DISA

The main objective with this course is to get an introduction into modern statistical methods for modeling and and prediction of data. After successfully completing the course, the student is anticipated to be able to

• Demonstrate a conceptual understanding of the following fields in statistics: classification, resampling methods, linear model selection and regularization, and unsupervised learning.
• Apply modern statistical software for classification, resampling methods, linear model selection and regularization, and unsupervised learning.

The course contains

• Linear regression: simple and multiple linear regression with assessing the accuracy of the coefficients and the model and comparison with K-nearest neighbors
• Classification: logistic regression, linear discriminant analysis, K-nearest neighbors
• Resampling methods: cross-validation, bootstrap
• Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions
• Unsupervised learning: Principal component analysis, clustering methods
• Writing and presentation of a report where real data materials are analyzed with appropriate statistical approaches from the particular statistical field

Type of Instruction
Teaching consists of lectures, presentations, laboratory work, and tutoring.

Examination
The course is assessed with the grades A, B, C, D, E, Fx or F. The grade A constitutes the highest grade on the scale and the remaining grades follow in descending order where the grade E is the lowest grade on the scale that will result in a pass. The grade F means that the student’s performance is assessed as fail (i.e. received the grade F). The student’s knowledge is assessed in form of

• Graded conceptual assignments (3 credits), grades A to F
• Graded computer assignments (3 credits), grades A to F
• Presentation of the use of statistics in the student’s research alternatively presentation of a statistical topic not covered in this course (1,5 credits), grading scale U-G.

Required reading
G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning: with applications in R, latest edition, Springer.
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, latest edition.

Timetable
The course will start on Fri March 27 and finish by the end of May/beginning of June. If convenient for participants it is suggested that we meet weekly on Fridays at, say 13:15.

Registration
Try to finalize the registration no later than Mar 17 so it will be easier to plan. Register here https://forms.gle/a3zzgG5toouMFqYb7

Prerequisites
1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.

If you have any questions please contact Roger Pettersson (https://lnu.se/personal/roger.pettersson/).