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E.g., 2016-10-25
E.g., 2016-10-25
E.g., 2016-10-25
Oct 31st 2016

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Average: 8.2 (19 votes)
Oct 31st 2016

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

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Oct 24th 2016

This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory.

Average: 8.3 (3 votes)
Oct 17th 2016

Learn the fundamentals of digital signal processing theory and discover the myriad ways DSP makes everyday life more productive and fun.

Average: 8 (2 votes)
Oct 5th 2016

Learn how to model and simulate complex and dynamic behavior in biological systems. Biological systems are dynamic, complex, and made of many parts. In the past, scientists often tried to understand them by examining each constituent part. However, this approach was unsuccessful in many cases because the parts of any complex biological system can “interact” with each other and understanding such interaction is critical.

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Aug 1st 2016

Premiers pas dans MATLAB et Octave avec un regard vers le calcul scientifique

Average: 5 (1 vote)
Mar 21st 2016

Ce cours contient les 7 premiers chapitres d'un cours donné aux étudiants bachelor de l'EPFL. Il est basé sur le livre "Introduction à l'analyse numérique", J. Rappaz M. Picasso, Ed. PPUR. Des outils de base sont décrits dans les 5 premiers chapitres. Les deux derniers chapitres abordent la question de la résolution numérique d'équations différentielles.

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Self Paced

This is an introductory course to Digital Signal Processing that can be taken at any time. The course deals with the fundamentals in addition to exploring techniques like filtering, correlation and Fourier analysis. There is an emphasis in applying DSP theory to practical problems.

Average: 5 (13 votes)