Statistical Learning
Postgraduate course
- ECTS credits
- 10
- Teaching semesters
- Autumn
- Course code
- STAT260
- Number of semesters
- 1
- Teaching language
- English
- Resources
- Schedule
Course description
Objectives and Content
Topics treated in this course include regression, classification, model selection and a certain introduction to machine learning. The student will apply different packages in R.
Learning Outcomes
After completing the course, students should be able to:
- use nonlinear regression methods such as Spline, Local Regression and Generalized Additive Models
apply classification methods such as Logistic Regression, Linear Discriminant Analysis - know how to use resampling (cross validation, bootstrap) and Model Selection methods to assess and select models and deal with high dimensional data
- apply Tree-Based Methods such as decision tree, Bagging, Random Forests, Boosting
- avail Support Vector Machines for resolving classification and regression problem.
- know unsupervised learning methods such as Principal Components Analysis and Clustering Methods.
- know about Deep learning and Naive Bayes.
Semester of Instruction
Autumn irregular, course will be offered if it is on this course list: Workbook: Emneliste for innreisende utvekslingsstudenter (uhad.no)
Recommended Previous Knowledge
Compulsory Assignments and Attendance
Two approved compulsory excercises
Forms of Assessment
Oral examinations. Approved compulsory exercises is required to take the exam .
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.