STK-IN4300 – Statistical learning methods in Data Science

Schedule, syllabus and examination date

Choose semester

Course content

The course focuses on methods for modern data analysis both within a practical and theoretical framework. Fewer assumptions are made when using these methods, such as machine learning or statistical learning, than when using classical methods. Consequently, the data play a larger role in the identification of structures and relationships. Starting from the basic methods, the course will then cover more advanced procedures, specifically designed to tackle modern data challenges such as increasing complexity and large amounts of information (Big Data settings). 

Learning outcome

After completing the course you will:

  • understand key concepts for a good analysis of the data;
  • understand the theoretical aspects of methods within machine/statistical learning;
  • know a range of different methods for data analysis, including penalized likelihood and basis expansions, neural networks, boosting and ensemble methods and Gaussian processes within machine learning;
  • know procedures for fitting such methods to data, including (stochastic) gradient descent and back-propagation;
  • be able to evaluate strengths and weaknesses of the methods and choose between them in practice.

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

To be admitted to this course, you must be admitted to a Master's program at the Department of Mathematics. 

Prerequisites

Recommended previous knowledge

Some basic mathematical knowledge in probability (STK1100 – Probability and statistical modelling), linear regression (STK1110 – Statistical methods and data analysis ), linear algebra (MAT1120 – Linear algebra), and basic programming skills (IN1900 – Introduction to Programming with Scientific Applications).

Teaching

3 hours lectures/exercises per week.

Examination

2 mandatory assignments.

Final oral or written examination. The form of examination will be announced by the teaching staff by 15 October/15 March for the autumn semester and the spring semester respectively.

Examination support material

No examination support material is allowed.

Language of examination

Subjects taught in English will only offer the exam paper in English.

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

Resit an examination

This course offers both postponed and resit of examination. Read more:

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course

Credits

10

Level

Master

Teaching

Every autumn

Examination

Every autumn

Teaching language

English

The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.