CS-448: Sublinear algorithms for big data analysisIn this course we will define rigorous mathematical models for computing on large datasets, cover main algorithmic techniques that have been developed for sublinear (e.g. faster than linear time) data
CS-302: Parallelism and concurrency in softwareFrom sensors,to smart phones,to the world's largest datacenters and supercomputers, parallelism & concurrency is ubiquitous in modern computing.There are also many forms of parallel & concurrent execu
CS-630: Fault-tolerant quantum computingThe course explains how to execute scalable algorithms on fault-tolerant quantum computers. It describes error correction used to build reliable logical operations from noisy physical operations, and
CS-202: Computer systemsThis course will teach operating systems and networks in an integrated fashion,emphasising the fundamental concepts and techniques that make their interaction possible/practical. Core lectures will be
MICRO-515: Evolutionary roboticsThe course gives an introduction to evolutionary computation, its major algorithms, applications to optimization problems (including evolution of neural networks), and application to design and contro
CS-401: Applied data analysisThis course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat
COM-406: Foundations of Data ScienceWe discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
