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Syllabus

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Autor: Ricardo Aler
Syllabus de la asignatura: Temas que forman parte de la asignatura.

Introduction / basic concepts

Models for classification and regression
- Nearest neighbours
- Decision / regression trees and rules

Evaluation and Methodology
- Basic pipeline
- Model evaluation
- Hyper-parameter optimization

Large scale machine learning:

- MapReduce
- Spark (MLLIB / ML / Pyspark)

Methods for attributes:

- Feature selection  / dimensionality reduction
- Feature transformation

Ensembles of models:
- Bagging / Random Forests
- Boosting / Gradient Boosting

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