Introduction to network optimization and discrete optimization
Introduction to network optimization and discrete optimization
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
Basic signal processing concepts, Fourier analysis and filters. This module can
be used as a starting point or a basic refresher in elementary DSP
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization
