STK-IN9300 – Statistical Learning Methods in Data Science
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
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.
Admission to the course
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
- Basic mathematical knowledge in probability equal to STK1100 – Probability and Statistical Modelling.
- Linear regression equal to STK1110 – Statistical Methods and Data Analysis.
- Linear algebra equal to MAT1120 – Linear Algebra.
- Basic programming skills equal to IN1900 – Introduction to Programming with Scientific Applications.
- 10 credits overlap with STK-IN4300 – Statiske læringsmetoder i Data Science.
- 7 credits overlap with STK4030 – Statistical Learning: Advanced Regression and Classification (discontinued).
- 7 credits overlap with STK9030 – Statistical Learning: Advanced Regression and Classification (discontinued).
3 hours of lectures/exercises per week throughout the semester.
The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.
Final written exam 4 hours or final oral exam, which counts 100 % towards the final grade.
The form of examination will be announced by the lecturer by 15 October/15 March for the autumn semester and the spring semester respectively.
This course has 2 mandatory assignments that must be approved before you can sit the final exam.
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 before you can sit the final exam.
It will also be counted as one of the three attempts to sit the exam for this course, if you sit the exam for one of the following courses: STK-IN4300 – Statiske læringsmetoder i Data Science
Examination support material
No examination support material is allowed.
Language of examination
Courses 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.
Resit an examination
This course offers both postponed and resit of examination. Read more: