Machine learning has many important applications in science and industry. Modern machine learning uses a mix of insights from different disciplines, most notably artificial intelligence, statistics and optimization - areas that traditionally have not had much overlap. The participants will take part in tutorials given by experts from several different areas of machine learning - an opportunity that many students do not have at their home institutions.
The project supports student participation in a Machine Learning Summer School to be held at Carnegie Mellon University in Pittsburgh during June 16-27, 2014. The summer school emphasizes big data and scalable machine learning algorithms. It features speakers from academia and industry with established experience in large scale data analysis. Approximately 50 graduate students from around the U.S. are expected to participate in person. The content will be streamed live as well as archived online making it possible for a much larger number of students from academia and industry to benefit from the summer school. In addition to in-depth tutorial lectures given by leading researchers, the summer school will include exercise sessions that provide the participants hands-on experience with large scale data (using the Kaggle platform and Amazon cloud services).
Broader Impact: The Summer School provides state-of-the art knowledge of machine learning and big data analytics to graduate students - an opportunity that many students do not have at their home institutions. Thus, it would not only help train an new generation of machine learning and big data analytics researchers, but also reduce the barrier to entry of researchers who want to apply state-of-the-art machine learning techniques to applications in areas such as social network analytics, bioinformatics etc.
