CS2750: Machine Learning (Fall 2026)

Course Description

This PhD-level course on machine learning provides an in-depth exploration of theoretical foundations and algorithmic innovations of machine learning. The course emphasizes mathematical rigor, algorithmic efficiency, and the ability to critically evaluate research in the field. Machine learning as a subject has evolved dramatically over the past decades. Topics covered by this class include supervised learning, deep learning, reinforcement learning, generative AI, and their applications to real-world social impact problems.

Prerequisites

Formal prerequisites include linear algebra, probability, algorithms, and proficiency in at least one programming language. A high level of mathematical maturity will be helpful. Please see the instructor if you are unsure whether your background is suitable for the course.

Textbooks

This course does not exactly follow any textbook. Most lectures will have some optional reading to help you better understand the material or see a different presentation/perspective. Most of the time, we are discussing new topics that have not been written into any mature textbooks yet. We will assign research papers as we go.

Course Schedule (Subject to Change)

Paper Presentation

You will be asked to present 1 paper in class throughout the semester.

You will want to understand the technical details. You will also want to critically examine the paper and talk about its strengths and weaknesses. It might help to situate this paper in the literature by finding and reporting on at least one older paper that substantially influenced the current paper and at least one newer paper that is influenced by the current paper. It would also be helpful to propose a couple of follow-up directions that were not mentioned by the paper’s authors.

That said, don't feel you need to cover every aspect mentioned above, or every detail of the paper in the presentation. The length of each presentation is expected to be around 8-10 minutes. We prefer depth over breadth. It's better to discuss one or two key ideas in the paper and explain them well, than to try to cover everything superficially.

You are required to meet with the instructor to discuss the paper and the presentation, no later than 3 days before your presentation. You may use the designated office hour or email the instructor for additional times.

Course Project

You will work in groups of 2-3 people on an original research project related to machine learning. The progress of projects will be checked through the Project Proposal, Project Progress Report, Project Presentation, and Final Project Report. The proposal and progress reports will be peer-reviewed and graded by the instructor. The presentation and the final report will be evaluated by the instructor directly. The grading of final project report will be benchmarked against the level acceptable to a competitive workshop at a major AI/ML conference. You are encouraged to align the course project with your own research agenda.

Proposal due: September 18th
Progress report due: October 23th
Oral presentations: December 7th
Final report due: December 8th

Quizzes

There will be two short in-class and closed-book quizzes. The quizzes will be relatively light, but provide a good opportunity to assess your understanding of the material.

Grading

Course Policies

Grading

• Late-submission policy: No grace days are given. The instructor reserves the right not to grade late assignments, except for the following situations:
  • Medical Emergencies: If you are sick and unable to complete an assignment or attend class, please go to Student Health Services.
  • Family/Personal Emergencies: If you have a family emergency (e.g. death in the family) or a personal emergency (e.g. mental health crisis), please contact your academic adviser and/or University Counseling Center.
  • University-Approved Travel: If you are traveling out-of-town to a university approved event or an academic conference, you may request an extension for any time lost due to traveling. For university approved absences, you must provide confirmation of attendance, usually from a faculty or staff organizer of the event or via travel/conference receipts.
  For any of the above situations, you may request an extension by emailing me. The email should be sent as soon as you are aware of the conflict, and at least 3 days prior to the deadline. Extension requests received after this date will not be considered. In case of an emergency, no advanced request is needed. This is reserved for truly emergent situations.
• Re-grading policy: To request a re-grade, please write an email to the instructor titled “Re-grade request from [Student's Full Name]” within one week of receiving the graded assignment.

Collaboration

• For the course project, you may collaborate with others outside the class (including students, faculty members, and domain experts) with approval from the instructor. You are still expected to take the leading role in the project. If you have external collaborators, you need to give proper credits to all parties involved, and report the contributions of each contributor in the proposal, progress report, final report, and presentations, which will be considered in the grading.

Academic Integrity

• You may use AI to help with ideation, debugging, and code generation. However, you must clearly document any use of LLM in your write-ups. You should verify the correctness and appropriateness of LLM-generated content. You are ultimately responsible for the work you submit.
• Plagiarism in any submitted assignments is strictly prohibited. There are serious consequences. All academic integrity violations are reported to the university. Please consult the University Guidelines on Academic Integrity.
  • First violation: 0% on the assignment.
  • Second violation: Failure in the course and possible disciplinary actions from the university.

Accommodations for Students with Disabilities

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Statement on Student Wellness

As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. Pitt services are available, and participation in services does work. You can learn more about confidential mental health services available on campus here. Support is always available (24/7) from University Counseling Center: 412-648-7930.