CPSC 66: Machine Learning — Fall 2023

Our hope is to fill these slots before the semester starts; once the semester has begun, we’ll go back to the normal waitlist procedure outlined below.

For Fall 2023, please use the following waitlist procedure to stay active in the course and to be considered for open positions:

We will utilize A Course in Machine Learning by Hal Daume III as our primary text. It is an online, free ebook. We will also include additional reading material on the course schedule, most of which will also be available online for free.

Note that this book is only available in PDF form; if you want a hard copy, you should be able to get printouts made using TAP funds. Please let me know if you have any difficulties with this.

Welcome to CPSC 66. Machine learning is the study of algorithms that "learn" by finding patterns in examples provided to them. This course will introduce you to various frameworks (e.g., supervised learning) and associated algorithms for these frameworks (e.g., decision trees). The major aim of this course, however, is to develop an understanding of the entire machine learning pipeline rather than focus on the algorithm du jour. We will also spend a significant amount of time inspecting core concepts (e.g., generalization) from statistical and theoretical perspectives. With each topic, we will consider both the practical and open research questions at the heart of the field. You will be expected to implement solutions through lab assignments, but also digest and discuss readings that build off of lecture topics.

In addition to the technical side of ML, we will also be studying AI Ethics and the ways in which ML systems interact with society, with a focus on understanding the potential pitfalls of trying to generalize from real-world data. You will be expected to read, synthesize, and discuss ideas relating to ethics, data bias, and the possibility for ML systems to do harm (both through malice and through inattention).

To enroll in this course you must have completed CPSC 35. There are no other requirements, though familiarity with the basics of linear algebra, probability, discrete math, and calculus will be useful. The course will also cover a good deal of probability theory, and ethical frameworks, but much of this can be picked up from provided readings and lectures. This course is designated as a natural sciences and engineering practicum (NSEP) and qualifies as a Group 3: Applications course for the CS major/minor requirements.

By the end of the course, you will understand:

I have outlined the skills and objectives this course promises to provide you. For these promises to be upheld, you will need to commit to the policies outlined below. To succeed you should:

This is subject to change but is a rough estimate

As discussed in responsibilities, your participation involves:

Partnerships where partners work mostly independently rarely work out well and frequently fail to produce complete, correct and robust solutions. More importantly, they don’t result in both partners learning well, which is ultimately the point of doing the work in the first place. Partnerships where partners work side-by-side for all or most of the time tend to work out very well, with both partners learning (and accomplishing) more than they would alone.

You and your partner are both equally responsible for initiating scheduling times when you can meet to work together, and for making time available in your schedule for working together.

For partnered lab assignments, you should follow these guidelines:

Taking time up front to design a plan for your solution together and to doing incremental implementation and testing together may seem like it is a waste of time, but in the long run it will save you a lot of time by making it less likely that you have design or logic errors in your solution, and by having a partner to help track down bugs and to help come up with solutions to problems.

Academic honesty is required in all your work. Under no circumstances may you hand in work done with or by someone else, or produced by an algorithm, without making the source of the work explicitly clear.

Discussing ideas and approaches to problems with others on a general level is encouraged, but you should never share your solutions with anyone else nor allow others to share solutions with you. You may not examine solutions belonging to someone else, nor may you let anyone else look at or make a copy of your solutions. This includes, but is not limited to, obtaining solutions from students who previously took the course or solutions that can be found online. You may not share information about your solution in such a manner that a student could reconstruct your solution in a meaningful way (such as by dictation, providing a detailed outline, or discussing specific aspects of the solution). You may not share your solutions even after the due date of the assignment.

In your solutions, you are permitted to include material which was distributed in class, material which is found in the course textbook, and material developed by or with an assigned partner. In these cases, you should always include detailed comments indicating on which parts of the assignment you received help and what your sources were.

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.

When working on tests, exams, or similar assessments, you are not permitted to communicate with anyone about the exam during the entire examination period (even if you have already submitted your work). You are not permitted to use any resources to complete the exam other than those explicitly permitted by course policy. (For instance, you may not look at the course website during the exam unless explicitly permitted by the instructor when the exam is distributed.)

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:

This policy applies to all course work, including but not limited to code, written solutions (e.g. proofs, analyses, reports, etc.), exams, and so on. This is not meant to be an enumeration of all possible violations; students are responsible for seeking clarification if there is any doubt about the level of permissible communication.

For lab assignments, each student may use up to a total 5 late days across the entire semester, no questions asked. Late days do not apply to homework, quizzes, or other forms of assessment. A late day is a considered a full 24-hours (i.e., 15 minutes late is the same as 23 hours late) and late days are counted against all lab partners.

To use your late days, you must make a private Ed post to Instructors after you have completed the lab and pushed to your repository. You do not need to inform anyone ahead of time, and you do not need to provide a reason. When you use late days, you should still expect to work on the newly-released lab during the following lab section meeting. The professor will always prioritize answering questions related to the current lab assignment during lab meetings and office hours. In the rare case in which only one partner has unused late days, the partnership can use the late days, 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 (e.g., extended illness, concussion, hospitalization) or other emergency, you must contact the dean’s office and your instructors. Faculty will coordinate with the deans to determine and provide the appropriate accommodations. Note that for illnesses, the College’s medical excuse policy, states that you must be seen and diagnosed by the Worth Health Center if you would like them to contact your class dean with corroborating medical information.

If you believe you need accommodations for a disability or a chronic medical condition, please contact Student Disability Services via email at studentdisabilityservices@swarthmore.edu to arrange an appointment to discuss your needs. As appropriate, the office will issue students with documented disabilities or medical conditions a formal Accommodations Letter. Since accommodations require early planning and are not retroactive, please contact 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, in advance, through Student Disability Services.

To receive an accommodation for a course activity you must have an official Accommodations Letter and you need to meet with me to work out the details of your accommodation at least two weeks prior to any activity requiring accommodations.