The use of observations to automatically improve the capabilities of programs has been a long standing challenge since the invention of the computer. Machine learning strives to achieve this goal using techniques from diverse areas such as computer science, engineering, mathematics, and statistics.
Rapid progress in machine learning has made it the method of choice for many applications in areas such as business intelligence, computational biology, computational finance, computer vision, information retrieval, natural language processing and other areas of science and engineering. The summer school aims to bring both the theory and practice of machine learning to research students, researchers as well as professionals who wish to understand and apply machine learning.
Participants will get the opportunity to interact with leading experts in the field and potentially form collaborations with other participants. It is suitable for those who wish to learn about the area as well as those who wish to broaden their expertise. For research students, the summer school provides an intensive period of study, appropriate for those doing research in machine learning or related application areas. For academics and researchers, the summer school provides an opportunity to learn about new techniques and network with others with similar interests. For professionals who use machine learning, this is an opportunity to learn the state of the art techniques from leading experts in the area.
The summer school is part of the machine learning summer school series started in 2002. It is co-organized by Institute for Infocomm Research, National Infocomm Australia (NICTA), National University of Singapore (NUS), and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2) with generous support from the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development, Office of Naval Research Global, SAS and the Lee Foundation.