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

• pre-processing and arranging of data: visualisation, outlier detection, scaling, formatting of data types.
• clustering: K-means, DB-scan, hierarchical trees, PCA
• classification: KNN, logistic regression, LDA, SVM, decision trees
• regression: OLS, regularisation, polynomial regression, tree based methods, PCR, PLS
• various criteria for modell selection

The course will give an introduction to the basic theoretical properties of the methods, but has main focus on applied modelling using real data.

Skills and insight into basic techniques for machine learning generated by modern data capture. 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.

Machine learning is a subject that constantly evolves, and online learning resources will be connected to lectures and exercises through the course webpages in Canvas.

The students can also request appointments with the lecturer in his/her office on pre-arranged times and via email.

MATH-INF110, alternatively STAT100 or similar.

MATH113/MATH131 or similar basic linear algebra.

INF120 or a similar course in basic programming.