Introduction to statistical learning
• Supervised learning
o Predictive regression models: linear regression, regularization, and shrinkage, non-linear regression, regression trees, random forests.
o Predictive classification models: logistic regression, discriminant analysis, classification trees.
• Crossvalidation and randomization
• Unsupervised learning
o PCA
o Clustering
Syllabus and other information
Syllabus
PNS0213 Introduction to statistical learning, 4.0 Credits
Subjects
Mathematical StatisticsEducation cycle
Postgraduate levelGrading scale
Language
EnglishPrior knowledge
Statistics I: Basic Statistics and Statistics III: Regression analysis or equivalentObjectives
The objective of the course is to give an overview of statistical (machine) learning methods. On completion of the course, the student will be able to:
• select an appropriate predictive model for a given problem
• program prediction and classification models in the statistical software R
• understand the role of model selection and assessment using cross-validation and randomization
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions
Content
The course will give an introduction to the following topics:
• Supervised learning
o Predictive regression models: linear regression, regularization, and shrinkage, non-linear regression, regression trees, random forests.
o Predictive classification models: logistic regression, discriminant analysis, classification trees.
• Crossvalidation and randomization
• Unsupervised learning
o PCA
o Clustering
Responsible department
Department of Energy and Technology