Machine Learning and Data Analysis

Relations:

It is not possible to register for the course A4M33SAD if the student is concurrently registered for or has already completed the course AE4M33SAD (mutually exclusive courses).

The requirement for course A4M33SAD can be fulfilled by substitution with the course AE4M33SAD.

It is not possible to register for the course A4M33SAD if the student is concurrently registered for or has previously completed the course AE4M33SAD (mutually exclusive courses).

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Computer Science

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. The course will also address a theoretical framework explaining why/when the explained algorithms can in principle be expected to work.

The lectures are given in English.

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 subtrees and subgraphs.

7. Dimensionality reduction.

8. Computational learning theory - intro, PAC learning.

9. Computational learning theory (cont'd).

10. PAC-learning logic forms.

11. Learning in predicate logic.

12. Infinite Concept Spaces.

13. Empirical testing of hypotheses.

14. Wrapping up (if 14 lectures).

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. Spectral cluestering.

5. Frequent itemset mining, association rules

6. Frequent sequence/subgraph mining.

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

Note:

Further information:

http://cw.felk.cvut.cz/doku.php/courses/a4m33sad/start

No time-table has been prepared for this course

The course is a part of the following study plans: