STK2100 – Machine Learning and Statistical Methods for Prediction and Classification
Schedule, syllabus and examination date
Changes in the course due to coronavirus
Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.
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 at UiO register for courses and exams 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 and 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 examination.
Examination support material
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 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: