1. INTRODUCTION
1.1. Machine Learning, Knowledge Discovery, Data Mining
1.2. Genetic Algorithms, Explanation-Based Learning
1.3. Multi-agent 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 OBSERVATION)
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. Multi-relational 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
La nota final (NF) s'obté del promig ponderat de la nota de pràctiques (NP), la nota de seminaris(NS), la nota de treballs (NW) i la nota de proves escrites (NE). El càlcul es farà de la manera següent:
NF = 0,3*NP + 0,2*NS + 0,3*NW + 0,2*NE
Aquest promig es farà sempre i quant es tingui que NP>5, NS>5, NW>5 i NE >5.