1. INTRODUCTION
1.1. Machine Learning, Knowledge Discovery, Data Mining
1.2. Genetic Algorithms, Explanation-Based Learning
1.3. Multiagent Learning
1.4. Statistics (Regression, Monte Carlo, Clustering)
1.5. Challenges
2. PREPROCESSING
2.1. Sampling
2.2. Feature Extraction, Feature Selection, Feature generation
2.3. Dimension Reduction (Principal Component Analysis)
3. PREDICTIVE LEARNING (FROM OBSERVARTION)
3.1. Bayesian Classifier
3.2. Induction of Decision Trees
3.3. Classification Rule Induction
3.4. Classifier Evaluation
4. DESCRIPTIVE LEARNING
4.1. Subgroup Discovery
4.2. Associative Rule Induction
5. STRUCTURED DATA LEARNING
5.1. Data series
5.2. Multirelational data: Inductive Logic Programing, Itemsets
6. STATISTICAL LEARNING METHODS
6.1. Neural Networks
6.2. Instance-based Learning
6.3. Bayesian Networks
6.4. Support Vector Machine
7. REINFORCEMENT LEARNING
Average between practical works (P) and job (J), if. P>5 and J>5. The average will be computed as follows:
NF = 0,5*P + 0,5*J
Criteris específics de la nota «No Presentat»:
Whenever the student delivers either some practical work or the job, the qualification will be other than No Presentat.