- ”An Introduction to Statistical Learning (with applications in R)” by James, Witten et al. ISBN: 978-1461471370
- ”Neural Networks with R” by G. Ciaburro. ISBN: 978-1788397872
• Have a solid conceptual grasp on the described statistical learning methods.
• Be able to correctly identify the appropriate techniques to deal with particular data sets.
• Have a working knowledge of R programming software in order to apply those techniques and subse- quently assess the quality of fitted models.
• Demonstrate the ability to clearly communicate the results of applying selected statistical learning methods to the data.
- Introduction: What is Statistical Learning? Supervised and unsupervised learning. Regression and classification.
- Linear and Logistic Regression. Continuous response: simple and multiple linear regression. Binary response: logistic regression. Assessing quality of fit.
- Model Validation. Validation set approach. Cross-validation.
- Tree-based Models. Decision and regression trees: splitting algorithm, tree pruning. Random forests: bootstrap, bagging, random splitting.
- Neural Networks. Single-layer perceptron: neuron model, learning weights. Multi-Layer Perceptron: backpropagation, multi-class discrimination
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