• Link to Canvas course site
• If you have asked for an add code: you have been already added to the waitlist. Otherwise, before emailing me, please check carefully the information on this page, and let me know in your email that you satisfy them. I will give add codes the week classes start. Priority will be given to PhD students whose research is in Machine Learning, and to Statistics graduate students. I would like to accept everyone, but we must keep in mind the workload of the TA, which will not be made easier with all the unpredictables of this fall. So, I will try to accept up to 10 more students above the current capacity; if you do not get an email from my on Monday, look for updates here. Please also consider auditing!

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 (TB UPDATED).

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 (Winter), and
STAT 548/CSE 547 Machine Learning for Big Data (Winter)
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.
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 Textbooks are listed here

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,12:30 - 1:50, & Thursdays 12:30-1:50 in CMU 226. This is a hybrid class. The plan is to hold most lectures in person, but some on-line lectures are likely. The lectures will not be recorded.

Office hours: Monday 2:30-3:30 on-line (TB confirmed)

Course home page: http://www.stat.washington.edu/courses/stat535/fall21 (this page)

Course Canvas site: is here