We are pleased to announce the first hands-on Machine Learning workshop using the JEDI format: http://www.cosmology.org.za/ml-jedi/
The ethos of the JEDI workshops (jedi.saao.ac.za) is to transfer skills primarily by working on real-world research problems in small groups, rather than through lectures. In this workshop we will introduce machine learning for big data problems and cover some algorithms and existing packages for machine learning. Apart from an industry keynote and a panel discussion, the majority of the workshop will be dedicated to working on real machine learning problems in teams. Participants will be free to choose a problem of their choice from a list of selected problems and teams will form dynamically to attack the problems. Some of the problems will come from www.kaggle.com for which the teams have the option to compete for prizes.
Participation will be limited to 25, selected primarily on merit and fit. The workshop will take place over two weekends, separated by two weeks (12/13 April and 26 April 2014), to allow teams to make significant progress in between. A prize will be presented to the team who make the most progress at the close of the workshop. Teams will be encouraged to continue working on the problem after the workshop, allowing participants to cement their new research networks and potentially leading to a publication.
The target groups are graduate and advanced undergraduate students in science and engineering, as well as industry professionals wanting to gain real-world experience and skills in the exciting field of big data analytics, machine learning and artificial intelligence. Sample example problems include loan default prediction, twitter sentiment analysis, galaxy image classification and reconstruction of neural wiring.
If you are interested in attending please register at the main website http://www.cosmology.org.za/ml-jedi/registration/ before 15 March 2014.
Since we expect the workshop to be heavily oversubscribed it is important to include a brief rationale for what you hope to gain from attending and why you think we should choose you. Experience in coding or machine learning, data analysis etc.. would definitely be a benefit, as would enthusiasm, leadership or startup experience.