Department of Information Technology

Statistical Machine Learning

Course dates: 2017-01-16 -- 2017-03-17.

Course content

This is an introductory course to statistical machine learning focusing on classification and regression. The course will cover a range of techniques used in machine learning and data science, including:

  • Classical and Bayesian linear regression
  • Classification via logistic regression
  • Linear discriminant analysis
  • Gaussian processes and kernel methods
  • Regularization (ridge regression and the LASSO)
  • Regression and classification trees
  • Boosting
  • Neural networks and deep learning

These methods will be studied and applied to real data from various applications throughout the course.

Course Structure

Schedule

The course schedule is available in TimeEdit.

Formalities

The course is for 5 credits. Entry requirements are: 120 credits, including Probability and Statistics, Linear Algebra II, Single Variable Calculus, and one basic programming course.

Teachers

Updated  2017-03-01 19:37:00 by Fredrik Lindsten.