Introduction to statistical learning
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
- Predictive regression models: linear regression,
regularization,
and shrinkage, non-linear regression, regression trees, random
forests.
- Predictive classification models: logistic regression,
discriminant analysis, classification trees.
- Crossvalidation and randomization
- Unsupervised learning
- PCA
- Clustering
Prerequisites
Statistics I: Basic Statistics and Statistics III: Regression analysis
or equivalent
Admitted to a Ph.D. program.
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