Centre for Data Intensive Sciences and Applications

New PhD-course in Statistical Learning 7,5 credits


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.

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.

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.

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

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/).

Seminar: “A Few Notes on Artificial Intelligence and Database Technology” – Thursday Jan. 23 at 13-14

Title: A Few Notes on Artificial Intelligence and Database Technology

Place: D1173
Time: 13-14
Date: January 23, 2020

Abstract: In this seminar I would like clarify the importance of the scientific theory functional object-types to approach reality. This theory help to evaluate knowledge representation formalisms, deep learning and data modelling and data transformations. Further, the theory, together with mathematical logic, is the foundation for the Match™ Technology Ecosystem that enables organisations to model, build no code applications and simplifying IT architectures.

About: Dr.Larry Lucardie has a background in Artificial Intelligence and Semantic Database modelling. At The Technical University of Eindhoven he graduated on a theory of complexity, functional classifications, that is fundamental to knowledge representation, deep learning and data modelling. Larry is the main architect of the Match™ AI & Data Technology Platform that is aligned to functional classifications. The Match™ platform enables organisations to design ISO compliant models of enterprise content, business fluid no-code applications, simplified IT architectures and smart internet portals.

As a Professor at the Uppsala University in Sweden, Larry lectured logic programming, knowledge and data modelling and E-business and supervised PHD students. He is founder and the current CEO of Knowledge Values and as such involved in improvement projects in the areas of value chain and process re-engineering and of process underlying technology as application development, IT architectures and data processing. Business areas: regulation management and compliance, E-commerce, financial processes products, incident management, compliance and Brexit.

Warm welcome,
Arianit Kurti, Associate Professor, Department of Computer Science and Media Technology