Concept

Deep learning

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PHYS-759: Nonequilibrum Physics for Complex Systems

We will discuss recent topics in nonequilibrium physics of complex living and artificial systems, ranging from collective behaviors in active matter to noise and information processing in cellular bio

MATH-804: MLSTATS

ML 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

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The Loss Landscape of Neural Networks is in general non-convex and rough, but recent mathematical results lead provide insights of practical relevance. 9 online lectures, lecturers from NYU, Stanford

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Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analy

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MICRO-723: Deep Learning for Optical Imaging

This course will focus on the practical implementation of artificial neural networks (ANN) using the open-source TensorFlow machine learning library developed by Google for Python.

PHYS-815: 7th Machine learning in HEP Summer School

The school will cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including "Multivariat

MSE-806: Spin-based devices for neuromorphic computing

The course entails a 5-days-program with lectures and exercises about spin-based computing and novel spin texture-based computing devices. An additional round table discussion and journal club session

MATH-631: Mathematical foundations of neural networks

This course is in the form of a reading course / working group. We will focus on some mathematical aspects of the theory of neural networks, including universal approximation theorems, connections to

BIO-623: State of the Art Topics in Neuroscience XII

Surprise, Reward, and Curiosity are drives of human, animal, and robot behavior. The class links theories of reinforcement learning with human behavior beyond standard notions of reward.