STK-IN9300 – Statistical Learning Methods in Data Science

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.

PhD candidates from the Faculty of Mathematics and Natural Sciences at 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.

• 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 eller emne: MAT1125.
• Basic programming skills equal to IN1900 – Introduction to Programming with Scientific Applications.

4 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 or final oral exam, which counts 100 % towards the final grade.

The form of examination will be announced by the lecturer by 1 October/1 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 – Statistical Learning Methods in Data Science

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass, the original examination.