Syllabus • Schedule • Academic Integrity 

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 9/19 at 11:59, the night before the 9/20 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
1 9/1 Introduction and overview Syllabus
Integrity policy
 Ch. 1 Syllabus: http://tiny.cc/ai-class
Schedule: http://tiny.cc/ai-schedule
Integrity: http://tiny.cc/ai-ai
2 9/6 Agents 2.1, 2.2 intro, 2.2.1;
skim 2.3.1-2.3.2		 Ch. 2 HW1 out
3 9/8 Problem solving as search
Uninformed search		 3.1 intro, 3.1.1, skim 3.3
3.4 intro, 3.4.1–3.4.3 		Ch. 3.1–3.4
4 9/13 Informed search 3.5 intro, 3.5.1, skim 3.5.2 Ch. 3.5–3.7
5 9/15 Local search, genetic algorithms
Intro to constraint satisfaction		 4.1 intro, 4.1.1 Ch. 4.1–4.2
6 9/20 Constraint Satisfaction 6 intro, 6.1 intro, 6.1.1 Ch. 6.1–6.4 (skip 6.3.3)
supplement:
Vipin Kumar Survey		 HW1 due
HW2 out
7 9/22 Game playing 5 intro, 5.1 Ch. 5.1–5.3, 5.4.1, 5.5
8 9/27 Probabilistic reasoning
Bayes' Nets
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!
9 9/29 Inference in Bayesian networks
Guest lecturer: Nadja Bzhilyanskaya		 Really understand Ch. 13 Ch. 14.1–14.4.2; skim 14.3
10 10/4 Decision making under uncertainty (Updated filtering slides) 15.1 Ch. 15.1–15.2.1, 16.1–16.3 HW2 due
HW3 out
11 10/6 Multi-agent systems Ch. 17.5–17.6
12 10/11 ML 1: Concepts, decision trees intro 18.2 Ch. 18.1–18.3
13 10/13 Review,
project teams formed		 Project description out
Teams information due (only one person has to fill this out)
14 10/18 Midterm: through multi-agent systems
15 10/20 Class canceled - work on project ideas! Slides with filtering examples
Filtering example as a writeup
Filtering math as a spreadsheet
Information Gain example as a writeup
16 10/25 ML 2: Decision trees,
Evaluating learned models		 20.1 Ch. 20.1–20.2
17 10/27 Knowledge-based agents, propositional logic 7.4.1-7.4.2 Ch. 7 HW3 due
HW4 V3 out
10/28 HW3 due Friday 10/28 at 11:59 PM
18 11/1 First-order logic 8.2, 9.5 Ch. 8.1–8.3 Project design due
at 11:59pm, 10/31
19 11/3 Knowledge-based agents, Logical inference Ch 9
20 11/8 Knowledge Representation
Project work		 Ch. 12.1–12.2, 12.5–12.6
21 11/10 Planning and partial-order planning Ch. 10.1–10.2, 10.4.2–10.4.4 HW4 due
22 11/15 Probabilistic planning Ch. 17.1–17.2.2, 17.4.1 Ch. 17.1–17.3
23 11/17 No lecture Project phase 1 code due
24 11/22 Reinforcement learning Ch. 21.1–21.3 HW5 out
11/24 Thanksgiving Day
25 11/29 Project work day - bring computers!
26 12/1 Clustering and Ethics
12/3 HW5 due Saturday 12/3 at 11:59 PM
27 12/6 Applications: Robotics Project phase 2 code due
12/7 Project Phase II code due 12/7 at 11:59 PM
28 12/8 Applications: Natural Language
Final exam review		 Project final paper due
12/10 Project final paper due 12/10 at 11:59 PM
29 12/13 Final exam (in class)