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 9/16 at 11:59, the night before class.

The schedule:

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

date topic / slides pre-readings readings homework handouts / notes
8/29 Introduction and overview Syllabus
Integrity policy
 Ch. 1 Syllabus: http://tiny.cc/671-class
Schedule: http://tiny.cc/671-schedule
Integrity: http://tiny.cc/671-integrity
9/3 Agents 2.1, 2.2 intro, 2.2.1;
skim 2.3.1-2.3.2		 Ch. 2 HW1 out (updated turnin instructions)
9/5 Problem solving as search
Uninformed search 1		 3.1 intro, 3.1.1, skim 3.3
3.4 intro, 3.4.1–3.4.3 		Ch. 3.1–3.4
9/10 Informed search 3.5 intro, 3.5.1, skim 3.5.2 Ch. 3.5–3.7
9/12 Local search, genetic algorithms
Intro to constraint satisfaction		 4.1 intro, 4.1.1 Ch. 4.1–4.2
9/17 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 9/16
HW2 out (note new version with one constraint removed!)
9/19 Game playing 5 intro, 5.1 Ch. 5.1–5.3, 5.4.1, 5.5
9/24 Probabilistic reasoning
Bayes' Nets 1
 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!
9/26 Inference in Bayesian networks
 Really understand Ch. 13 Ch. 14.1–14.4.2; skim 14.3
10/1 Decision making under uncertainty 15.1 Ch. 15.1–15.2.1, 16.1–16.3 HW2 due 9/30
HW3 out
Project description out		 Slides with filtering examples
Filtering example as a writeup
Filtering math as a spreadsheet
10/3 Multi-agent systems Ch. 17.5–17.6
10/8 ML 1: Concepts, decision trees intro 18.2 Ch. 18.1–18.3 Alice Gao on Variable Elimination
10/10 Midterm review, with notes
Class canceled, please see email!
10/15 Midterm: through multi-agent systems
10/17 Guest lecture:
Robotics, Dr. Puck Wen
10/22 ML 2: Decision trees,
Evaluating learned models
Dr. Puck Wen		 20.1 Ch. 20.1–20.2 HW3 due 10/21 Information Gain example as a writeup
10/24 Knowledge-based agents, propositional logic 7.4.1-7.4.2 Ch. 7 HW3 due 10/25
10/29 First-order logic
Midterm review		 8.2, 9.5 Ch. 8.1–8.3 Project design due
10/28
10/31 Knowledge-based agents, Logical inference Ch 9
HW4 out Writeup of filtering problem from HW3
11/5 Knowledge Representation Ch. 12.1–12.2, 12.5–12.6
11/7 NO CLASS—project work day!
11/12 Planning and partial-order planning Ch. 10.1–10.2, 10.4.2–10.4.4
11/14 Probabilistic planning Ch. 17.1–17.2.2, 17.4.1 Ch. 17.1–17.3 Project phase 1 code due 11/13 now 11/17
11/19 Reinforcement learning Ch. 21.1–21.3
11/21 Clustering
Ethics 1		 HW4 due 11/20
11/26 Ethics 2
11/28 Thanksgiving Day
12/3 Applications: NLP Project phase 2 code due 12/2 now 12/7
12/5 Final exam review
12/10 Final exam (in class)
12/15 Project final paper now due 12/15 at 11:59 PM
12/17 Slip day Our final exam period is 12/17 1–3 PM