Class: | Tues., Thurs., 1:15-2:30pm, SCI 183 |
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Lab A: | Monday 1:15 - 2:45pm, SCI 240 |
Lab B: | Monday 3:00 - 4:30pm, SCI 240 |
Communication: | Ed Discussion Platform Important announcements will be posted here You can ask questions and answer other students' questions here |
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Professor (Lecture, Lab A): | Ben Mitchell |
Email: | mitchell at cs dot swarthmore.edu |
Office: | Science Center 252 C |
Office Hours: | Thur. 2:30-4:00 pm Also by appointment, or any time my door is open |
Professor (Lab B): | Lisa Meeden |
Email: | meeden at cs dot swarthmore.edu |
Office: | Science Center 243 |
Office Hours: | Fri. 2:15-3:45 pm 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, and as a result AI has become a very broad field. AI 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 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. The first half of the semester will focus on search, and the second half of the semester will focus on machine learning.
5% | Class Participation |
5% | Reading Journal |
15% | Exam 1, in lab 03/04/24 |
15% | Exam 2, in class 04/16/24 |
30% | Labs |
30% | 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:
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 1:00pm 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:
Labs will be assigned on Monday, during the scheduled lab time, and will be due by the following Sunday 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 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 please let me know as soon as possible.
The general ethos of this policy is that actions which shortcut or avoid the learning process are forbidden, while actions which promote learning are encouraged.
For example: studying lecture materials or discussing readings together provides an additional avenue for learning and is encouraged. Using a classmate’s solution, however, is prohibited because it avoids the process of doing the work; since doing the work is how much of the learning takes place, avoiding the work inherently means avoiding the learning as well. Note that this applies to generative AI tools (e.g. chatGPT, GitHub CoPilot) just the same way it does to any other resource.
If you have any questions about what is or is not permissible, please contact your instructor.
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.
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.
Here is an example of what this might look like:
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.
WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LAB |
1 | Jan 23 | Introduction to AI
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Jan 25 | ||||
2 | Jan 30 | State space search
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Feb 01 | ||||
3 | Feb 06 | Local search
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Feb 08 | ||||
4 | Feb 13 | Game tree search
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Feb 15 | ||||
5 | Feb 20 | Monte Carlo Search
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Feb 22 | ||||
6 | Feb 27 | Evaluating Classical AI
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Feb 29 | ||||
7 | Mar 05 | Exam 1 | Perceptrons and Neural Networks
| Exam 1 in lab |
Mar 07 | ||||
Mar 12 | Spring Break | |||
Mar 14 | ||||
8 | Mar 19 | Deep Learning
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Mar 21 | ||||
9 | Mar 26 | Reinforcement Learning
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Mar 28 | ||||
10 | Apr 02 | Genetic Algorithms
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Apr 04 | ||||
11 | Apr 09 | Evaluating Machine Learning
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Apr 11 | ||||
12 | Apr 16 | Exam 2 | Exam 2 in class | |
Apr 18 | Natural Language Processing | |||
13 | Apr 23 | Transformers & LLMs | Project checkpoint | |
Apr 25 | ||||
14 | Apr 30 | Future of AI, wrap up
| Project continued | |
May 02 |