CS 91.3: Machine Learning and Brain-Computer Interfaces - Spring 2022

Welcome to CS91.3. This is a research methodology course that focuses on developing research skills in Computer Science (CS) & Brain-Computer Interfaces (BCI). This course will introduce machine learning and deep learning algorithms and their implementation in CS and BCI interdisciplinary research. We investigate the empirical research methods for their applicability and suitability to a research problem. This course will focus on a subset of topics including: classification, clustering, dimensionality reduction, transfer learning, regression, and time series analysis. This is a project-oriented course intended to walk through the steps needed to conduct publishable research as an undergraduate researcher. The related research methods and frameworks will be demonstrated as research projects targeting CS conferences.

Please be aware that many elements on the course website will change throughout the semester, including the course schedule. It is your responsibility to review the course website periodically for updates.

We value any and all student feedback. Please be constructive in any comments so that we can adjust the course as best possible. This semester, we are using EdSTEM to manage course discussions and announcements.

Meeting Times:

By the end of the course, we hope that you will have developed the following skills:

Grades will be weighted as follows:

This course features weekly lab assignments which are the largest component of your course grade. Lab attendance is required by all students. You must attend the lab session for which you are enrolled.

Lab assignments will typically be released on Wednesday and will be due by midnight on the following Tuesday. You are strongly encouraged to start early and to attend the office hours for possible questions.

You will submit your assignments electronically using emails. You may submit your assignment multiple times, and a history of previous submissions will be saved. You are encouraged to submit your work regularly.

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 quizzes, 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.

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 Services website.

To receive an accommodation for a course activity, you must have an official accommodation letter from the Office of Student Disability Services and you need to meet with course staff to work out the details of your accommodation at least two weeks prior to the activity.

You are also welcome to contact any of the course staff privately to discuss your academic needs. However, all disability-related accommodations must be arranged, in advance, through Student Disability Services.

Programming is not a dry mechanical process, but a form of art. Well written code has an aesthetic appeal while poor form can make other programmers and instructors cringe. Programming assignments will be graded based on style and correctness. Good programming practices usually include many of the following principles:

This semester, we are using EdSTEM to manage course discussions and announcements.