Study materials explanation

L1 Intro. Project. Overview of machine learning.

L2 Association rule mining. Multirelational learning (ILP)

L4 Tree learning (cont.). On Kolmogorov complexity and tree learning. Ensembles

L5 Experiment preparation and evaluation

Intrasemestral exam

L 6 Learning from imbalanced data

L7 Anomaly analysis. Class-based outliers

L8 PRESENTATIONS ARE STARTING. Outlier explanaiton. Dataset hardness.

L9 Presentations (2). Instance hardness. Automated machine learning.

L10 Presentations (3). AutoML. Active and semi-supervised learning. Mining in graphs (intro.)

Mining in graphs (cont.), sequencies and time series

Mining time series. Notes on theory of ML