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.
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
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.
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
- 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
- Progress Report due May 10: 1-2 page
- 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