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The goal of this course is that the student acquires the basics of machine learning, knows some of the basic methods and learns the procedures for parameter selection and model evaluation.

Python as a programming language for machine learning. Machine learning pipeline: preprocessing, model training, parameter validation and evaluation metrics. Linear and polynomial regression models. Basic classification models: Logistic regression, decision trees. Unsupervised learning: PCA and K-means.

AF3 Theoretical practical classes AF4 Laboratory practices AF5 Tutorials AF6 Team work AF7 Student individual work AF8 Partial and final exams METHODOLOGY MD1: Class lectures by the professor with the support of computer and audiovisual media, in which the main concepts of the course are developed and complemented with bibliography. MD2: Critical reading of texts recommended by the professor of the course. MD3: Resolution of practical cases, problems, etc. .... posed by the teacher individually or in groups. MD4: Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the course, as well as case studies. MD5: Elaboration of works and reports individually or in groups.