STK2100 - Machine learning and statistical methods for prediction and classification
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
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.
Formal prerequisite knowledge
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
One of these:
- Mathematics R1
- Mathematics (S1+S2)
And and in addition one of these:
- Mathematics (R1+R2)
- Physics (1+2)
- Chemistry (1+2)
- Biology (1+2)
- Information technology (1+2)
- Geosciences (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. Read more about special admission requirements (in Norwegian).
Recommended previous knowledge
STK1100 - Probability and statistical modelling, STK1110 - Statistical methods and data analysis 1, MAT1100 - Calculus, MAT1110 - Calculus and linear algebra og MAT1120 - Linear algebra, INF1100 - Introduction to programming with scientific applications (continued), MAT-INF1100 - Modelling and computations.
7 credits overlap with STK4030 - Statistical Learning: Advanced Regression and Classification (discontinued)
3 hours lectures, 1 hour topics examined in plenum and 2 hours group sessions/data lab every week of the semester.
Between two and four compulsory assignments need to be passed within given deadlines to be allowed to take the final exam. Final mark based on written examination at the end of the semester.
Examination support material
Approved calculator and formula lists for STK2100.
Information about approved calculators (Norwegian only)
Language of examination
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.
Explanations and appeals
Resit an examination
This course offers both postponed and resit of examination. Read more:
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.