The course curriculum covers the fundamental topics in machine learning, including supervised learning, linear models, Bayesian decision theory, decision trees, neural networks, collaborative filtering and their applications in subdomains of cognitive science, including natural language processing and vision The students will be able to design and run machine learning models by using the knowledge gained during the lectures.