Here you will find all the recordings and material of the courses held at Datalab-ICMAT.
Introduction to Machine Learning
Chapter 2. Advanced Linear Regression models.
Chapter 3. Linear classification models.
Chapter 4. Tree based algorithms for Classification and Regression.
Chapter 5. Probabilistic Graphical Models.
Chapter 6. Support Vector Machines.
Chapter 7. Deep learning.
Chapter 8. Intro to unsupervised learning.
Chapter 9. Deep reinforcement learning.
Chapter 10. Ethics and security of ML.
Bayesian Data Science
Chapter 1. Key concepts through basic models.
Chapter 2. Overview of Bayesian computational methods.
Chapter 3. Intro to MCMC. From Gibbs to Hamilton.
Chapter 4. Computations for Bayesian Decision Analysis.
Chapter 5. Large scale Bayesian inference.
Chapter 6. Machine Learning models from a Bayesian perspective.
6.1 Bayesian neural nets and deep learning