Course Overview

This doctoral-level seminar will introduce the statistical and algorithmic principles in the field of scalable machine learning. The course will consist of instructor-led lectures and student-led presentations on current research topics.

Prerequisites

Students are expected to have a strong background in machine learning at the graduate level (CS260 or equivalent), and also have a solid background in statistics, optimization, and linear algebra.

Piazza Forum

We will use Piazza for class discussions. Please go to this Piazza website to join the course forum (note: you must use a ucla.edu email account to join the forum). Students are strongly encouraged to post on this forum rather than emailing Prof. Talwalkar directly. Students should use Piazza to:

• Post reading material for classmates for student-led lectures.
• Ask clarifying questions about the course material.
• Share useful resources with classmates.
• Answer questions posted by other students to solidify your own understanding of the material.

Also, please be polite.

Grading Policy

Grades will be based on the following components:

• Presentation (35%)
  • Each student is required to individually prepare and present a lecture on a current research topic in the area of scalable machine learning. This will involve reading several research papers and distilling the core ideas into a 1.5 hour technical presentation.
  • The presenting student must send presentation slides to Prof. Talwalkar for review at least one week before the scheduled presentation.
  • The presenting student must select a short reading assignment for the class, and send it to Prof. Talwalkar at least one week before the scheduled presentation.
• Participation (15%)
  • Students are expected to attend all classes, complete all course readings prior to class, and actively participate in class discussions.
• Project (50%)
  • Students can work alone or in groups of 2.
  • Deliverables include:
    • Initial Proposal due April 21: 2 page writeup along with a short in-class presentation.
    • Progress Report due May 10: 1-2 page writeup.
    • Final Report due June 10: 5 page writeup along with an in-class presentation during the last week of class.
  • It is expected that several class projects will form the basis of future publications. Projects will thus be evaluated as if they are submissions to a technical workshop/conference.

Tentative Topics and Schedule

See this google doc.

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