Introduction to Machine Learning (S424)

Course Description:
This is an introductory course in statistical machine learning, focusing on classification and regression: linear regression, regularization (ridge regression and LASSO), classification via logistic regression, linear discriminant analysis, classification and regression trees, boosting, neural networks, deep learning; practical considerations such as cross validation, model selection, the bias-variance trade off, applying the methods to real data.

On completion of the course, the student should be able to:

  • Structure and divide statistical learning problems into tractable sub-problems, formulate a mathematical solution to the problems, and implement this solution using statistical software.
  • Use and develop linear and nonlinear models for classification and regression.
  • Describe the limitations of linear models and understand how these limitations can be handled using nonlinear models.
  • Explain how the quality of a model can be evaluated and how model selection and tuning can be done using cross-validation.
  • Explain the trade-off between bias and variance.
  • Describe the difference between parametric and nonparametric models.


Prerequisites:  background in elementary calculus, linear algebra, and probability theory.

Format:  The course will be delivered through lectures, tutorials, and lab sessions. Student performance will be evaluated through individual projects, exercises & labs, quizzes, and written exams.

Acknowledgment of Course Design: The content of these slides and the overall course structure follow the
 official course design developed at Uppsala University.