COMS 4772 Advanced Machine Learning (Fall 2015)

Time & venue
Monday and Wednesday 1:10–2:25 PM in 414 CEPSR
Instructor
Daniel Hsu
Course e-mail
coms####@gmail.com (replace #### with course number)
Office hours
Wednesday 2:30 PM - 4:30 PM in 702 CEPSR
TA office hours
[TBD]
Announcements
June 16
There will be a calibration quiz during the first lecture on which admittance into the course will be based.
At present, registration may only be open to CS students. It will be open to non-CS students closer to the start of the semester.
Schedule
Course information and policies
Topic
Selected topics in machine learning theory
Prerequisites
Machine learning (at the level of COMS W4771/STAT 4400)
Algorithms and data structures (at the level of CSOR W4231)
Linear algebra (e.g., orthogonal subspaces, eigenvalue decompositions)
Multivariable calculus (e.g., convergent sequences, gradients, multiple integrals)
Probability and statistics (e.g., random variables, independence, confidence intervals)
General mathematical maturity
Course work
(Note: this is subject to change)
Homework assignments (50%)
Project (40%)
Class participation (10%)
Homework write-ups
Your write-up must be neatly typeset as a PDF document. You can use LaTeX (see intro, short math guide, wikibook for tips) or any other system that produces typesetting of equal quality and legibility (especially for mathematical formulas).
If you use LaTeX, please use the following template: homework.tex, homework.cls, homework.pdf. If you do not use LaTeX, please use an equivalent template. In particular, ensure that your name, your UNI, and the UNI's of students you discussed the homework with appear on the first page.
Submit your write-up as a single PDF file on Courseworks by 1:00 PM of the specified due date. Late assignments will not be accepted.
Policies on collaboration, outside references, and academic honesty will be strictly enforced.