CPSC 66: Machine Learning — Spring 2021

For Spring 2021, 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. Online, free ebook. We will also include additional reading material on the course schedule.

Welcome to CPSC 66. Machine learning is the study of algorithms that learn through experience. This course will introduce you to various frameworks (e.g., supervised learning) and associated algorithms for these frameworks (e.g., support vector machines). 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.

To enroll in this course you must have completed CPSC 35. There is no other requirements, though linear algebra and familiarity with probability will be useful. The course will also cover a good deal of probability theory, but much of this can be picked up with provided reading. 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:

we have outlined the skills and objects this course promises to provide you. For this promises to be upheld, you will need to commit towards 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:

For partnered lab assignments, you should follow these guidelines:

Taking time 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.

Partnerships where partners work mostly independently rarely work out well and rarely result in complete, correct and robust solutions. Partnerships where partners work side-by-side for all or most of the time tend to work out very well.

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.

All homework and lab deadlines will have a 24 hour grace period. This means that assignments submitted less than 24 hours late will be accepted and graded normally. In addition, for lab assignments, each student may use up to a total 7 late days across the entire semester, no questions asked. This is more than a normal semester due to added difficulties of working remotely this semester. 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. The grace period does not count against late days; as an example, if an assignment is due at 1pm on Tuesday and you submit it at 9am on Wednesday (<24 hours), you do not need to use a late day, but if you submit it at 7pm on Wednesday (>24 hours, <48 hours), you would use one late day.

To use your late days, you must email your professor 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 or a grace period, 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 us 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 us to work out the details of your accommodation at least two weeks prior to any activity requiring accommodations.

Academic honesty is required in all your work. Under no circumstances may you hand in work done with or by someone else under your own name. 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.

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.

The general ethos of this policy is that actions which shortcut the learning process are forbidden while actions which promote learning are encouraged. Studying lecture materials together, for example, provides an additional avenue for learning and is encouraged. Using a classmate’s solution, however, is prohibited because it avoids the learning process entirely. If you have any questions about what is or is not permissible, please contact your instructor.

Regret clause for Spring 2021: If you commit some act that is a violation of the integrity policy (or, if you are unsure if it violates the policy) but bring it to the attention of the course’s instructor within 48 hours, the course may impose local sanctions that may include an unsatisfactory or failing grade for work submitted, but the course will not refer the matter for further disciplinary action except in cases of repeated acts.