Statistics 697ML - ST-Stat Machine Learning

Fall
2019
01
3.00
Patrick Flaherty
M W 2:30PM 3:45PM
UMass Amherst
33428
Lederle Grad Res Tower rm 206
flaherty@math.umass.edu
This course is intended as an introductory course in statistical machine learning with emphasis on statistical methodology as it applies to large-scale data applications. At the end of this course, students will be able to build and test a latent variable statistical model with companion inference algorithm to solve real problems in a domain of their interest. Course topics include: introduction to exponential families, sufficiency and conjugacy, graphical model framework and approximate inference methods such as expectation-maximization, variational inference, and sampling-based methods. Additional topics may include: cross-validation, bootstrap, empirical Bayes, and deep learning networks. Graphical model examples will include: naive Bayes, regression, hidden Markov models, principal component, factor analysis, and latent variable/topic models.
Open to Graduate students only. STATISTC 516 OR 607 Students who have not taken STATISTC 516 at UMass, but have equivalent knowledge, may seek instructor permission to enroll.

Note: Students must have an understanding of linear algebra at the level of Math 235.
Note: Students must be comfortable with a high-level programming language such as MATLAB, R or Python.
Permission is required for interchange registration during the add/drop period only.