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.
- 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).