Datamining

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Keywords

Datamining

Objectives

Tools and methods useful when dealing a large volume of data are studied in this course. Students should acquire the capacity of using different methods of processing and analysis of data jointly with computer tools in order to structure and present in a form appropriate for decision making the results of data processing and analysis, highlighting the underlying and relevant information.

Number of hours

  • Travaux Dirigés : 28.5h
  • Cours Magistral : 30h

Form of assessment

First session

Second session

Continuous assessment: 100%

Final examination: 0%

Remedial examination: 100 %

Remedial examination length: 2 hours

Syllabus

I. Classification methods:

1. association rules

2. decision trees

3. random forests

II. Neural network:

1. basic perceptron and multilayer NN

2. Kohonen maps (or self-organizing map - SOM)

3. recurrent neural network (RNN)

4. Multiple tests and correction methods (FWER, FDR).

5. Supervised learning methods (lasso regression, ridge regression, Partial Least Square – PLS – and its sparse version) and unsupervised learning methods (principal components analysis – PCA – and its sparse version.

     

    In brief

    ECTS credits 6.0

    Number of hours 58.5

    Level of study Master degree level

    Contact(s)

    Organizational unit

    Administrative contact(s)

    Secrétariat de Mathématiques

    Email : secretariat-mathematiques @ univ-pau.fr

    Places

    • Pau