STK2100 – Machine Learning and Statistical Methods for Prediction and Classification
Schedule, syllabus and examination date
Exams after the reopening
As a general rule, exams will be conducted without physical attendance in the autumn of 2021, even after the reopening. See the semester page for information about the form of examination in your course. See also more information about examination at the MN Faculty in 2021.
STK2100 gives an introduction to different methods for supervised learning (regression and classification). The course contains both model and algorithm based approaches. The main focus is supervised learning, but also unsupervised methods like clustering will be briefly discussed. The course also deals with issues connected to large amounts of data (’big data’).
The course gives a good basis for further studies in statistics or Data Science, but is also useful for students who need to perform data analysis in other fields.
After completing the course you:
- know the fundamental principles for supervised learning (regression and classification), and also how to evaluate such methods
- can master many different methods for supervised learning, including linear models, logistic regression, tree-based methods, bootstrapping and other simulation-based methods, dimension reduction and regularization, bagging and boosting and support vector machines
- have knowledge on issues connected to high-dimensional data
- have knowledge of problems connected to large amounts of data and methods for unsupervised learning.
Admission to the course
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.
Special admission requirements
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
Mathematics R1 (or Mathematics S1 and S2) + R2
And in addition one of these:
Information technology (1+2)
Technology and theories of research (1+2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies (in Norwegian).
Recommended previous knowledge
- STK1100 – Probability and Statistical Modelling
- STK1110 – Statistical Methods and Data Analysis
- MAT1100 – Calculus
- MAT1110 – Calculus and Linear Algebra
- MAT1120 – Linear Algebra
- IN1900 – Introduction to Programming with Scientific Applications
- MAT-INF1100 – Modelling and Computations
- 7 credits overlap with STK4030 – Statistical Learning: Advanced Regression and Classification (discontinued).
3 hours lectures and 2 hours exercise sessions every week of the semester.
Final written exam which counts 100 % towards the final grade.
This course has 2-4 mandatory assignments that must be approved before you can sit the final exam.
Examination support material
Approved calculator and formula lists for STK2100.
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 scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more: