The objective of the course is to give an overview of statistical
(machine) learning methods. On completion of the course, the student
will be able to:

- select an appropriate predictive model for a given problem
- program prediction and classification models in the statistical software R
- understand the role of model selection and assessment using cross-validation and randomization
- interpret and evaluate results correctly and draw reasonable conclusions
- clearly and concisely communicate results and conclusions

Content

The course will give an introduction to the following topics:

- Supervised learning
- Predictive regression models: linear regression, regularization, and shrinkage, non-linear regression, regression trees, random forests.
- Predictive classification models: logistic regression, discriminant analysis, classification trees.
- Crossvalidation and randomization
- Unsupervised learning
- PCA
- Clustering

**Prerequisites**

Statistics I: Basic Statistics and Statistics III: Regression analysis
or equivalent

Admitted to a Ph.D. program.

Introduction to statistical learning, Uppsala

Uppsala, Distance

2021-04-19-----2021-05-21

2021-03-25