Machine Learning Summer School (MLSS), Canberra 2010
Machine Learning is a foundational discipline of the Information Sciences. It combines deep theory from areas as diverse as Statistics, Mathematics, Engineering, and Information Technology with many practical and relevant real life applications. The aim of the summer school is to cover the entire spectrum from theory to practice. It is mainly targeted at research students, IT professionals, and academics from all over the world.
This school is suitable for all levels, both for people without previous knowledge in Machine Learning, and those wishing to broaden their expertise in this area. It will allow the participants to get in touch with international experts in this field. Exchange of students, joint publications and joint projects will result because of this collaboration.
For research students, the summer school provides a unique, high-quality, and intensive period of study. It is ideally suited for students currently pursuing, or intending to pursue, research in Machine Learning or related fields. Limited scholarships are available for students to cover accommodation and registration costs. If funds are available partial travel support might also be provided.
IT professionals who use Machine Learning will find that the summer school provides relevant knowledge and exposure to contemporary techniques. In addition, they will benefit by direct interaction with top-notch researchers and knowledge workers. Previous experience indicates that personnel from both the industry as well as national laboratories like CSIRO, DSTO benefit immensely from the school.
For academics, the summer school is an excellent opportunity to help getting started in research on novel topics in Machine Learning. It provides an ideal forum for networking and discussions. Academics will also benefit from interaction with IT professionals which will lead to a deeper understanding of real life problems.
Detailed information about the event can be found at MLSS2010.
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These MLSS talks are very popular with each talk having almost a few hundred views. Since we are missing the descriptions of for all the talks, we would be very happy if anyone from the ML scene could try to write them down.
We are also challenging the most ambitious to write a review of the talks for our Blog at http://blog.videolectures.net/ :)
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Can you please upload other videos too? I think that the list is not complete.
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