Statistikinlärning för doktorander
For you that are a PhD student who wants to understand some of the modern statistical tools that are used to analyze big complex datasets concerning classification, resampling methods, linear model selection with regularization, and unsupervised learning. In the examination is included presentation of the use of statistics in your research alternatively presentation of a related statistical topic.
Kursinformation
Antal hp
7,5 hp
Ges av
Fakulteten för teknik
Öppen för
Doctoral students at the Linnaeus University
Undervisningsspråk
Engelska
Ort
Växjö
Nästa kurstillfälle
Spring 2022
Studietakt eller motsvarande
25%
Sista ansökningsdag
February 1, 2022
Anmälan
Förkunskapskrav
1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.
Undervisningsformer
Teaching consists of lectures, presentations, laboratory work, and tutoring, both physical and by distance.
- 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, quadratic discriminant analysis, K-nearest neighbors, naive Bayes
- 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
