CS63 Artificial Intelligence
Fall 2022

Course Information

Class: Mon., Wed., Fri. 10:30 - 11:20PM, Singer 222
Lab A: Friday 2:15 - 3:45PM, SCI 256
Lab B: Friday 4:00 - 5:30PM, SCI 256

Contact Information
Professor: Lisa Meeden
Email: meeden at cs dot swarthmore.edu
Office: Science Center 243
Office Hours: Wed. and Thur. 2:00-3:30 pm
And any time my door is open
Communication: Ed
I will post important announcements here
You can ask questions and answer other students' questions here


(See recent articles of AI in the news)

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
24%Exam 1, in lab 10/7/22
24%Exam 2, in lab 11/18/22
10%Final Project


Rather than using a single textbook, we will be using materials from a variety of sources. Many of the materials will be available online. You only need to purchace the first text listed below by Melanie Mitchell, which is required.

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 for the week before coming to class on Monday morning. 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 8:00am Monday of the week where they will be discussed.

You will clone a reading journal repo containing markdown files for each week's reading. You will write your responses in the appropriate file and submit them via git (using add, commit, and push).

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 Friday, during the scheduled lab time, and will be due by the following Thursday 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 to let me know.

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 please let me know as soon as possible.

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 through 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.



Aug 29

Introduction to AI

Introduction to AI

Lab 0: Robot in a Maze

Aug 31

Symbolic vs Subsymbolic

Sep 02

Properties of environments


Sep 05

Labor Day

Sep 07

Ben Mitchell guest lecture
Uninformed search

State space search

Lab 1: A* search on Traffic Jam

Sep 09

Informed search


Sep 12

Hill climbing
Map coloring

Local search

Lab 2: Local search on TSP

Sep 14

Simulated annealing

Sep 16

Beam search


Sep 19

Adversarial search

Game tree search

Lab 3: Game play with Minimax

Sep 21

Minimax algorithm

Sep 23

Pruning algorithm


Sep 26

Intro to MCTS

Monte Carlo Search

Lab 4: MCTS with Hex

Sep 28

UCB and MCTS values
Trace of MCTS

Sep 30

Implementing MCTS


Oct 03

Evaluating Classic AI

Evaluating Classical AI

Exam 1 in lab

Oct 05

Exam 1 Review

Oct 07

Exam 1 Study Guide


Oct 10

Fall Break

Oct 12

Oct 14


Oct 17

Supervised learning, Perceptrons

Perceptrons and Neural Networks

  • Review pgs 24-34 on Perceptrons Mitchell Ch. 1
  • Neural Networks and the Ascent of Machine Learning Mitchell Ch. 2
  • Videos on neural networks and backpropagation
  • Journal questions

Lab 5: Neural Networks

Oct 19

Perceptron learning
Multilayer networks

Oct 21

Derivation of Backprop


Oct 24

Motivating deep learning

Deep Learning

Lab 6: Convolutional Networks

Oct 26

Deep learning

Oct 28



Oct 31

Reinforcement learning

Reinforcement Learning

  • Rewards for Robots Mitchell Ch. 8
  • Game On Mitchell Ch. 9 (pgs. 145-152)
  • Beyond Games Mitchell Ch. 10
  • Journal questions

Lab 7: Deep Q-Learniing

Nov 02

Demo of Q-learning, Intro to DeepQ

Nov 04

DeepQ applied to Atari games


Nov 07

Intro to GAs

Genetic Algorithms

Lab 8: Genetic Algorithms

Nov 09


Nov 11

GAs applied to RL problems


Nov 14

Schema theorem

Evaluating Machine Learning

Exam 2 in lab

Nov 16

Evaluating machine learning

Nov 18

Exam 2 Study Guide


Nov 21


Final Project

No lab

Nov 23


Nov 25



Nov 28

Strong vs Weak AI

Philosophy of AI

Project checkpoint

Nov 30


Dec 02

Singularity, Embodiment


Dec 05

Summing up

Future of AI

  • Knowledge, Abstraction, and Analogy in AI Mitchell Ch. 15
  • Questions, Answers, and Speculations Mitchell Ch. 16
  • Journal questions

Project continued

Dec 07

ChatGPT, Robot game