NOVA Information Management School

Data Mining II (Modelos Preditivos)/ Predictive Models

Código

200029

Unidade Orgânica

NOVA Information Management School

Créditos

7.5

Professor responsável

Língua de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Objectivos

Introducing the main concepts and methods of supervised Machine Learning.

Pré-requisitos

No requirement.

Conteúdo

1. Introduction to Machine Learning- The concept of learning. Learing a function.
- Concept of generalization. Training set e test set.
- Supervised and unsupervised learning.
- Classification and clustering.
- Performance of a classifier. Data splitting. Crossvalidation and its variants. Precision e Recall. F-measure. K-statistic.
- The concept of feature. Feature selection.

2. Decision Trees
- General Functioning of the method
- Examples of application

3. Neural Networks
- Introduction
- Perceptron:
- One neuron model
- Perceptron Learning Rule.
- Convergence theorem of Perceptron.
- Main activation functions.
- Adaline:
- general structure
- Delta rule. The concept of gradient descent.
- Linearly separable and non-linearly separable problems.
- Layers of hidden neurons.
- Theorem of Universal Approximation.
- Backpropagation
- Ciclic or recursive Neural Networks:
- Jordan Networks
- Elman Networks
- Hopfield Networks (the concept of associative memory, Hebb learning rule).
- Examples of application

4. Support Vector Machines
- General functioning
- Kernel functions
- Examples of application

5. Genetic Programming
- Representation of solutions and principal differences with Genetic Algorithms.
- Genetic Operators
- Fitness Calculation
- Property of Closure and Sufficiency
- Steady State.
- Automatically Defined Functions (ADF).
- GP Benchmarks (even parity, multiplexer, symbolic regression, artificial ant on the Santa Fe trail).
- Parallel and Distributed Genetic Programming (definition and experimental study).
- Diversity and premature convergence
- Open issues and new trends in GP
- integration of semantic awareness in GP
 

Bibliografia

"Machine Learning" Tom Mitchell McGraw-Hill, 1997; "A Brief Introduction to Neural Networks" D. Kriesel 2007.; "Introduction to Data Mining", Chapter 4 Pang-Ning Tan, Michael Steinbach, and Vipin Kumar 2006.; "An Introduction to Support Vector Machines for Data Mining" Robert Burbidge and Bernard Buxton 2001; "A field guide to genetic programming" Riccardo Poli, William B. Langdon and Nicholas Freitag McPhee, 2008.

Método de ensino

Theoretical classes: board + slides; Practical casses: slides + projection of exercises and examples using various software environments.

Método de avaliação

20% project number 1, 20% project number 2, 60% final exam.

Cursos