Foundations of Machine Learning
STAT 535 Autumn Quarter 2015

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UW Statistics

Announcements
  • Final exam Monday December 14, 10:30-12:10 *** ends 10 minutes earlier!, in a location

What will the course be about?
The class will teach the basic principles of Machine Learning, and in particular will highlight the intimate connection between statistics and computation (meaning algorithms, data structures, and optimization) in modeling large or high-dimensional data. Solutions that are algorithmically elegant, often end up being also statistically sound, and sometimes when the model estimation program runs fast, we find that the model fits the data well. These principles will be illustrated during the study of a variety of models, problems and methods. See also the syllabus.

Who is this class for?
This class is a core class in the Machine Learning/Big Data PhD Track in Statistics. For any Statistics PhD student who wants to learn Machine Learning/Big Data, this class is the fist in the triplet of graduate courses 535 --> 538/548 and serves as a prerequisite to
STAT 538 Advanced Machine Learning (taught in Winter), and
STAT 548 Machine Learning for Big Data
For the Statistics MS Students in the Statistical Learning Track, this class is the third in the sequence 534,527,535 that leads to completion of this certificate.
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, in particular to students involved in Machine Learning research across campus.

Optional Textbook "Machine Learning: A Probabilistic Perspective" by K. Murphy The grade is based (approximately) on homework + quizzes (55-60%), miniproject (10-15%), final exam (25-30%) and class participation (5%). The homework will contain both problems and implementation assignements. The project will consist of implementation, write-up and poster presentation. The final exam will be in class, at the date fixed by the university, no electronics, 6 pages of notes allowed.

Prerequisites

  • A course in probability, including basic of multivariate analysis (conditional probability, independence, marginals, expectation, variance in multivariate seeting)
  • Fundamentals of statistics: Maximum Likelihood Estimation, MAP estimation, priors, likelihood, estimating parameters of usual distributions (normal, multinomial), Bayes' formula
  • Calculus and linear algebra: partial derivatives, gradient, the chain rule, vectors and matrices, matrix multiplications, eigenvalues and eigenvectors, positive definite matrices
  • Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation).
  • Medium (beyond beginner) ability with a computer programming language (like C, C++, Java or Matlab, Splus, R) at the level of STAT 534

    Statistics students, taking STAT 534 is a good way to get up to speed in algorithms and programming.

Instructor: Marina Meila   mmp at stat dot washington dot edu

Lectures: Tuesdays,10:30 - 11:50, & Thursdays 11:30:12:50 in Low XXX

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

Course home page: http://www.stat.washington.edu/courses/stat535/fall14 (this page)

Class mailing list: stat535a_au15 at UW