Data Programming (19138) Statistics and Data Science I (19140) Statistics and Data Science II (19141)

- Ability to use relevant machine learning concepts and methods to formulate, structure and solve practical problems involving massive or complex data. - Ability to apply basic machine learning models for prediction and decision making.

1. Introduction to Machine Learning 1.1. To explain or to predict? 1.2. Bias vs Variance 1.3. Performance evaluation 2. Unsupervised Learning 2.1. Dimensionality reduction: PCA 2.2. Clustering: k-means, hierarchical methods 3. Supervised Learning 3.1. Classification: statistical learning (Bayesian classifiers), machine learning (nearest neighbors, decision trees, random forest, gradient boosting, neural networks) 3.2. Advanced Regression: model selection, regularization tools, feature selection 4. Case Studies for all the topics

Training Activities: - Theoretical-practical classes Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group.