| Class: | Monday, Wednesday, 10:30-11:45pm, SCI 204 |
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| Lab A: | Friday 2:00 - 3:30pm, Martin 227 |
| Lab B: | Friday 3:450 - 5:15pm, Martin 227 |
| 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: | Lisa Meeden |
| Email: | meeden at cs dot swarthmore.edu |
| Office: | Martin 208 |
| Office Hours: | Wednesdays 1:00-3:00pm Also by appointment, or 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.
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.
| 5% | Class Participation |
| 20% | Exam 1: In lab Feb. 27 |
| 20% | Exam 2: In class Apr. 13 |
| 30% | Labs |
| 25% | 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 reading questions.
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 reading questions to consider 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. Be ready to discuss your answers in class!
Labs will be available on Fridays at noon, 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 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 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 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.
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, etc.) 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. 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:
If you believe you need accommodations for a disability or a chronic medical condition, please visit the Student Disability Services website for details about the accommodations process. Since accommodations require early planning and are not retroactive, contact Student Disability Services as soon as possible. You are also welcome to contact me privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.
| WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LAB |
| 1 | Jan 19 | No class on MLK day | Introduction to AI
| Lab 1: Python Refresher/Warmup |
Jan 21 | ||||
| 2 | Jan 26 | State space search
| Lab 2: Robot Maze | |
Jan 28 | ||||
| 3 | Feb 02 | Local search
| Lab 3: Informed search | |
Feb 04 | ||||
| 4 | Feb 09 | Game tree search
| Lab 4: Local search | |
Feb 11 | ||||
| 5 | Feb 16 | Monte Carlo Search
| Lab 5: Game play with Minimax | |
Feb 18 | ||||
| 6 | Feb 23 | Evaluating Classical AI
| Exam 1 in lab | |
Feb 25 | ||||
| 7 | Mar 02 | Perceptrons and Neural Networks
| Lab 6: MCTS | |
Mar 04 | ||||
Mar 09 | Spring Break | |||
Mar 11 | ||||
| 8 | Mar 16 | Deep Learning
| Lab 7: Neural Networks | |
Mar 18 | ||||
| 9 | Mar 23 | Reinforcement Learning
| Lab 8: Convolutional Networks | |
Mar 25 | ||||
| 10 | Mar 30 | Genetic Algorithms
| Lab 9: Reinforcement Learning | |
Apr 01 | ||||
| 11 | Apr 06 | Evaluating Machine Learning
| Lab 10: Genetic Algorithms | |
Apr 08 | ||||
| 12 | Apr 13 | Exam 2 in class | Large Langue Models | Final Project |
Apr 15 | ||||
| 13 | Apr 20 | Transformers | Project checkpoint | |
Apr 22 | ||||
| 14 | Apr 27 | Future of AI, wrap up
| Project continued | |
Apr 29 | ||||