CS63 Artificial Intelligence
Spring 2019


Final projects are now due Sunday, May 5th by midnight.

Course Information

Class: Mon., Wed., Fri. 11:30 - 12:20PM, Science Center 181
Lab A: Thursday 1:05 - 2:35PM, Clothier
Lab B: Thursday 2:45 - 4:15PM, Clothier

Contact Information
Professor: Lisa Meeden
Email: meeden at cs dot swarthmore.edu
Office: Science Center 243
Office Hours: Tuesday 1:30-3:30pm
Wednesday 1:30-3:30pm
And any time my door is open


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 simulated annealing 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 2/28/19
20%Exam 2, in lab 4/18/19
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:

Many of the readings are fairly short, but can contain mathematics or algorithms that require a moderate amount of study. Make your best effort to understand the material and bring any questions 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 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.

In our first lab you will clone a reading journal repo containing several text files: one for the search readings, another for the machine learning readings, and a final one for the more philosophical issues we'll be reading about at the end of the semester. 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 grades and comments will be pushed to your grading repo each week. 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 2018 Jerod Weinman



Labs will be assigned on Thursday, during the scheduled lab time, and will be due by the following Wednesday before midnight. Even if you do not fully complete a lab, you should submit what you have done to receive partial credit.

You should work with a partner on all labs after lab 0. We will be using Teammaker to facilitate the creation of partnerships. Note that you must select a partner from within your lab section. If you and another student would like to partner together, simply select one another via Teammaker. 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. Note that for illnesses, the College's medical excuse policy, states that you must be seen and diagnosed by the Worth Health Center if you would like them to contact your class dean with corroborating medical information.

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.



Jan 21

No class
MLK day

Introduction to AI

Lab 0

Jan 23


Jan 25


Jan 28

Subsymbolic vs Symbolic AI

State space search

Lab 1

Jan 30


Feb 01

A* and heuristics


Feb 04

Local search, hill climbing

Local search

Lab 2

Feb 06

Simulated annealing, Beam search

Feb 08

Genetic algorithms


Feb 11

Game playing

Game tree search

Lab 3
Tournament results

Feb 13


Feb 15

Alpha-Beta pruning


Feb 18

Go and MCTS

Monte Carlo Search

Lab 4

Feb 20


Trace of MCTS

Feb 22

Review game playing


Feb 25


Review Search Approaches

Study Guide


Feb 27

Exam review questions

Mar 01

No class


Mar 04

Markov Decision Processes

Midterm Evaluation

Markov Decision Processes

Lab 5

Mar 06

Value iteration

Mar 08

Machine learning


Mar 11

Spring Break

Mar 13

Mar 15


Mar 18


Reinforcement Learning

Lab 6

Mar 20

Approx. Q-learning

Mar 22


Mar 25


Perceptrons and Neural Networks

Lab 7

Mar 27


Mar 29

Applying ANNs to problems


Apr 01

Deep learning, convolution

Deep Learning

Lab 8

Apr 03

More deep learning

Apr 05

Deep Q-learning


Apr 08

Self-supervised learning

Evaluating what Machines have Learned

Final Project

Apr 10

Possible problems with deep learning

Apr 12



Apr 15

Review Part1

Review Machine Learning

Study Guide


Apr 17

Review Part2

Apr 19

Ethical AI


Apr 22

Philosophy of AI

Philosophy of AI

Project Checkpoint

Apr 24

Philosophy continued

Apr 26

Bias in AI and ML
Ameet Soni Guest Lecture_S19


Apr 29

Future of AI

Future of AI

Finish Project

May 01

Summing up

May 03