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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


Last modified: Monday, 18 April 2022, 2:23 PM