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Lecture notes

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Autor: Ricardo Aler
This section contains the slides used during the lectures, each one with some orientation of what is explained in class.

Introduction to Machine Learning

  • LN-G-001. Guide ( PDF )
  • LN-F-001. Presentation ( PDF )
    The subject is introduced by illustrating practical applications and explaining the three main concepts: tasks, models, and algorithms.

 

Models: KNN and decision trees

  • LN-G-002. Guide ( PDF )
  • LN-F-002. Presentation ( PDF ).
    Two simple but powerful machine learning models are introduce: K-nearest neighbour and decision trees, both for classification and regression.

 

Model evaluation

  • LN-G-003. Guide ( PDF )
  • LN-F-003. Presentation ( PDF )
    The importance of evaluating models is discussed, and also several methods and measures for model evaluation.

 

Large Scale Machine Learning: MapReduce and Spark

  • LN-G-004. Guide ( PDF )
  • LN-F-004.1. MapReduce Presentation ( PDF )
    MapReduce is introduced as a data parallelism programming model.
  • LN-F-004.2. Spark Presentation ( PDF )
    Spark is introduced as a data parallelism programming model, that solves some issues of MapReduce.

 

Attribute selection and transformation

  • LN-G-005. Guide ( PDF )
  • LN-F-005. Presentation ( PDF ) This lecture explains methods for selecting (filter, wrapper, ...) and transforming attributes (PCA, Random Projections).

 

Ensembles

  • LN-G-006. Guide ( PDF )
  • LN-F-006. Presentation ( PDF)
    This lecture explains how to improve models by means of ensemble techniques (Bagging and Boosting).
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