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.
 • "Soft days" give us room if something takes longer than expected. They are regular classes.
 • Due dates are the night before the listed class. For example, homework 1 is due on 9/16 at 11:59, the night before the 9/17 class.

The schedule:

This is a tentative schedule.  Slides will be posted after class. Topics, assignments, homeworks, and exam dates will change.

class date topic / slides pre-readings readings homework handouts / notes / links
1 8/29 Introduction and overview Class web page
Integrity policy
 Ch. 1
2 9/3 Agents 2.1, 2.2 intro, 2.2.1;
skim 2.3.1–2.3.2		 Ch. 2 HW1 out
3 9/5 Problem solving as search 3.1 intro, 3.1.1, skim 3.3 Ch. 3.1–3.3
4 9/10 Uninformed search 3.4 intro, 3.4.1–3.4.3 Ch. 3.4
5 9/12 Informed search 3.5 intro, 3.5.1, skim 3.5.2 Ch. 3.5–3.7
6 9/17 Campus closed due to water outage HW1 due
(11:59pm 9/16)
HW2 out
7 9/19 Local search, genetic algorithms 4.1 intro, 4.1.1 Ch. 4.1–4.2
8 9/24 Constraint Satisfaction 6 intro, 6.1 intro, 6.1.1 Ch. 6.1–6.4 (skip 6.3.3)
supplement:
Vipin Kumar Survey
9 9/26 CSPs 2, Game playing 1 5 intro, 5.1 Ch. 5.1–5.3, 5.4.1, 5.5
10 10/1 Probabilistic Reasoning
Guest lecturer: Dr. Ferraro		 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 10/3 AI Applications: Vision
Guest lecturer: Dr. Pirsiavash		 13.2.1-13.2.2 Ch. 13 HW2 due
12 10/8 Bayesian networks Really understand Ch. 13 Ch. 14.1–14.4.2; skim 14.3
13 10/10 Games 2, Multi-Agent Systems Ch. 17.5–17.6
14 10/15 Homework Review, Midterm Review Project teams form
15 10/17 Decision making under uncertainty 15.1 Ch. 15.1–15.2.1, 16.1–16.3
16 10/22
Midterm: through 10/15
17 10/24 ML 1: Concepts, decision trees 18.2 Ch. 18.1–18.3 Project description
18 10/29 Project problem practice
19 10/31 ML 2: Intro to information theory 20.1 Ch. 20.1–20.2
20 11/5 ML 3: Model evaluation 7.4.1–7.4.2 Ch. 7
21 11/7 Bayesian Reasoning, Bayes' Nets 2
Guest lecturer: Dr. Ferraro
22 11/12 Logical inference, Knowledge-based Agents, Knowledge Representation Ch. 8
23 11/14 Project design work
24 11/19 Clustering
25 11/21 Planning and Partial-Order Planning Ch. 10.1–10.2, 10.4.2–10.4.4
26 11/26 Partial-order planning (slides above), project work
11/28 Thanksgiving Day
12/1
12/1 (Friday) at 11:59pm: Phase II due
27 12/3 Reinforcement learning Ch. 21.1–21.3
28 12/5 Guest lecture: 100+ Years of AI
Drs. Paula & David Matuszek
29 12/12 Tournament Project final writeup due Last Day of Class!
Final 12/17 Final exam, December 17th, 1:00-3:00 PM, regular classroom