Policies and Requirements

Assignments: Assignments are in two hands-on forms: ‘‘Theory to practice’’ and ‘‘method’’ assignments. The former bridges the gap between theory and practice, while the latter improves students’ independent problem-solving skills.

Theory to practice assignments: Students are expected to implement (program) and return a concise 1-page report of a machine learning model described in a textbook or lecture notes. The report should include the following sections: method (5%): a brief description (3–5 sentences) of the essential components of the machine leaning model; implementation and insights (80%): a discussion on specific choices made during your implementation process, including data structures, training and evaluation techniques, and any modifications or deviations from the original model and its effect, as well as insights obtained after analyzing the results; and limitations (15%): a list of specific points about the limitations of the model. Code will be graded in terms of correctness of implementation and experiments for insights. Students will individually complete these assignments.

Method assignments: Students are expected to develop a machine learning model of their choice to address a given problem. Evaluation will be based on code correctness and model performance on unseen test data. Students will individually complete these assignments.

Final project: Students are expected to identify a machine learning problem of their choice, and collaborate on a shared project. The project includes data preprocessing as necessary; implementation of a suitable computational model to address the proposed problem; and thorough evaluation of the model, providing metrics, visualizations, and insights. Students will compile their findings, methodologies, and insights into a three-page report. The project includes proposal submission, class discussion, final report submission, and a final presentation during the course's closing week. Students are required to form a group of five (5) individuals enrolled in this course to conduct their projects. (Rationale: if you have a project idea and you're not able to convince another student to work on it, the idea is probably not quite interesting, try to join another group.) Templates for the project proposal, final report, and presentation slides, including formatted sections in MS Word, Overleaf or Google Slides, will be provided.

Extra credit: Extra credit opportunities are available for students who demonstrate exceptional engagement. Options include: active participation in class discussions; exceeding expectations in assignments, such as conducting additional analyses that yield insightful observations about the problem; or attending and reporting on local research talks. Examples of relevant talks can be found here. For research talk reports, please submit a 1-page summary within two weeks of the event. Your report should clearly outline: the research problems addressed, contributions and methodology of the research, and results and significant insights. Excellent reports are those that draw connections to materials discussed in class.

Grading: We do our best to return grades within three weeks of the due dates. Students have a right to question the grading within three days of the return of the preliminary grades.

Lateness: Timely submission of assignments, project proposals, and final reports is required. Late submissions will be penalized as follows:

• Submissions made within three days of the deadline will incur a 30% reduction from the total score.

• Submissions made after the three-day grace period will receive a zero mark. Please note that late submissions of slides for project presentations are not accepted and will result in a zero mark for the project component.

Attendance: Students are expected to regularly attend classes and actively participate in class discussions. However, I recognize that emergencies may arise, leading to unavoidable absences. In such cases, students are not required to email instructors or disclose their specific circumstances to them. Nonetheless, it's important to remember that regular attendance is a key factor to effective learning and academic success.

Emergencies: In the event of unforeseen circumstances such as severe weather conditions or other emergencies that lead to a university closure and disrupt our regular class schedule, a recorded lecture covering the planned materials will be provided on Blackboard. In addition, I will be available during my regular office hours (see page 1) to answer student questions. If you are unable to meet during these hours, please feel free to schedule a meeting with me via email.

Academic Integrity Policy: Students are responsible for the honest completion and representation of their work. The university's policy on academic integrity is available here, and can be read in about 5 minutes.

Academic accommodation: Eligible students should provide a letter of accommodation from the Office of Disability Services within the first three (3) weeks of the semester. They can be reached at 978-934-4574 or disability@uml.edu.

Religious observance: Students observing religious holidays that conflict with course due dates are requested to inform the instructor by the end of the first week of classes. I accommodate students of all faiths on an individual basis by ensuring that no assignment or exam is scheduled on recognized religious holidays.

Anti-Harassment (Adapted from ACL Anti-Harassment policy): The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the aims and goals of this course. Harassment and hostile behavior in any form (including but not limited to harassment based on race, gender, religion, age, appearance, national origin, ancestry, disability, sexual orientation, gender identity, etc.) is prohibited in this course.