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EE-618: Theory and Methods for Reinforcement Learning

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MICRO-513: Signal processing for functional brain imaging

Computational methods for the analysis of human brain imaging data

ENV-400: Air pollution and climate change

A survey course describing the origins of air pollution and climate change

MGT-618: Computational research methods for social sciences

The objective of this course is to introduce doctoral students to computational methods for data-driven research in the social sciences.

MATH-435: Bayesian Computation

This course aims at giving a broad overview of Bayesian inference, highlighting how the basic Bayesian paradigm proceeds, and the various methods that can be used to deal with the computational issues

MGT-492: Data science and machine learning I

This 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,

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

BIO-465: Biological modeling of neural networks

In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by determ

EE-802: Mathematical guarantees of data-driven control

Students learn about advanced topics in data-driven control theory. Students also learn to present and analyze scientific work.

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.

CS-434: Unsupervised & reinforcement learning in neural networks

Learning is observable in animal and human behavior, but learning is also a topic of computer science. This course links algorithms from machine learning with biological phenomena of synaptic plastic