Probability and data analysis Statistical learning Predictive modeling

1. Random number generation 2. Monte Carlo methods a. Rejection method b. Importance sampling 3. Markov Chain Monte Carlo Methods a. Metropolis Hastings algorithm b. Gibbs sampling c. Slice sampling 4. Resampling methods a. Bootstrap b. Jacknife c. Randomization tests d. Cross-validation

AF1: THEORETICAL-PRACTICAL LESSONS where the knowledge that students should acquire is presented. Students will receive class notes and basic reference texts to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems will be solved by students and workshops and evaluation tests will be held to acquire the necessary skills. AF3: INDIVIDUAL OR GROUP WORK OF THE STUDENT. AF9: FINAL EXAM where the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: CLASS THEORY. Presentations offered by the teacher in class with computer support and audiovisual media, where the main concepts of the subject are developed and materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical case studies, problems, etc. proposed by the teacher individually or in groups. MD3: TUTORIALS. Individualized assistance (individual tutorials) or group (collective tutorials) offered to students by the teacher.