All programmes > Data Mining and Neural Networks

Data Mining and Neural Networks (B-KUL-G9X29A)

4 ECTSEnglish22 First termCannot be taken as part of an examination contract
Suykens Johan
POC Master in statistiek

Aims

- The student must understand basic and more advanced techniques of neural networks for datamining, as well as related methods of nonlinear modeling.

- The student must be able to apply the methods to real data sets and constructively work towards good solutions.

Identical courses

This course is identical to the following courses:
G9X29B : Data Mining and Neural Networks

Is included in these courses of study

Activities

1 ects. Data Mining and Neural Networks: Preparatory Reading (B-KUL-G0T65a)

1 ECTSEnglishFormat: AssignmentFirst term
Suykens Johan
POC Master in statistiek

Content

Preparatory reading to the exercise sessions

Is also included in other courses

2.5 ects. Data Mining and Neural Networks: Lectures, Part 1 (B-KUL-H05R4a)

2.5 ECTSEnglishFormat: Lecture16 First term
Suykens Johan
POC Wiskundige ingenieurstechnieken

Content

Lectures:

1. Introduction
2. Multilayer feedforward networks and backpropagation
3. Nonlinear modelling and time-series prediction
4. Classification and Bayesian decision theory
5. Generalization, Bayesian learning of neural networks
6. Vector quantization, self-organizing maps, regularization theory
7. Basic principles of support vector machines and kernel-based models
8. Nonlinear principal component analysis, autoencoders, deep learning with stacked autoencoders and convolutional neural networks
9. Generative models: deep Boltzmann machines, generative adversarial networks, variational autoencoders, others
10. Normalization, attention, transformers

Course material

- English course text in toledo

- Slides of the lectures are available in toledo

Format: more information

- Lectures and computer exercise sessions

- Report of the exercise sessions

Is also included in other courses

0.5 ects. Data Mining and Neural Networks: Training Sessions, Part 1 (B-KUL-H05R6a)

0.5 ECTSEnglishFormat: Practical6 First term
Suykens Johan
POC Wiskundige ingenieurstechnieken

Content

computer exercise sessions

Format: more information

Report of the exercise sessions

Is also included in other courses

Evaluation

Evaluation: Data Mining and Neural Networks (B-KUL-G2X29a)

Type : Exam during the examination period
Description of evaluation : Oral, Written
Type of questions : Open questions
Learning material : Course material

Explanation


The exam consists of an oral discussion of the report of the exercise sessions during the examination period.