Statistics, Statistical Learning, Advanced Course, 7.5 credits

Learning outcomes

Knowledge and understanding

After completed studies, the student shall

• have knowledge of methods for modeling large and/or complex data sets
• know the limitations and strengths of models covered in the course
• be aware of the considerations when evaluating and choosing models covered in the course.

Competence and skills

After completed studies, the student shall be able to

• propose, design and apply statistical solutions to problems with the use of models covered in the course
• present analyzes and results from using the models covered in the course.

Judgement and approach

After completing the course the student shall have the ability to

• Evaluate, compare and choose between models covered in the course.

Content

The content of the course is based on concepts (within data mining and statistical learning) such as prediction and model selection/evaluation, classification, regression models, unsupervised learning and neural networks.

Examinations and grades

Written Examination, 3 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Assignments, 4.5 credits (Code: A002)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

To obtain a passing grade for the course as a whole, a passing grade is required on all course components. The final grade for the entire course is a function of the grades of the course components. Detailed information on the requirements for different grade levels is given at the course start.

Modes of assessment

Written Examination, 3 credits (Code: A001)

Assignments, 4.5 credits (Code: A002)
Oral and written presentation.

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

First-cycle courses of 60 credits in statistics including the course, Statistics, Statistical Theory, Second Cycle 15 credits and Linear Algebra 7.5 credits or Mathematics for statisticians 7.5 credits. Alternatively Statistics, Statistical Theory, Second Cycle 15 credits and First-cycle courses of 22.5 credits in Mathematics

For further information, see the university's admission regulations.

Other provisions

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Required Reading

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (2013)
An Introduction to Statistical Learning: with Applications in R
Springer, Available on website: http://www-bcf.usc.edu/~gareth/ISL/

Additional Reading

Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009)
The elements of statistical learning
Springer, Available on website: https://web.stanford.edu/~hastie/ElemStatLearn/