Machine Learning with R, part 2, 3 Credits

Contents

The course will introduce fundamental ideas and core concepts of machine learning along with methods based on linear models. Building on this, the focus will be on classical algorithms based on ensemble ideas for tabular data, e.g., random forest, and more advanced models based on neural networks for non-tabular data (e.g., image data). The aim of the course is to help students acquire knowledge about machine learning algorithms. In addition, implementations of various methods will be demonstrated in the R environment.

This course is a perfect opportunity to deepen your knowledge within machine learning if you have already taken the course Machine Learning with R, part 1. However, it can also be taken as a standalone course.

Expected learning outcomes

Knowledge and understanding
Students must be able to

1.    Explain the basic ideas and objectives of machine learning
2.    Explain the mechanism of operation and characteristics of the models for machine learning that are covered in the course
3.    Explain the problem of overfitting in machine learning

Skill and abilities
Students must be able to

4.    Transfer concrete problems into machine learning problems and select appropriate methods and models to solve them
5.    Use R to implement methods for training, model selection, and model validation that are introduced in the course

Judgment and approach
Students must be able to

6.    Choose, based on appropriate criteria for the problem at hand, the best model among candidates using different validation methods

Required Knowledge

7,5 ECTS in Statistics incl. R, e.g., Machine Learning with R, part 1 (7.5 ECTS). Proficiency in English equivalent to Swedish upper secondary course English B/6

Form of instruction

Learning is supported by lectures, computer lab sessions, and tutoring.

Examination modes

The examination consists of a project study carried out as a group assignment. The project study includes a written report and an oral presentation. The grades used are VG (Pass with distinction), G (Pass), and U (Fail). The grade awarded on the course is a comprehensive evaluation of the results, for each individual student, on the various parts of the examination and is not granted until all mandatory tasks are passed.

A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional examination opportunity will be held according to a pre-determined schedule.

A student that has failed an examination on two occasions has a right to have another examiner or grading teacher appointed unless there are special reasons against it. A written request addressed to the Director of Studies should be made no later than two weeks before the next examination opportunity.

Examinations based on the same course syllabus as the ordinary examinations are guaranteed to be offered up to two years after the date of the student's first registration for the course.

Adaptations
Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaptation of modes of assessment must give due consideration to the student's needs. The adaptation of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination - and have received a decision on the right to support from the coordinator at the Student Services Office for students with disabilities - must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaptation of the examination, after which the student will be notified.

Academic credit transfer
Academic credit transfers are according to the University credit transfer regulations.

Other regulations

This course partly overlaps with the course Statistics C: Methods for Data Science (10 Credits) and cannot therefore be credited in the degree together with that overlapping course.