9.00 – 9.15: Welcome
9.15 – 10.00: Michael Felsberg, LiU. Deep visual features: Selection, fusion, and compression with applications in visual object tracking. (Download presentation)
10.00 – 10.15: Coffee
10.15 – 11.00: Lennart Ljung, LiU. The historic roots of learning in control: Stochastic approximation and recursive identification. ()
11.15-12.00: Håkan Hjalmarsson, KTH. Iterative feedback tuning (IFT) in a learning perspective. ()
12.00 – 13.15: Lunch
13.15-14.00: Torkel Glad, LiU. Reinforcement learning from an optimization viewpoint. ()
14.15-15.00: Mikael Norrlöf, ABB/LiU: Learning for improved control performance in industrial robot applications. ()
15.00-15.15: Coffee
15.15-16.00: Lennart Ljung, LiU: Influences from machine learning in today's System Identification. ()
16.15-17.00: Thomas Schön, Uppsala University. Constructing probabilistic Newton-type algorithms to learn nonlinear dynamics. ()
9.15 – 10.00: Michael Felsberg, LiU. Deep visual features: Selection, fusion, and compression with applications in visual object tracking. (Download presentation)
10.00 – 10.15: Coffee
10.15 – 11.00: Lennart Ljung, LiU. The historic roots of learning in control: Stochastic approximation and recursive identification. ()
11.15-12.00: Håkan Hjalmarsson, KTH. Iterative feedback tuning (IFT) in a learning perspective. ()
12.00 – 13.15: Lunch
13.15-14.00: Torkel Glad, LiU. Reinforcement learning from an optimization viewpoint. ()
14.15-15.00: Mikael Norrlöf, ABB/LiU: Learning for improved control performance in industrial robot applications. ()
15.00-15.15: Coffee
15.15-16.00: Lennart Ljung, LiU: Influences from machine learning in today's System Identification. ()
16.15-17.00: Thomas Schön, Uppsala University. Constructing probabilistic Newton-type algorithms to learn nonlinear dynamics. ()
