Pre­dict­ive Mod­el­ing og Ma­chine Learn­ing

Course content

The course provides an introduction to the topics that are often referred to as artificial intelligence, data mining, and machine learning. During the course, the focus is going to be on the understanding of the methods and their theoretical foundation, as well as on concrete data applications of the various methods.

Specifically, the course covers the following topics:

• Non-parametric methods for function estimation: Smoothing, splines, regularization, additive models
• Penalized regression and classification: Properties, model selection, and inference
• Resampling methods, including cross-validation
• Tree-based models: Bagging, random forests, and boosting
• Neural networks, including structural networks such as recurrent and convolutional neural networks
• Selected topics on causal machine learning