Machine Learning and Data Analysis

Lecturer:

Filip Železný (gar.), Jiří Kléma

Tutor:

Filip Železný (gar.), Jiří Kléma, Ondřej Kuželka, Andrea Szabóová

Supervisor:

Department of Cybernetics

Synopsis:

The course explains machine learning methods helpful for getting insight into data by automatically discovering interpretable data models such as graph- and rule-based. Emphasis is given to the modeling of relational data. The course will also address a theoretical framework explaining why/when the explained algorithms can in principle be expected to work.

Requirements:

Topics contained in course A4B33RPZ.

For details see http://cw.felk.cvut.cz/doku.php/courses/m33sad/start

Syllabus of lectures:

1. Course introduction. Cluster analysis -- foundations (k-means, hierarchical and EM clustering).

2. Cluster analysis -- advanced methods (spectral clustering).

3. Cluster analysis -- special methods (conceptual and semi-supervised clustering, co-clustering).

4. Frequent itemset mining. the Apriori algorithm, association rules.

5. Frequent sequence mining. Episode rules. Sequence models.

6. Frequent subgraph search.

7. Learning from texts and web, applications.

8. Computational learning theory, concept space, PAC learning

9. PAC learning of logic forms, classification rule learning.

10. Inductive logic programming, least generalization, inverse entailment.

11. Learning from logic interpretations, relational decision trees, relational features.

12. Statistical relational learning: probablistic relational models.

13. Statistical relational learning: Markov logic.

14. Summary.

Syllabus of tutorials:

1. Entry test (prerequisite course RPZ). SW tools for machine learning (RapidMiner, WEKA)

2. Data preprocessing, missing and outlying values, clustering

3. Hierarchical clustering, principal component analysis

4. Frequent itemset mining, association rules

5. Frequent sequence/subgraph mining.

6. Learning from texts.

7. Test (first half of the course). Learning Curve.

8. Underfitting and overfitting, ensemble classification, error estimates, cross-validation

9. Model selection and assessment, ROC analysis

10. Project work

11. Project work

12. Inductive logic programming: the Aleph system

13. Statistical relational learning: the Alchemy system

14. Credits

Study Objective:

Learn principles of selected methods of data analysis methods and classifier learning, and elements of learning theory.

Study materials:

T. Mitchell: Machine Learning, McGraw Hill, 1997

P. Langley: Elements of Machine Learning, Morgan Kaufman 1996

T. Hastie et al: The elements of Statistical Learning, Springer 2001

S. Džeroski, N. Lavrač: Relational Data Mining, Springer 2001

L. Getoor, B. Taskar (eds): Introduction to Statistical Relational

Learning, MIT Press 2007

V. Mařík et al. (eds): Umělá inteligence II, III, IV (Czech)

Note:

Time-table for winter semester 2011/2012:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue	roomKN:E-128 Železný F. Kléma J. 11:00–12:30 (lecture parallel1) Karlovo nám. Cvičebna K3roomKN:E-132 Szabóová A. Kuželka O. 14:30–16:00 (lecture parallel1 parallel nr.101) Karlovo nám. Laboratoř PCroomKN:E-132 Szabóová A. Kuželka O. 16:15–17:45 (lecture parallel1 parallel nr.102) Karlovo nám. Laboratoř PC
Fri
Thu
Fri

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Otevřená informatika - Umělá inteligence (compulsory course of the specialization)
• Otevřená informatika - Počítačové vidění a digitální obraz (compulsory course of the specialization)
• Biomedicínské inženýrství a informatika - Biomedicínská informatika (compulsory course of the specialization)