DAT200 Applied Machine Learning

Introduction to basic machine learning methodology using modern, powerful computing tools. The methodology covered includes:

• preprocessing and arranging of data: feature extraction (PCA, LDA), feature selection/importance, visualisation, scaling, formatting of data types.
• clustering: among other K-meansclassification: KNN, logistic regression, LDA, SVM, decision trees
• regression: OLS, regularisation, polynomial regression, tree based methods, PCR, PLS
• strategies for model validation and parameter optimisation

The course will give an introduction to the basic theoretical properties of the methods, but has main focus on applied modelling using real data. The students will learn to build effective and accurate models that, depending on the application, can contribute to several of UN's sustainability goals, among others 3, 11, 12, 14, 15.

Skills and insight into basic techniques for machine learning. Basic understanding of various models' mathematical properties and operations. The student will learn to master analysis methods suited for 1) Explorative data analysis (diagnose and visualisation), 2) Pre-processing of data from various sources, 3) Modelling and prediction med continuous and categorical responses (regression and classification) and validation of predictive models.

The student will learn to connect problems with choice of appropriate analysis methods.