This class explores key climate questions through data. Students will learn to collect, clean, and analyze data, apply causal methods using Python, and communicate insights clearly. With a focus on sustainability, the course builds skills to avoid pitfalls ...
This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees
The students learn the theory and practice of basic concepts and techniques in algorithms. The course covers mathematical induction, techniques for analyzing algorithms, elementary data structures, major algorithmic paradigms such as dynamic programming, s ...
This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...
We develop, analyze and implement numerical algorithms to solve optimization problems of the form min f(x) where x is a point on a smooth manifold. To this end, we first study differential and Riemannian geometry (with a focus dictated by computational con ...
The theoretical part of this course covers classical genetics and contemporary genomics. Because bioinformatics has become important for genomic research, the course also includes practical applications to genomic analyses using Python, including group pro ...