Lecture in Winter 2018/2019, Paris Lodron University of Salzburg, Austria

On this web page you can find information about the lecture "Data Mining / Intelligent Data Analysis" that is given by Christian Borgelt in winter 2018/2019 at the Paris Lodron University of Salzburg, Austria. This page will be updated in the course of the semester.

Type of activity: |
Lecture/Exercise Lesson | |

Semester week hours: |
2 (total: 32 hours) | |

Credits: |
? (to be determined) | |

Language: |
German or English according to preference | |

Requirements: |
Standard university mathematics | |

Certificate: |
oral examination (graded: ca.~30 minutes, ungraded: ca.~10-15 minutes) |

**Contents:**

- Data and Knowledge, Knowledge Discovery in Databases, Data Mining, Intelligent Data Analysis
- Descriptive Statistics: Tabular and Graphical Representations, Characteristic Measures, Principal Component Analysis
- Inductive Statistics: Parameter Estimation, Hypothesis Testing, Model Selection
- Regression: Linear, Multivariate Polynomial, and Logistic
- Decision and Regression Trees: Induction Algorithm, Attribute Selection Measures, Pruning, Random Forests
- Artificial Neural Networks: Multilayer Perceptrons, Function Approximation, Training Methods
- K-Nearest Neighbor: Lazy Learning, Number of Neighbors, Attribute Weighting and Selection, Access Data Structures
- Clustering: k-Means, Learning Vector Quantization, Fuzzy Clustering, Estimation of Mixtures of Gaussians, Hierarchical Agglomerative Clustering
- Frequent Item Set Mining: Item Sets and Association Rules, Mining Algorithms

**Learning Objectives:**

- Knowledge of the basic ideas of data mining, intelligent data analysis and knowledge discovery in databases
- Knowledge of the most frequently used data mining methods
- Understanding of the advantages and disadvantages of different data mining methods
- Ability to select a data mining methods for a given problem
- Ability to build models with data mining methods
- Application of data mining methods in practice

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Day of the Week | Time | Room | Start |
---|---|---|---|

Monday | 15:00-17:00 | HS 415 (H34OG1.A-002) | 01.10.2018 |

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If you have questions about the lecture or the exercises, please refer (by email or in person after a lecture) to:

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Lecture slides (in English, version 2018.10.08, 464 slides).

ida.pdf | (2186 kb) | one slide per page |

ida4.pdf | (2214 kb) | four slides per page, two by two |

ida4s.pdf | (2213 kb) | four slides per page, four by one |

ida.zip | (5221 kb) | LaTeX and METAPOST source files |

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Here exercise sheets with several exercises will be made available, a selection of which will be discussed during the lecture.

The exercise sheets as well as their (future) solutions are only available in German.

- 1. Übungsblatt: ida01_ger.pdf

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**Guide to Intelligent Data Analysis: How to Intelligently Make Sense of Real Data**

Michael R. Berthold, Christian Borgelt, Frank Höppner, and Frank Klawonn

Springer-Verlag, Berlin, Germany 2010, ISBN 978-1-84882-259-7

(397 pages, in English)**Data Mining: Practical Machine Learning Tools and Techniques**

Ian H.~Witten, Eibe Frank, Mark Hall, and Christopher Pal

Morgan Kaufmann, Burlington, CA, USA 2016, ISBN 978-0-12-804291-5

(654 pages, 4th edition, in English)**Data Mining: Concepts and Techniques**

Jiawei Han, Micheline Kamber, and Jian Pei

Morgan Kaufmann, Burlington, CA, USA 2011, ISBN 978-0-12-381479-1

(744 pages, 3rd edition, in English)

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