Learn to optimize on smooth, nonlinear spaces: Join us to build your foundations (starting at "what is a manifold?") and confidently implement your first algorithm (Riemannian gradient descent).
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.
Introduction to linear optimization, duality and the simplex algorithm.
Introduction to linear optimization, duality and the simplex algorithm.
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.
