Department of Mathematics

Phd courses at CIM

New course suggestions

Do you have an idea about a course that would be perfect for CIM? Send your suggestion to the director of CIM, Erik Ekström.

Upcoming and previous courses

Autumn 2017

Sequential Monte Carlo methods. Contact: Fredrik Lindsten, Thomas Schön, Andreas Svensson

Reinforcement Learning: A Graduate Course (6hp). Teacher: Alexandre Proutiere. Contact: Kristiaan Pelckmans.

Mathematical Models of Social Behaviour (10hp). David Sumpter.

Numerical Optimization (10hp). Ken Mattsson, Maya Neytcheva.

Spring 2017

Statistical decision theory and Bayesian methods (5hp), Rauf Ahmad, Behrang Mahjani, Dietrich von Rosen, Tilo Wiklund, Silvelyn Zwanzig

Discrete Optimization with Application in Communication Networks (6hp), Di Yuan

Spring 2016

Numerical methods in stochastic modelling and simulations (7.5hp), Stefan Engblom

Network dynamics (6+3hp), Giacomo Como, Lund University

Matrix Computations in Statistics with Applications, Maya Neytcheva

Statistical Machine Learning (9+3hp), Thomas Schön

Autumn 2015

Advanced statistical computing (5/7.5hp), Carl Nettelblad, Behrang Mahjani, Silvelyn Zwanzig

Computational dynamics: invariant manifolds and beyond (7.5hp), Jordi-Lluís Figueras

Modelling for Combinatorial Optimisation (5hp), Jean-Noël Monette, Pierre Flener, Justin Pearson

Stochastic dynamic systems (10hp), Torsten Söderström

Foundations of Machine Learning (10hp), Kristiaan Pelckmans

Spring 2015

Introduction to Adaptive Dynamics (3hp), Claus Rüffler

Computer vision for social machines (5hp), Ginevra Castellano and Anders Hast

Introduction to Uncertainty Quantification (5hp), Per Lötstedt

Mathematical Models and Numerical Methods for Fluid Mechanics (6hp), Mattias Liefvendahl

PhD course in probability theory and statistics, (5hp), Erik Broman

Stochastic phenomena, (5hp), Svante Janson

Autumn 2014

Constraint Modelling for Solving Combinatorial Problems (5hp), Jean-Noël Monette

Mathematical and Numerical Techniques for Partial Differential Equations (10 hp), Gunilla Kreiss

Spring 2014

Numerical Linear Algebra (7.5hp), Maya Neytcheva (March-April)

Perturbation Theory and Asymptotic Expansions (5hp), Bengt Fornberg and Natasha Flyer (May-June)

Machine Learning - probabilistic techniques, Thomas Schön

Autumn 2013

Applied Mathematics, Daniel Strömbom

Stochastic control and optimization, Erik Ekström

Stochastic Dynamic Systems (7-10 hp), Torsten Söderström

Spring 2013

Mathematical Biology, David Sumpter

Mathematics of complex networks, Andrew Uzzel

Autumn 2012

Mathematical and Numerical techniques in Partial Differential Equations (15hp), Gunilla Kreiss

Spring 2012

Bayesian methods (10 hp), Silvelyn Zwanzig

Autumn 2011

Stochastic processes with applications (10hp), Takis Konstantopoulos
Numerical Linear Algebra (7.5 hp), Maya Neytcheva

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