CS63 Artificial Intelligence
Spring 2022


Course Information

Schedule
Class: Mon., Wed., Fri. 10:30 - 11:20PM, Science Center 181
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

Introduction

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


Grading

Grade weighting
5%Class Participation
10%Reading Journal
35%Labs
20%Exam 1, in lab 3/4/22
20%Exam 2, in lab 4/15/22
10%Final Project

Reading

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 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.

Preparation

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.

Journal

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.

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) 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


Policies

Labs

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.



Schedule

WEEK DAY ANNOUNCEMENTS TOPIC & READING LAB
1

Jan 17

MLK Day

Jan 19

 

AI in the news

Python tutorial

Reading journal

Jan 21

 
2

Jan 24

Introduction

Introduction to AI

  • W: Roots of Artificial Intelligence Mitchell Ch. 1
    NOTE: Chapter 1 of this book is available online because we are remote. However, this is a required text that you should purchase for the rest of the semester.
  • Journal questions

Lab 0: Logic puzzles

Jan 26

Symbolic vs Subsymbolic AI

Jan 28

Properties of environments

3

Jan 31

State space search

State space search

Lab 1: A* on Traffic Jam

Feb 02

Uninformed search

Feb 04

Informed search

4

Feb 07

Hill climbing

Local search

Lab 2: Local search on TSP

Feb 09

Map coloring
Simulated annealing

Feb 11

Beam Search

5

Feb 14

Adversarial Search

Game tree search

Lab 3: Playing games

Feb 16

Bounded Minimax

Feb 18

AlphaBeta Pruning

6

Feb 21

Monte Carlo Tree Search

Monte Carlo Search

Lab 4: MCTS

Feb 23

MCTS details
MCTS trace

Feb 25

MCTS pseudocode

7

Feb 28

Problems with classic AI

Evaluating Classical AI

Exam 1

Mar 02

Exam1 Review
Partial review answers

Mar 04

No class, exam in lab

 

Mar 07

Spring Break

Mar 09

Mar 11

8

Mar 14

Intro to ML, Perceptrons

Perceptrons and Neural Networks

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

Lab 5: Backpropagation

Mar 16

Multilayer networks

Mar 18

Derivation of Backprop

9

Mar 21

Motivating ConvNets

Deep Learning

Lab 6: Deep learning

Mar 23

Deep learning

Mar 25

Understanding ConvNets

10

Mar 28

Reinforcement learning

Reinforcement Learning

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

Lab 7: Approximate Q-learning

Mar 30

Approximate Q-learning

Apr 01

Deep Q-learning example

11

Apr 04

Genetic algorithms

Genetic Algorithms

Lab 8: Genetic Algorithms

Apr 06

Schema theorem

Apr 08

Using GAs for RL

12

Apr 11

Evaluating ML

Evaluating Machine Learning

  • M: A Closer Look at Machines that Learn Mitchell Ch. 6
  • W, F: Review Machine Learning
  • Journal questions

Exam 2

Apr 13

Discussion of review topics

Apr 15

Exam2 Review

13

Apr 18

Philosophy of AI

Philosophy of AI

Final project

Apr 20

Chinese Room, Brain Replacement Experiment

Apr 22

 
14

Apr 25

Commonsense
Future of AI

Future of AI

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

Project continued

Apr 27

Lunar Lander Hyperparameters
Summing Up

Apr 29