Bryn Mawr College
CMSC/PHIL 372: Artificial Intelligence
Spring 2012 - Section 001

Syllabus and Schedule Course Information Text and Software
Course Policies
Reference Links

Syllabus and Schedule

This is a tentative syllabus and schedule.  Topics, reading assignments, and due dates are subject to change.
All projects/papers are due by 11:59:59pm on the day listed by electronic submission.  All written problem sets are due by the start of class.
By default, the readings refer to Russell and Norvig.
* Note that Prof. Eaton may be on travel for one or more days during these weeks, subject to change.
Week Date Topic Reading Assignments
1 1/17 Course overview; What is AI? skim Ch. 1, McCarthy paper, Dartmouth Proposal

1/19 Intelligent Agents

Ch. 2

Python Lab Exercise (optional, but strongly recommended)
Python tutorial
2 1/24 Problem solving as search Ch. 3.1-3.3   PPT version of slides with animations
1/26 Uninformed search (BFS,DFS,UCS)
Ch. 3.4
same slides as 1/24
3 1/31 Informed search (A*)
Ch. 3.5-3.7
Problem Set 1 due in-class

2/2 Optimization and Genetic Algorithms
Ch. 4.1-4.2, Genetic Algorithms  
Adversarial Search Ch. 5.1-5.3 Project 1 / Paper 1 due by midnight

Ch. 5.4-5.5, 5.7, 5.9, Schaeffer article

Reasoning Under Uncertainty
5 2/14 Probabilistic Reasoning Ch. 13.1-13.8, Skim 7.2

2/16 Bayesian Networks (Part I)
Ch. 14.1-14.2
 Problem Set 2 due in-class
2/21 Bayesian Networks (Part II)
Ch. 14.3-14.4.1
Ch. 14.5.2


Project 2 / Paper 2 due by midnight
2/28 HMMs and Filters
Ch. 15.1-15.3

3/1 Ch. 15.4-15.6 Problem Set 3 due in-class

Spring Break

Midterm Exam

Perspectives on AI Chronology of AI; Ch. 26, Turing article; Searle article

Machine Learning
Reinforcement Learning
Sutton and Barto, Skim Ch. 3,4,6


Project 3 / Paper 3 due by midnight
Supervised Learning: kNN, Naive Bayes, Linear Models


Supervised Learning: SVMs

Problem Set 4 due in-class

Supervised Learning: Neural Nets


Project 4 / Paper 4 due by midnight
13 4/17
Unsupervised Learning

No Class - Prof. Eaton on Travel



Problem Set 5 due in-class
Optional Project/Paper due by midnight
Kristen Grauman's Lecture on Computer Vision (4/26 @ 4pm)

Final Exam

General Information

Instructor: Eric Eaton, Ph.D.

When you e-mail me, make sure you put "CS372" or "PHIL372" at the start of the subject line to ensure a quicker response.

E-mail is the best way to reach me, and I make a concerted effort to respond to all e-mails within 24 hours on weekdays and 48 hours on weekends (often, much less!).

Office Hours:
Tuesday/Thursday 1-2pm and by appointment in Park 249

Tuesday/Thursday 9:45 am to 11:15 am
Room: Park 349
Lab: Fridays 10am-Noon in Park Room 231 (Computer Science Lab)

Course Description:   Survey of Artificial Intelligence (AI), the study of how to program computers to behave in ways normally attributed to “intelligence” when observed in humans. Topics include heuristic versus algorithmic programming; cognitive simulation versus machine intelligence; problem-solving; inference; natural language understanding; scene analysis; learning; decision-making.

Artificial Intelligence is, in my opinion, the coolest and most exciting subfield of computer science. In this course, we will explore several major areas of AI, including search, game playing, reasoning under uncertainty, planning, and machine learning. We will also relate these topics to their applications in robotics, computer gaming, medical diagnosis, computer vision, natural language understanding, and many other areas. Google, NSA, and NASA all use AI technology. AI's successes, such as the IBM Deep Blue chess program beating Kasparov (1996), the DARPA Urban Challenge (2007) and Grand Challenge (2005, 2004) autonomous vehicle competitions, and solving checkers (2007) are well-publicized and very popular.

Prerequisite: Computer Science 206, or Junior standing in Philosophy, or permission of instructor.

Text & Software

Required Textbook:
Russell and Norvig

Stuart J. Russell and Peter Norvig, 2009. Artificial Intelligence: A Modern Approach, 3rd edition, Prentice Hall. ISBN: 0136042597.

The website for this book has links to many useful online AI/Python resources.

Recommended (CMSC only):  Some Python reference.  I'm recommending several below.
Learning Python
Learning Python, 2nd Edition by Mark Lutz and David Ascher.  Published by O'Reilly Media.
Python In a Nutshell
Python in a Nutshell by Alex Martelli.  Published by O'Reilly Media.

The Python Tutorial, available online at

Course Policies


Attendance and active participation are expected in every class. Participation includes asking questions, contributing answers, proposing ideas, and providing constructive comments.

As you will discover, I am a proponent of two-way communication and I welcome feedback during the semester about the course. I am available to answer student questions, listen to concerns, and talk about any course-related topic (or otherwise!). Come to office hours! This helps me get to know you. You are welcome to stop by and chat. There are many more exciting topics to talk about that we won't have time to cover in-class.

Whenever you e-mail me, be sure to use a meaningful subject line and include the phrase "CS372" or "PHIL372" at the beginning of that line. Your e-mail will catch my attention and I will respond quicker if you do this. I make an effort to respond to e-mails within 24 hours on weekdays and 48 hours on weekends.

Although computer science work can be intense and solitary, please stay in touch with me, particularly if you feel stuck on a topic or project and can't figure out how to proceed. Often a quick e-mail, phone call or face-to-face conference can reveal solutions to problems and generate renewed creative and scholarly energy. It is essential that you begin assignments and projects early, since we will be covering a variety of challenging topics in this course.


Your grade will be based upon five problem sets, two exams, and either (CMSC) 5 projects or (PHIL) 5 papers.

At the end of the semester, final grades will be calculated as a weighted average of all grades according to the following weights:

Midterm Exam: 10%
Final Exam: 20%
5 Problem Sets: 25% (5% each)
5 Projects/Papers:
45% (9% each)
Total: 100%

Incomplete grades will be given only for verifiable medical illness or other such dire circumstances.

All graded work will receive a percentage grade between 0% and 100%.  Here is how the percentage grades will map to final letter grades:

Rounded Percentage
Letter grade

Rounded Percentage Letter grade
97% -100%
A+ (4.0)
77% - 79% C+ (2.3)
93% - 96% A (4.0) 73% - 76% C (2.0)
90% - 92% A- (3.7) 70% - 72% C- (1.7)
87% - 89% B+ (3.3) 67% - 69% D+ (1.3)
83% - 86% B (3.0) 60% - 66% D (1.0)
80% - 82% B- (2.7) 0% - 59% F (0.0)

The instructor reserves the right to adjust the percentage ranges for each letter grade upward in your favor.

Submission and Late Policy

All work must be turned in either in hard-copy or electronic submission, depending on the instructions given in the assignment.  E-mailed submissions will not be accepted.  Extensions will be given only in the case of verifiable medical excuses or other such dire circumstances, if requested in advance.

Late submissions will receive a penalty of 10% for every 0-24 hours it is past the due date and time (e.g., assignments turned in 25 hrs late will receive a penalty of 20%).

Problem Sets

All problem set solutions must be typeset. Any solutions requiring math or proofs must use proper mathematical notation. Similarly, graphs should be well-constructed with all axes properly labeled. I highly recommend the use of the LaTeX text formatting system, but you can use any program capable of producing proper mathematical notation. The bottom of this page contains links to good LaTeX websites and links to example files.

All solutions are required to be your own, individual work. I encourage you to discuss methods, concepts, and assignments with anyone; however, the solutions turned in must be your own work. A good rule of thumb is to be alone when you sit down to actually generate solutions to the assigned problems, and to not show your solutions to anyone else.

At the top of your submission, you must include a clear statement specifying the source of any assistance you received on this assignment.  This includes any websites you consulted, other students with whom you discussed any of the problems, etc.  If you did not receive any assistance, you must say so.  Submissions without this statement will be penalized.


There will be two exams in this course.  The exams will be closed-book and closed-notes.  They will cover material from lectures, homeworks, and assigned readings (including topics not discussed in class).  So, keep up with those readings!  In the case of verifiable medical excuses or other such dire circumstances, arrangements must be made with the instructor for a makeup exam before the test date.

Study Groups

I want to encourage you to discuss the material and work together to understand it. Here are my thoughts on collaborating with other students:

If you have any questions as to what types of collaborations are allowed and which are dishonest, please ask me before you make a mistake.

Electronic Devices

I have no problem with you using computers or tablets to take notes or consult reference materials during class.  Tempting though it may be, please do not check e-mail or visit websites that are not relevant to the course during class.  It is a distraction, both for you and (more importantly) for your fellow classmates.  Please silence your phones and computers when you enter class.

Reference Links