Outline 
<p>
<ul>
Association rule mining.
<p>
Multirelational learning (ILP)
<p>
Tree learning
<p>
Ensembles
<p>
Experiment preparation and evaluation
<p>
INTRASEMESTRAL EXAM
</ul>
Unsorted:
<p>
<ul>
Learning from Imbalanced Data
<p>
Mining in graphs, sequences and time series. Imbalance in Time series forecasting (see LIDTA 2020)
<p>
Automated Machine Learning
<p>
Active and semi-supervised learning
<p>
Dataset and Instance hardness. Anomaly analysis
<p>
Theory of ML
<p>
</ul>
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Cost-sensitive learning. Logistic regression. Tools for ML and DM.
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