Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
Delves into the application of artificial intelligence in finance, exploring tools like neural networks and Bayesian techniques, successful use cases in fraud detection and robo-advisors, and the importance of interpretability in machine learning models.