CS63 Artificial Intelligence
Spring 2015

Schedule | Grading | Academic Integrity | Lab Policy | Academic Accommodations


Have a wonderful summer!

Course Information

Class: T, TH 9:55am – 11:10am SCI 181
Lab A: F 1:15 – 2:45pm SCI 256
Lab B: F 3:00 – 4:30pm SCI 256

Professor: Lisa Meeden
Office: SCI 243
Office Hours: W 2:00 – 5:00, or by appointment
Phone: 8565


Artificial Intelligence (AI) is the branch of computer science that is concerned with the automation of intelligent behavior. Intelligent behavior encompasses a wide range of abilities. As a result, AI has become a very broad field that includes search, game playing, reasoning, planning, natural language processing, modeling human performance (cognitive science), machine learning, and robotics. This course will focus on a subset of these topics, specifically search and machine learning, while also drawing connections to cognitive science. In search, we will see familiar techniques such as depth-first and breadth-first, as well as new techniques such as A*, minimax, and genetic algorithms applied to AI problems. In machine learning, which is concerned with how to create programs that automatically learn from experience, we will explore reinforcement learning and neural networks. The first half of the semester will focus on search, and the second half of the semester will focus on machine learning.


Required Text Book: Artificial Intelligence: A Modern Approach, Third Edition by Stuart Russell and Peter Norvig. This book is also on reserve at Cornell. There will be supplemental readings available as pdfs.

Goals for the course


Grades will be weighted as follows:
5%Class Participation
5%Reading Quizzes
25%Exam 1: Thursday, Feb. 26
25%Exam 2: Thursday, Apr. 16
10%Final Project

Academic Integrity

Academic honesty is required in all of your work. Under no circumstances may you hand in work done with (or by) someone else under your own name. Your code should never be shared with anyone; you may not examine or use code belonging to someone else, nor may you let anyone else look at or make a copy of your code. This includes, but is not limited to, obtaining solutions from students who previously took the course or code that can be found online. You may not share solutions after the due date of the assignment.

Failure to abide by these rules constitutes academic dishonesty and will lead to a hearing of the College Judiciary Committee. According to the Faculty Handbook: "Because plagiarism is considered to be so serious a transgression, it is the opinion of the faculty that for the first offense, failure in the course and, as appropriate, suspension for a semester or deprivation of the degree in that year is suitable; for a second offense, the penalty should normally be expulsion."

Discussing ideas and approaches to problems with others on a general level is fine (in fact, we encourage you to discuss general strategies with each other), but you should never read any other student's code or let another student read your code. All code you submit must be your own with the following permissible exceptions: code distributed in class, code found in the course text book, and code worked on with a partner. In these cases, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were.

Lab Policy

Labs will be assigned on Fridays, during the scheduled lab time, and will be due on Thursdays by midnight. Some labs will be two weeks in duration, while others will only be one week long. Even if you do not fully complete a lab, you should submit what you have done to receive partial credit.

Late labs will only be accepted if you contact me at least a day before the deadline with a legitimate reason for needing extra time (such as an illness or needing to leave campus).

Academic Accommodations

If you believe that you need accommodations for a disability, please contact Leslie Hempling in the Office of Student Disability Services (Parrish 113) or email lhempli1@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, she will issue students with documented disabilities a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact her as soon as possible. For details about the accommodations process visit Student Disability Services. You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged through the Office of Student Disability Services.



Jan 20


Introduction to AI
Tues: R&N, Ch. 1 (pg 1-29)
Thur: R&N Ch. 2 (pg 34-57)
The Thinking Machine
Using git
Lab 1: Pac-Man Agents

Jan 22



Jan 27

State space

Tues: R&N, Ch. 3 (pg 64-81)
Thur: R&N, Ch. 3 (pg 81-91)
Lab 2: Search in Pac-Man

Jan 29

Lab 1 due

Blind search


Feb 03

Informed search

Informed and Local Search
Tues: R&N Ch. 3 (pg 92-107)
Thur: R&N, Ch. 4 (pg 120-130)

Feb 05

Local search


Feb 10

Evol. search

Evolutionary Search
Tues: Mitchell, Ch. 1 (pg 2-12)
Lab 3: Genetic Algorithm

Feb 12

Lab 2 due

Schema Theorem


Feb 17

Game Playing

Adversarial Search
Tues: R&N, Ch. 5 (pg 161-167)
Thur: R&N, Ch. 5 (pg 167-174)

Feb 19

Lab 3 due



Feb 24

Exam 1 in class Feb. 26
Lab 4: Game playing

Feb 26


Mar 03

Machine learning

Machine Learning
Tues: R&N, Ch. 18 (pg 693-697)
Thur: R&N, Ch. 18 (pg 697-710)

Mar 05

Lab 4 due

Decision trees


Mar 10

Spring Break

Mar 12


Mar 17


Markov Decision Processes
Tues: R&N, Ch. 17 (pg 645-658)
Thur: Guest David Mimno '99
Lab 5: Reinforcement Learning

Mar 19

Value iteration


Mar 24

Reinforcement learning

Reinforcement learning
Tues: R&N, Ch. 21
Thur: Tesauro's TDgammon

Mar 26

Last day to declare
CR/NC or W (Mar 27)


Mar 31

Neural nets

Neural Networks
Tom Mitchell, Ch. 4
Tues: pgs 81-89
Thur: pgs 89-91, 95-99, 104-116, 122-123 (summary)
Lab 6: Neural Networks

Apr 02

Lab 5 due



Apr 07


Embodiment and Robotics
Subsumption Architecture
Lab 7: Subsumption architecture

Apr 09

Lab 6 due


Apr 14

Exam 2 in class April 16
Lab 8: Machine learning project

Apr 16

Lab 7 due


Apr 21

Philosophy of AI

Philosophical foundations and the Future
Tues: R&N Ch. 26: pgs 1020-1033
Thur: R&N Ch. 27: all
Work on project

Apr 23

Summing Up


Apr 28

Deep RL

State of the art in AI
Tues: Deep Mind Article1, Article2
Thur: Watson
Complete project

Apr 30

Final project due (May 04)