description IF time permits: projects lab day. Please BRING computers, notes, and any materials you may need!
23 11/15 ML 3: Bayes learning, Bayes nets Topic postponed: snow day.
24 11/20 Propositional Logic, First-order logic 8.2 Ch. 8.1–8.3
11/22 Thanksgiving Day
11/26
Project Plan Due
25 11/27 ML 3: Bayes learning, Bayes nets HW4 Parts I, II, IV due
HW5 out
26 11/29 Logical agents, Logical inference 9.5 Ch. 9
HW4 Part III due
12/2
Project Phase I due
27 12/4 Planning & Partial-order planning Ch. 10.1–10.2 10.4.2–10.4.4
28 12/6 Project work day: bring computers! HW5 due
 No HW6, to give you time to work on the project.
12/9
12/9 at 11:59pm: Final project turnin due
29 12/11 Final exam review
12/16
Project final writeup due
Final 12/18
Final exam, December 18th, time 1:00–3:00 PM

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