Course Information

Schedule

Class:	Tues., Thurs., 11:20-12:35pm, SCI 158
Lab A:	Wednesday 1:05 - 2:35pm, Martin 113

Contact Information

Communication:	Ed Discussion Platform Important announcements will be posted here You can ask questions and answer other students' questions here
Professor (Lecture, Lab A):	Ben Mitchell
Email:	mitchell at cs dot swarthmore.edu
Office:	Martin 334
Office Hours:	TBA Also by appointment, or 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, and as a result AI has become a very broad field.

In recent years, the term "AI" has become associated very strongly with large-language models used for text generation (e.g. ChatGPT, Gemini, Claude, etc.). While we will discuss these models, the course will not be entirely centered on them. Our understanding of "AI" will be that AI is a field of study which has existed for many decades, and includes search, game playing, reasoning, planning, natural language processing, computer vision, 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 programs that define their behavior in terms of previous examples, we will explore reinforcement learning and neural networks, including the popular sub-field of deep learning, of which LLMs are a sub-sub-field. 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

• Apply a variety of search techniques to AI problems.
• Apply a variety of machine learning techniques to AI problems.
• Compare and contrast the strengths and weaknesses of various AI approaches.
• Given a particular AI problem, determine an appropriate representation and approach for solving it.
• Understand the ethical implications and dangers of AI.

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Grading

Grade weighting

5%	Class Participation
5%	Reading Journal
15%	Exam 1, TODO-in lab 03/05/25
15%	Exam 2, TODO-in class 04/15/25
30%	Labs
30%	Final Project

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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 listed below by Melanie Mitchell, which is required.

• Artificial Intelligence: A Guide for Thinking Humans, by Melanie Mitchell
• Artificial Intelligence: A Modern Approach (3rd Edition), by Stuart Russell and Peter Norvig
• Note: there is a 4th Edition of this book, which is very expensive and isn't actually any better for the parts we're using. If you happen to already have a copy of the 4th edition you can use it, but don't go out of your way to buy one.
• Artificial Intelligence: Foundations of Computational Agents (2nd Edition), by David Poole and Alan Mackworth

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 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:

• Overview: You should quickly skim through the reading once to get an overview of the material to be covered, paying particular attention to subject headings and topic introductions. This should be very quick.
  (Estimated time: 5 minutes)
• In-Depth: Next, read the material closely. Try to understand what individual steps of algorithms are accomplishing. Not everything will make sense at this point, but hopefully many things will.
  (Estimated time: 40 minutes)
• Final Notes: After carefully reading the material, mentally review and try making a few notes to yourself about what you think are the most important concepts being covered, as well as any questions that you have.
  (Estimated time: 5 minutes)

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 10:00pm 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:

• PLUS: Exhibits exceptional clarity, insight, and/or creativity
• CHECK: Exhibits evidence of processing and studying concepts
• MINUS: Superficial response or insufficient evidence of engagement
• ZERO: Did not complete

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

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Policies

Labs

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

You will work with a partner on all labs after lab 0. 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 three 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 or private message on Ed 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 or other extenuating circumstance, 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. Regardless of the source, you should always include comments that indicate on which parts of the assignment you received help, and what your sources were. You may not share your solutions even after the due date of the assignment.

Any code, text, or content not created exclusively by you and used without attribution is plagiarism. This is true regardless of whether the original source was a scholarly text, another student, an online platform (e.g. StackOverflow), or a generative model (e.g. ChatGPT). Using resources such as these may be appropriate under some circumstances, and not under others, but regardless you must always properly acknowledge and cite the source of the information. When in doubt, add a statement of attribution! In addition, when using any type of generative AI, you must also describe how it was used, e.g. by giving the prompt.

The use of generative AI tools (e.g. chatGPT, GitHub CoPilot, etc.) without permission is also considered to be unauthorized collaboration with an outside source and is a violation of our academic integrity policy. If you feel these tools would be appropriate in a given context, feel free to ask the instructor. Note that even if permission is granted, these sources must be properly attributed.

Here are some examples of what this might look like:

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.

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Schedule

WEEK	DAY	ANNOUNCEMENTS	TOPIC & READING	LAB
1	Sep 02		Introduction to AI Roots of Artificial Intelligence Mitchell Ch. 1 Journal questions	Lab 0: Python Refresher/Warmup
	Sep 04
2	Sep 09		State space search Problem solving R&N 3.1-3.3 Uninformed search R&N 3.4 Informed search R&N 3.5-3.7 Journal questions	Lab 1: Robot Maze
	Sep 11
3	Sep 16		Local search Hill climbing, Simulated annealing, and Beam search R&N 4.1 Journal questions	Lab 2: A* search
	Sep 18
4	Sep 23		Game tree search Minimax Alpha-beta pruning P&M 11.3 Journal questions	Lab 3: Local search
	Sep 25
5	Sep 30		Monte Carlo Search The Grand Challenge of Go Mitchell Ch. 9 (pgs. 152-164) MCTS Journal questions	Lab 4: Game play with Minimax
	Oct 02
6	Oct 07		Evaluating Classical AI The Fundamental Problems of Classical AI and Cognitive Science P&S Ch. 3 Journal questions	Lab 5: MCTS
	Oct 09
	Oct 14	Fall Break
	Oct 16
7	Oct 21		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	Exam 1 in lab
	Oct 23
8	Oct 28		Deep Learning Who, What, When, Where, Why Mitchell Ch. 4 ConvNets and ImageNet Mitchell Ch. 5 Convolutional Neural Networks for Visual Recognition Journal questions	Lab 6: Neural Networks
	Oct 30
9	Nov 04		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: Convolutional Networks
	Nov 06
10	Nov 11		Genetic Algorithms Genetic algorithms Mitchell GA Ch. 1 Evolving Neural Networks Mitchell GA Sec. 2.3 Journal questions	Lab 8: Reinforcement Learning
	Nov 13
11	Nov 18		Evaluating Machine Learning A Closer Look at Machines that Learn Mitchell Ch. 6 Journal questions	Lab 9: Genetic Algorithms
	Nov 20
12	Nov 25		Exam 2 in class	Final Project
	Nov 27		Natural Language Processing
13	Dec 02		Transformers & LLMs Financial Times on Generative AI Journal questions	Project checkpoint
	Dec 04
14	Dec 09		Future of AI, wrap up Knowledge, Abstraction, and Analogy in AI Mitchell Ch. 15 Questions, Answers, and Speculations Mitchell Ch. 16 Journal questions	Project continued