In this course we will introduce classical methods in pattern classification and machine learning, focusing on statistical learning approaches for supervised and unsupervised learning problems. We will present the theoretical and algorithmic underpinnings of these methods. The course will consist of biweekly lectures, problem sets that contain both mathemetical and MATLAB/Octave programming exercises, and two in-class exams.
Undergraduate level training or coursework in algorithms, linear algebra, calculus and multivariate calculus, basic probability and statistics; an undergraduate level course in Artificial Intelligence may be helpful but is not required. A background in programming will also be necessary for the problem sets; specifically students are expected to be familiar with MATLAB/Octave or learn it during the course.
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). We strongly encourage students to post on this forum rather than emailing the course staff directly (this will be more efficient for both students and staff). Students should use Piazza to:
Instructor: Prof. Ameet Talwalkar
Office Hours: Monday after class, Boelter 4531F
Email: ameet at cs.ucla.edu
Grades will be based on the following components:
All registered students for this course can obtain MATLAB access via their SEASnet accounts. This applies to both Engineering and non-Engineering students), though students who are not officially enrolled (per the Registrar's Office) will not be able to get a SEAS account. SEASnet accounts can be applied for by going to this link, then clicking on the "Create Account" tab in the upper left hand corner. Student accounts must be picked up in person at the SEASnet Help Desk. Remote Desktop is also available to all enrolled students in Engineering. If you need assistance please contact email@example.com.
Octave is freely available.
Students are strongly encouraged to use LaTeX. LaTeX makes it simple to typeset mathematical equations, and is extremely useful for grad students to know. Most of the academic papers you read were written with LaTeX, and probably most of the textbooks too. Here is an excellent LaTeX tutorial and here are instructions for installing LaTeX on your machine. Also note that LaTeX is also installed on department-run linux machines.
This course is based in part on material developed by Fei Sha. Some of the administrative content on the course website is adapted from material from Jenn Wortman Vaughan, Rich Korf, and Alexander Sherstov.
|1/9||Course Overview, Math Review||[MLAPA] 1.1-1.3, 2||HW1 released (supplementary files)|
|1/11||Nearest Neighbors||[MLAPA] 1.4.1-1.4.3;
|1/16||No Class -- MLK Day|
|1/18||[MLAPA] 16.2; [ESL] 9.2||HW1 due|
|1/23||[MLAPA] 3.5; [ESL] 6.6.3||HW2 released (supplementary file)|
|1/25||[MLAPA] 1.4.6, 8.1-8.4;
[ESL] 4.1, 4.2, 4.4
Gaussian and Linear Discriminant Analysis, Multiclass Classification
|2/1||[MLAPA] 8.5, 1.4.5, 7.1-7.3, 7.5.1, 7.5.2, 7.5.4,7.6;
HW3 released (supplementary file)
|2/6||[MLAPA] 1.4.7, 1.4.8;
[ESL] 7.1-7.3, 7.10
Linear Algebra Review
|2/13||[MLAPA] 14.1, 14.2, 14.4; |
[ESL] 5.8, 6.3, 6.7
|2/15||In-class Midterm||HW4 released|
|2/20||No Class -- Presidents' Day|
|2/27||HW4 due; HW5 released (supplementary file)|
|3/6||[MLAPA] 16.5.1-6, 28;
|HW5 due; HW6 released (supplemental files)|
|3/8||[MLAPA] 11.1-11.5; |
|3/13||<!EM algorithm--> No Class (Prof. Talwalkar will hold office hours in BH 4531F)||[MLAPA] 12.2; [ESL] 14.5.1|