MLSS 2015 - Machine Learning Summer School
Topics/Call fo Papers
A machine learning summer school will be held at Chalmers, Gothenburg, the second largest Swedish city, in the period 14-16 April.
Information and registration:
http://www.chalmers.se/en/departments/cse/organisa...
Topics
Machine learning has increasing importance in today's society, with ever-wider application of autonomous learning systems in areas ranging from advertising to energy and finance. It also has many scientific applications, as evidence by the growth of data science as a discipline. This summer school will give thorough introductions to a number of techniques and application areas in machine learning, including.
On the theory side, topics covered will include Bayesian inference, Deep learning, Gaussian processes, Markov decision processes, Monte-Carlo methods and Reinforcement learning. The applications will include Computational Biology, Computer vision, Energy and Smart Grids, Medicine and Robotics.
Travel grants are available for students.
Speakers
Marc Deisenroth, Imperial College, UK.
Mattias Villani, Linköping University, Sweden.
Tomas Schon, Uppsala University, Sweden.
Josephine Sullivan, KTH, Sweden
Tom Heskes, Radbound University, Nimejgen, Netherlands.
Devdatt Dubhashi, Chalmers University of Technology, Sweden.
Lars Carlsson, Astra Zeneca, Sweden
Christos Dimitrakakis, Chalmers University of Technology, Sweden.
Damien Ernst, University of Liege, Belgium.
Ronald Ortner, University of Loeben, Austria
Sponsors:
- Chalmers (Computing Science Division, Energy Area of Advance, ICT Area of Advance)
- Swedish AI Society
Contact Christos Dimitrakakis chrdimi-AT-chalmers.se or Devdatt Dubhashi dubhashi-AT-chalmers.se for further information.
Information and registration:
http://www.chalmers.se/en/departments/cse/organisa...
Topics
Machine learning has increasing importance in today's society, with ever-wider application of autonomous learning systems in areas ranging from advertising to energy and finance. It also has many scientific applications, as evidence by the growth of data science as a discipline. This summer school will give thorough introductions to a number of techniques and application areas in machine learning, including.
On the theory side, topics covered will include Bayesian inference, Deep learning, Gaussian processes, Markov decision processes, Monte-Carlo methods and Reinforcement learning. The applications will include Computational Biology, Computer vision, Energy and Smart Grids, Medicine and Robotics.
Travel grants are available for students.
Speakers
Marc Deisenroth, Imperial College, UK.
Mattias Villani, Linköping University, Sweden.
Tomas Schon, Uppsala University, Sweden.
Josephine Sullivan, KTH, Sweden
Tom Heskes, Radbound University, Nimejgen, Netherlands.
Devdatt Dubhashi, Chalmers University of Technology, Sweden.
Lars Carlsson, Astra Zeneca, Sweden
Christos Dimitrakakis, Chalmers University of Technology, Sweden.
Damien Ernst, University of Liege, Belgium.
Ronald Ortner, University of Loeben, Austria
Sponsors:
- Chalmers (Computing Science Division, Energy Area of Advance, ICT Area of Advance)
- Swedish AI Society
Contact Christos Dimitrakakis chrdimi-AT-chalmers.se or Devdatt Dubhashi dubhashi-AT-chalmers.se for further information.
Other CFPs
- 2nd AIED Workshop on Simulated Learners
- 11th Annual LearnLab Summer School
- 9th WISTP International Conference on Information Security Theory and Practice
- 11st International Symposium on Wireless sensor network Technologies and Applications (WTA 2015)
- 2015 International Symposium on Real-time Natural User Interface and Natural User eXperience (RN2 2015)
Last modified: 2015-02-21 16:00:00