BIO-465: Biological modeling of neural networksIn 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
FIN-418: Machine learning for financeThis course is introduces machine learning techniques for financial applications in algorithmic trading, derivatives pricing, model calibration, hedging, and risk management. The course format is hand
MATH-455: Combinatorial statisticsThe class will cover statistical models and statistical learning problems involving discrete structures. It starts with an overview of basic random graphs and discrete probability results. It then cov
MICRO-723: Deep Learning for Optical ImagingThis 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.
EE-605: Statistical Sequence ProcessingThis course discusses advanced methods extensively used for the processing, prediction, and classification of temporal (multi-dimensional and multi-channel) sequences. In this context, it also describ
BIOENG-448: Fundamentals of neuroengineeringNeuroengineering is at the frontier between neuroscience and engineering: understanding how the brain works allows developing engineering applications and therapies of high impact, while the design of
MATH-615: Gaussian free field through random walksIn this lecture series some important objects of random geometry are introduced and studied. In particular, the relation between the Gaussian free field and random walks / Brownian motions is explored
MSE-610: Non-destructive evaluation methodsBasic knowledge of the classical non-destructive testing methods as they are used today in industrial applications and the advanced (mostly imaging) technologies used for the analysis of materials and
MICRO-211: Analog circuits and systemsThis course introduces the analysis and design of linear analog circuits based on operational amplifiers. A Laplace early approach is chosen to treat important concepts such as time and frequency resp