STK-IN9300 – Statistical learning methods in Data Science
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).
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.
In addition to the final exam, each PhD student is expected to give an oral presentation on a topic of relevance (chosen in cooperation with the lecturer). The presentation has to be approved by the lecturer for the student to be admitted to the final exam.
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
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).
- 10 credits overlap with STK-IN4300 – Statistical learning methods in Data Science
- 7 credits overlap with STK9030 – Statistical Learning: Advanced Regression and Classification (discontinued)
3 hours lectures/exercises per week.
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.
In addition, each PhD candidate is expected to give an oral presentation on a topic of relevance chosen in cooperation with the lecturer. The presentation has to be approved by the lecturer for the student to be admitted to the final exam.
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.
Grades are awarded on a pass/fail scale. 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.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.