Mon, 2:00-3:00, Evans 357;
Thu, 2:00-3:00, Soda 551.
Lectures: Soda 310. Tuesday/Thursday 12:30-2:00.
Course description
This course will provide an introduction to the design and
theoretical analysis of prediction methods, focusing on statistical
and computational aspects. It will cover
methods such as kernel methods and boosting algorithms, and
probabilistic and game theoretic formulations of prediction
problems. We will examine questions about the guarantees
we can prove about the performance of learning algorithms
and the inherent difficulty of learning problems.
Prerequisites: CS281A/Stat241A, or advanced training in
probability or statistics, at the level of Stat 205A or
Stat 210A.
[More details]
Assignments
The grade will be based 50% on homework, 40% on the final project, and
10% on lecture notes.
There will be roughly five homework assignments, approximately one
every two weeks. Late homeworks will not be accepted. You are welcome
to discuss homeworks with other students, but please work out and
write up the solutions completely on your own, and specify in your
solutions who you've discussed which problems with.
Some of the problems have appeared in the literature.
Please attempt them yourself, and if you need help,
ask the instructor or GSI for assistance, rather than
searching for someone else's solution.
If you happen to have seen a problem before, please write up a
solution on your own (and indicate that you've seen it before
- it would also be helpful to point out where).
You will need to act as scribe for
a small number of lectures, preparing a latex version of lecture
notes (including figures if appropriate)
and emailing it to the GSI within two weekdays of the
lecture. These notes will be posted to the web site.
Please use this latex template
in preparing your lecture notes. Also, see the latex
file of the notes for lecture 1.
There will be a final project. This can be in any area related to the
topics of the course. You might implement an algorithm,
run experiments on an algorithm for a particular application, try to
extend an existing method or theoretical result, or do a combination
of these. You will need to submit a brief written report and give a
presentation in class in the last week of semester (a poster
presentation or a talk, depending on the class size).
It is OK to work on projects in groups of two (please email me an
explanation if there's a good reason to work in a larger group). In
all cases you will need to write the report individually.
Project proposals are due on March 31 (please send one or two plain
text paragraphs in an email message to bartlett at cs).
Project reports are due on May 6.
2. Due Thursday, Feb 14, at the lecture.
pdfsolutions
3. Due Thursday, Feb 28, at the lecture.
pdfsolutions
4. Due Thursday, March 20, at the lecture.
pdfsolutions
5. Due Tuesday, April 22, at the lecture.
(New version posted Wednesday April 16 correcting Q2a.)
pdfsolutions
Announcements
Wed, Apr 30:
Project reports are due on Tuesday, May 6, at 5pm in the
box outside Soda 723.
Although there is no set page limit,
aim to produce a brief report of less than 10 pages.
We will have project poster presentations on Tuesday, May 6 and
Thursday, May 8, in the usual lecture time slot and location.
If you are enrolled in CS281B, you'll be presenting your poster
on Tuesday, May 6. If you are enrolled in Stat241B,
you'll be presenting your poster on
Thursday, May 8.
If you did a project in a group, you can choose to
present your poster on the appropriate day for any of the group
members.
When you are preparing your poster, the target audience should be
other students in the class. Be ready to stand by your poster and
give a brief overview of your project.
Please attend both poster sessions.
Tue, Apr 29:
David's office hours this week have been moved from Thursday 2-3:30 to
Friday 10:30-12 in Evans 357.
Wed, Apr 16:
Another revised version of Homework 5
has been posted, again correcting question 2(a).
It is now due on Tuesday, Apr 22, at the lecture.
Final project reports are due on May 6. Poster presentations of the
projects will be held in the lecture time slots on May 6 (for students
enrolled in CS281B) and May 8 (for students enrolled in Stat241B).
Tue, Apr 15:
Concerning question 4 on homework 5, the parameter eta must be positive.
As we have seen in lectures, regret bounds of the form (1) arise
for positive values of eta.
Mon, Apr 14:
A revised version of Homework 5
has been posted, correcting a typo in question 2(a) (a missing
positivity constraint). Notice also that the notation '-G' denotes the
set {-g s.t. g in G}, as in lectures.