Machine Learning

• Data – vector, stream, signals, text, networks.
• Data cleaning, dealing with missing values, aggregation.
• Dimension reduction, dimension expansion.
• Explorative data analysis
• Unsupervised learning – frequent pattern mining, clustering, clustering validation
• Anomaly detection
• Supervised learning
- Classification using linear models
- Classification using probabilistic models
- Classification using non-linear models
- Regression models
• Network data analysis
- Network models
- Clustering, relations
- Community detection
• Data visualization