MATH-804: MLSTATSML for predictive modeling is important in both industry and research. We join experts from stats and math to shed light on particular aspects of the theory and interpretability of DL. We discuss the
MGT-492: Data science and machine learning IThis class provides a hands-on introduction to data science and machine learning topics, exploring areas such as data acquisition and cleaning, regression, classification, clustering, neural networks,
CS-422: Database systemsThis course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students
BIOENG-450: In silico neuroscience"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.
MATH-425: Spatial statisticsIn this course we will focus on stochastic approaches for modelling phenomena taking place in multivariate spaces. Our main focus will be on random field models and on statistical methods for model-ba