Course content

Statistical learning, multiple linear regression, classification, resampling methods, modell selection/regularization, non-linearity, tree-based methods, neural networks.

Learning outcome

1. Knowledge. The student has knowledge about the most popular statistical models and methods that are used for prediction in science and technology, with emphasis on regression- of classification-type statistical models.

2. Skills. The student can, based on an existing data set, choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software. The student can present, interpret and communicate the results from the statistical analyses, and knows which conclusions can be drawn from the analyses, and what are the caveats.

Learning methods and activities

Lectures, exercises and compulsory works (projects). The assessment is a final written examination (100%), whereas two projects need to be completed as compulsory activities, where at least 60% of the points must be reached to be admitted to the exam.

Compulsory assignments

• Project

Recommended previous knowledge

The course is based on TMA4240/4245 Statistics, or equivalent. Good understanding of linear algebra (matrix methods) and optimization.

The student will get maximum output from the course when taken together with TMA4267 and TMA4315 (either previous semester or in combination).

Course materials

The course material will be communicated at the beginning of the semester.

Credit reductions