Summer School 2019

Course Contents

Probability Theory and Stochastic Processes

Probability space, σ-field, random variables, moments, conditional expectations, filtrations, martingales, Markov processes, Markov Chains

Markov Chain Monte Carlo Methods

Optimization with Essential Real Analysis

Basic analyis

Frechet.Gateuax/directional derivatives, first and second order conditions for optimality, McShane proof of KKT conditions

Algorithms for unconstrained optimization: Gradient, conjugate gradient, Newton, quasi-Newton (sketch)

Algorithms for constrained optimization: penalty and barrier functions, projected and reduced gradient, primal-dual, cutting plane (sketch)

Subgradient methods, discrete optimization

Optimization Methods for Machine Learning

Stochastic Optimization

Optimal Transport Problems in Machine Learning

Data Assimilation


Summer School 2019

  Programme Schedule

  Speakers

  Organizing Committee

  Poster







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