CS63 Artificial Intelligence
Fall 2019

Course Information

Class: Mon., Wed., Fri. 10:30 - 11:20 PM, SCI 105
Lab: Fri. 2:15 - 3:45 PM, SCI 256

Contact Information
Professor: Lisa Meeden
Email: meeden at cs dot swarthmore.edu
Office: Science Center 243
Office Hours: Tue., Wed. 1:30-3:30


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, simulated annealing, 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.

Goals for the course


Grade weighting
5%Class Participation
10%Reading Journal
20%Exam 1, in lab 10/11/19
20%Exam 2, in lab 11/22/19
10%Final Project


Rather than using a single textbook, we will be using materials from a variety of sources including:

Our class meetings will be a combination of lecture and discussion. To be ready to participate in the discussion will require some preparation on your part. Most of this will consist of careful reading and reflection on the assigned reading through the use of a reading journal.


You should check the class schedule and read any material that has been assigned before coming to class. You will get the most out of the reading if you approach it as follows:

Make your best effort to understand the material and bring any questions that you have to class. Then we can have a productive discussion with examples and exercises to clarify the material.


To help focus your efforts and to give us a basis for discussion, you will be provided with a short list of questions to answer for each week's reading. Reflecting on your responses to the questions will help give you a deeper understanding of the most important concepts surrounding each topic.

Your responses are due by 11:59pm the night before class where they will be discussed. No late responses will be accepted.

You will clone a reading journal repo containing several weekly text files. You will write your responses in the appropriate file and submit them via git (using add, commit, and push) before each class meeting when reading is assigned.

While these low-stakes writing assignments are technically informal, they must reflect a certain level of engagement and evidence of thinking seriously about the material. Responses will be graded using the following scale:

My comments on your journals will be pushed to your grading repo. I expect that most entries will receive a CHECK, thus I will primarily comment on your journal to report a PLUS or a MINUS. You should expect to discuss the issues raised in your reading journal entries during class.

Copyright 2019 Jerod Weinman



Labs will be assigned on Friday, during the scheduled lab time, and will be due by the following Thursday before midnight.

You should work with a partner on all labs after lab 0. We will be using Teammaker to facilitate the creation of partnerships. If you would like to be assigned a random partner, you can select this option through Teammaker as well. For each lab assignment you must re-select partners. Thus you can try out a partnership one week, and then decide to try a different partnership the following week.

You have two late days that you may use on any lab, for any reason. If you are using a late day, you must contact me by email when you submit your lab.

Your late days will be counted at the granularity of full days and will be tracked on a per-student (NOT per-partnership) basis. That is, if you turn in an assignment five minutes after the deadline, it counts as using one day. For partnered labs, using a late day counts towards the late days for each partner. In the rare cases in which only one partner has unused late days, that partner's late days may be used, barring a consistent pattern of abuse.

If you feel that you need an extension on an assignment or that you are unable to attend class for two or more meetings due to a medical condition (e.g., extended illness, concussion, hospitalization) or other emergency, you must contact the dean's office and your instructors. Faculty will coordinate with the deans to determine and provide the appropriate accommodations. You should review the College's medical excuse policy.

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. The only exception to this policy, is that you may freely share code with your lab partner.

You should not obtain solutions from students who previously took the course or copy 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 and code given in the readings. In these cases, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were.

Academic Accommodations

If you believe that you need accommodations for a disability, please contact the Office of Student Disability Services (Parrish 113W) or email studentdisabilityservices@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, the Office will issue students with documented disabilities a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact the Office of Student Disability Services as soon as possible. For details about the accommodations process, visit the Student Disability Service Website. 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.



Sep 02


Introduction to AI

Python tutorial

Lab 0: State Space Search on Logic Puzzles

Sep 04

History of AI

Sep 06

Agents and Environments


Sep 09

State Space Search

Problem solving with search

Lab 1: A* Search on Traffic Jam game

Sep 11

Uninformed Search

Sep 13

Informed Search


Sep 16

Local Search and Hill Climbing

Local search

Lab 2: Local search on Traveling Salesperson Problems

Sep 18

Simuated Annealing and Stochastic Beam Search

Sep 20


Sep 23


Game tree search

Lab 3: Minimax search on Mancala and Breakthrough games

Sep 25

Game Search

Sep 27

Minimax and pruning
Trace result
Blank trace


Sep 30

Go and MCTS

Monte Carlo Search

Lab 4: MCTS on Hex game

Oct 02

MCTS details

Oct 04

MCTS pseudocode


Oct 07

Fundamental problems with classical AI

Evaluating Classical AI

EXAM 1, Friday in lab

Oct 09

Exam 1 review

Oct 11


Oct 14

Fall Break

Oct 16

Oct 18


Oct 21


Perceptrons and Neural Networks

Lab 5: Implementing Neural Networks

Oct 23

Multilayer Networks

Oct 25

Deriving Backpropagation


Oct 28

Motivating Convolutional Networks

Deep Learning

Lab 6: Convolutional Neural Networks

Oct 30

Deep Learning

Nov 01

Research that uses ANNs


Nov 04

Reinforcement learning

Reinforcement Learning

Lab 7: Deep Q-Learning

Nov 06

Q-learning, Approx. Q-learning

Nov 08

Deep Q-learning


Nov 11

Evolutionary Computation, GAs

Genetic Algorithms

Lab 8: Genetic Algorithms

Nov 13

Schema theorem

Nov 15

Evolving neural networks


Nov 18

Evaluating ML

Evaluating Machine Learning

EXAM 2, Friday in lab

Nov 20

Exam 2 review

Review notes

Nov 22


Nov 25


Final Project

No lab

Nov 27

No class

Nov 29



Dec 02

Melanie Mitchell visiting

Philosophy of AI

Project Checkpoint

Dec 04

Can machines think?

Dec 06

More philsophy of AI

Future of AI


Dec 09

Summing up

Future of AI

Finish Project