Here is a list of some courses I’ve delivered:
- A long intro course to Machine Learning delivered at the National Statistical Institute: David Ríos Insua, Victor Gallego Alcalá, Roi Naveiro Flores and Alberto Torres Barrán.
- An intro short course to Bayesian methods in AI: David Ríos Insua and Roi Naveiro Flores
- An intro short course to Machine Learning: David Ríos Insua and Roi Naveiro Flores.
A course in Risk Analysis delivered at Aalto (and Shanghai Science Tech)
- Lecture 0: Intro
- Lecture 1: Problem Structuring. IDs
- Lecture 2: Modeling beliefs
- Lecture 3: Modelling preferences under uncertainty. Utilities
- Lecture 4: Decision analytic computations
- Lecture 5: Framework for risk analysis, with applications
- Lecture 6: Noncooperative games
- Lecture 7: Adversarial risk analysis
A course in Introduction to Machine Learning at ICMAT
- Course 0: Organization
- Course 1: General Intro
- Course 2: Regression models
- Course 3: Classification. Basic concepts
- Course 4: Tree based algorithms for Regression and Classification
- Course 5: Probabililisic Graphical Models
- Course 6. Support Vector Machines
- Course 7: Neural nets and deep learning
- Course 8: Unsupervised learning
- Course 9: Reinforcement learning
A course in Bayasian Data Science al ICMAT
- Session 1: Book: What If? Miguel Hernán. Chapter 1: A definition of causal effects
- Session 2: Book: What If? Miguel Hernán. Chapter 2: Randomized experiments
- Session 3: Book: What If? Miguel Hernán. Chapter 3: Observational Studies
- Session 4: Book: What If? Miguel Hernán. Chapter 4: Effect modification
- Session 5: Book: What If? Miguel Hernán. Chapter 5: Interaction
- Session 6: Book: What If? Miguel Hernán. Chapter 6: Graphical Representation of causal effects
- Session 7: Book: What If? Miguel Hernán. Chapter 8: Selection Bias
- Session 8: Book: What If? Miguel Hernán. Chapter 7: Confounding
- Session 9: Quantum information tools that deal with causality. The session is based on: [1], [2], [3]
- Session 10: Book: What If? Miguel Hernán. Chapter 9: Measurement bias
- Session 11: Book: What If? Miguel Hernán. Chapter 10: Random Variability
- Session 12: Casually (and causally?) playing with a large health database.
- Session 13: Mendelian Randomization: Using genetic variability as an instrumental variable in Causal Inference. Lecturer: Fátima Sánchez Cabo