Department of Information Technology

Statistical Machine Learning

Course dates: 2020-01-20 -- 2020-03-16.

Home exam

Information about the home exam can be found here

Course content

This is an introductory course to statistical machine learning for students with some background in calculus, linear algebra and statistics. The course is focusing on supervised learning, i.e, classification and regression. The course will cover a range of methods used in machine learning and data science, including:

  • Linear regression (including ridge regression and the Lasso)
  • Classification via logistic regression and k nearest neighbor
  • Linear and quadratic discriminant analysis
  • Regression and classification trees (including bagging and random forests)
  • Boosting
  • Neural networks and deep learning

These methods will be studied and applied to real data from various applications throughout the course. The course also covers important practical considerations such as cross-validation, model selection and the bias-variance trade-off. The course includes theory (e.g., derivations and proofs) as well as practice (notably the lab and the mini project). The practical part will be implemented using Python.

Course Structure

Schedule

The course schedule is available in TimeEdit.

Formalities

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

Grading

To pass the course you need to pass the mini-project, lab and exam. If you pass mini-project and lab, your exam grade will also be your course grade. Grade VG on the mini-project will increase your course grade by one step, but not from U to 3.
grading

Teachers

Updated  2020-06-12 09:32:45 by Johan Wågberg.