Advanced Machine Learning
STAT 538 Winter Quarter 2015

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UW Statistics

Announcements
  • The FINAL exam will be Savery 168 10:30--12:20
  • Previous exams and solutions are here

What will the course be about?

  • Optimization and convexity in ML, relationship with regularization, compressed sensing, [submodularity]
  • Exponential family models and concepts of information theory
  • Learning with structured data: mainly graphs and networks, but possibly orderings, structured prediction, and related combinatorial-continuous learning tasks.
  • [Graphical models and variational inference (if there is time and interest)]
These topics are related by a few underlying major concepts: convexity, sparsity, conditional independence. These concepts permeate most of the automated learning tasks of today. The class will highlight the intimate connection between statistics and computation (meaning algorithms, data structures, and optimization) in modeling large or high-dimensional data. The time and weight of the above topics receive will be decided with input from the students taking the class.

Who is this class for?
This class is a core class in the Machine Learning/Big Data PhD Track in Statistics. For any Statistics student who wants to learn Machine Learning/Big Data and has taken STAT 535 or CSE 546.
STAT 538 class is part of the Machine Learning/Big Data PhD track. For a bigger picture of the ML/BD classes offered at UW, see this page. Capacity permitting, the class is open to other graduate students with an interest in statistics, algorithms and computing.

Textbook No required textbook.

The grade is based (approximately) on homework + quizzes (%), miniproject or midterm (%), final exam (%) and class participation (%)., with the weights to be decided shortly. The homework will contain both problems and implementation assignements. The project will consist of implementation, write-up and short oral presentation. The final exam will be in class, at the date fixed by the university, no electronics, 6 pages of notes allowed.

Prerequisites

  • STAT 535/CSE 546 Foundations of Machine Learning
  • Medium ability with a computer programming language (like C, C++, Java or Matlab, Splus, R) at the level of STAT 534

Instructor: Marina Meila   mmp at stat dot washington dot edu

TA Amit Meir amitmeir at the same domain as mmp

Lectures: Tuesdays,10:30 - 11:50 & Thursdays 11:30:12:50 in Savery 155

Office hours: Monday 2-3pm in PDL B-321

TA Office hours: Thursday 15:30-16:30 PDL B-302B

Recitation: TBD

Course home page: http://www.stat.washington.edu/courses/stat538/winter15 (this page)

Class mailing list: stat538a_wi15