Machine Learning I

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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