PV056 Machine learning and data mining

Literature

Data Mining: Concepts and Techniques, 3rd Edition

Flach, Peter A. Machine learning : the art and science of algorithms that make sense of data.

Machine Learning by Tom Mitchell, McGraw Hill, 1997 slides
Data Mining: Practical Machine Learning Tools and Techniques Ian H. Witten, Eibe Frank, Mark A. Hall (Weka book)

Data Science for Bussiness Foster Provost & Tom Fawcett
Knowledge discovery in databases by Petr Berka (in Czech)

Teaching materials 2021 can be found thru ISMU Interactive syllabus (here)

MANY YEARS AGO ...

Weka (homepage) (datasets) (wiki) (how do I use WEKA's classes in my own code)
(multi-instance classification in weka) and (data sets)
Time series in Weka (two filters in weka) (TClass) (from wpi)
Artificial neural network plugins etc.
BioWeka

R
Home page of the R-project
R-project at CUNI

Ensemble methods. Bagging. Boosting. Random forests and RF visualization
Regression trees (C&RT)
Active learning
Learning from streams (ICDM 2006 Tutorial)
Learning from multi-instance data
Semi-supervised learning
Preference learning (->)
Meta-learning (intro) Experiment evluation, meta-learning. Minimal description length. Monte Carlo

DATA MINING

Basics: KDD process. Data mining, dependence, association, interestingness. Tasks. CRISP-DM. Predictive Model Markup Language: Description and PMML support in Weka
First-order frequent patterns. Methods. RAP (here) (PhD. Thesis, auth.)
Data pre-processing (overview) (Slides)
DBMiner (Project description) (Examples) (Tutorial) and inductive query languages
Other data mining tools
Introduction to Privacy-preserving data mining (pdf)
Swarm intelligence and data mining
    intro and swarm robotics (ppt)
    The bee algorithm and optimization (ppt) by Ziad Salem
    Ant colonies for data mining (ppt)
Distributed mining: decision tree learning*, frequent patterns mining (here) (PhD. Thesis, auth.)