Statistics, Statistical Learning, Advanced Course, 7.5 credits

Aims and objectives

General aims for first cycle education

First-cycle courses and study programmes shall develop:

• the ability of students to make independent and critical assessments
• the ability of students to identify, formulate and solve problems autonomously, and
• the preparedness of students to deal with changes in working life.

In addition to knowledge and skills in their field of study, students shall develop the ability to:

• gather and interpret information at a scholarly level
• stay abreast of the development of knowledge, and
• communicate their knowledge to others, including those who lack specialist knowledge in the field.

(Higher Education Act, Chapter 1, Section 8)

Course objectives

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

Main content of the course

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.

Teaching methods

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.

Examination methods

Written Examination, 3 credits (Code: A001)

Assignments, 4.5 credits (Code: A002)

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

For further information, see the university's local examination regulations (in Swedish).

Grades

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 prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

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

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

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

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

For further information, see the university's local examination regulations (in Swedish).

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.

Specific entry requirements

60 credits in Statistics including Statistical Theory 15 credits and Linear Algebra 7.5 credits, or 60 credits including Statistical Theory 15 credits and mathematics 22.5 credits, or Statistical theory advanced level 7.5 credits and Mathematics for statistics and economists advanced level 7.5 credits.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Reading list and other teaching materials

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/