BAN404 Statistical Learning
This course is an introduction to statistical learning and its application to help making economic decisions. Statistical learning is the science of extracting important patterns from data, patterns that can inform a decision maker. Particularly, the course focuses on predictive analytics, the activity of predicting future events or predicting an unobserved property of an individual, a company or some other unit. The course will contain applications on business data. The students will work on those using the statistical software R. Examples of applications are statistical fraud detection and market basket analysis.
- Linear regression and classification
- Resampling methods
- Model selection
- Non-linear models
- Tree-based methods
- Unsupervised learning
After completing the course, the students will be able to:
- Discuss the central ideas of statistical learning and predictive analytics.
- Understand scientific publications using statistical learning methods.
- Formulate a statistical learning problem and report its results.
- Implement the most common techniques to real-world prediction problems in R.
- Evaluate predictions and deal with the problem of overfitting.
- Appropriately choose between a set of statistical learning methods for a given problem,
- Explain and discuss concepts in statistical learning.
Lectures and, in addition, tutorials where students work on exercises.
Basic skills in statistical inference. Basic skills in R.
Credit reduction due to overlap
Course identical to BUS459.
Written project report (approved / not approved). The report must be written in English.
The final grade is based on an individual 6 hour digital school exam. The students will have access to the statistical software R, and the exam must be answered in English.
This course is a continuation of BUS459 and the total number of attempts applies to the course (not the course code).
James, Witten, Hastie and Tibshirani (2021) "An introduction to statistical learning with applications in R". Second Edition.
Permitted Support Material
All written support material permitted (category III)
One bilingual dictionary (Category I)
All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material
and https://www.nhh.no/en/for-students/regulations/ https://www.nhh.no/en/for-students/regulations/ https://www.nhh.no/en/for-students/examinations/examination-support-materials/ https://www.nhh.no/en/for-students/examinations/examination-support-materials/
- ECTS Credits
- Teaching language
Spring. Will be offered Spring 2024.
Professor Jonas Andersson, Department of Business and Management Science