Machine Learning Summer School Pittsburgh 2014

机器学习在科学和工业中有许多重要的应用。现代机器学习混合了来自不同学科的见解，最明显的是人工智能、统计学和优化——传统上没有太多重叠的领域。参与者将参加由几个不同机器学习领域的专家提供的教程，这是许多学生在自己的学校没有的机会。该项目支持学生参加2014年6月16日至27日在匹兹堡卡内基梅隆大学举办的机器学习暑期学校。暑期学校强调大数据和可扩展的机器学习算法。演讲者来自学术界和工业界，在大规模数据分析方面具有丰富的经验。预计将有大约50名来自美国各地的研究生亲自参加。课程内容将通过流媒体直播和在线存档，使更多来自学术界和工业界的学生能够从暑期学校中受益。除了由顶尖研究人员提供的深入的辅导课外，暑期学校还将包括练习课程，为参与者提供大规模数据的实践经验（使用Kaggle平台和亚马逊云服务）。更广泛的影响：暑期学校为研究生提供最先进的机器学习和大数据分析知识，这是许多学生在国内机构没有的机会。因此，它不仅有助于培养新一代机器学习和大数据分析研究人员，而且还减少了想要将最先进的机器学习技术应用于社交网络分析、生物信息学等领域的研究人员的进入门槛。

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.