REU Site: Advances of Machine Learning in Theory & Applications (AMALTHEA)

机器学习在理论和应用方面的进展（AMALTHEA）本科生研究经验（REU）网站旨在通过参与机器学习（ML）领域的研究，为多样化的本科生社区提供高质量的教育经验。机器学习的相关性和重要性并不像过去那样局限于专门的技术创新。如今，通过语音/人脸识别、信用欺诈检测、智能推荐系统等应用，它也越来越多地影响着人们的日常生活。此外，机器学习本质上是多学科的，因为它借鉴了多个学科的进展，如工程、计算、统计学、数学、物理和生物学，仅举几个主要学科。自2009年启动以来，AMALTHEA是第一个以ml为重点的REU网站之一，每年涉及10名来自广泛学科的本科生，夏季的教育经验长达10周。参与者将接触到尖端的机器学习研究，以及专业发展活动，如技术研讨会和职业相关研讨会。此外，这些参与者进行密切指导的研究，其结果将影响机器学习本身的领域，以及机器学习如何在其他科学学科中应用。在2016-2019年的时间跨度内，该项目将直接影响到30名积极进取的学生，他们中的大多数人可能没有机会获得这样的研究参与机会。该项目的重点领域是机器学习理论，以及如何将其集成并应用于重要的现实问题，从而使参与者同时接触到理论和应用。过去的项目包括应用程序，如从青蛙叫声中自动识别anuran，从犯罪地点发现犯罪网络，人-物交互识别，虚拟世界中的群体动力学建模，独立于扬声器的语音识别和车牌识别等。另一方面，过去对机器学习理论的贡献主要集中在功能数据分析、基于小波的密度估计、非线性降维、核方法和异常检测等方面。这些项目的简短视频可以在AMALTHEA的YouTube频道https://goo.gl/2JhYoF上找到。最后，关于这些主题及其成果的更多信息可以在该项目的网站http://www.amalthea-reu.org上找到。

The Advances of MAchine Learning in THEory and Applications (AMALTHEA) Research Experiences for Undergraduates (REU) Site aims to provide top quality educational experiences to a diverse community of undergraduate students through research participation in the area of Machine Learning (ML). The relevance and importance of ML is not limited to specialized technological innovations, as it was in the past. Nowadays, it also increasingly influences everyday life through its contributions to applications such as voice/face recognition, credit fraud detection, intelligent recommendation systems and many others. Furthermore, ML is inherently multi-disciplinary as it draws from advances in multiple disciplines such as engineering, computing, statistics, mathematics, physics and biology, to name a few major ones. Since its start in 2009, AMALTHEA, one of the first ML-focused REU sites, involves 10 undergraduate students per year from a broad spectrum of disciplines, and the educational experience spans 10 weeks in the summer. The participants are exposed to cutting-edge ML research, as well as professional development activities, such as technical seminars and career-related workshops. Moreover, these participants perform closely-mentored research, whose results are going to impact the field of ML itself, as well as how ML is applied in other scientific disciplines. Over the 2016-2019 time span, the project will directly impact a diverse group of 30 motivated students, the majority of which may not have access to such research participation opportunities otherwise.The project's thrust area is the theory of ML and how it can be integrated and applied to important real-life problems, hence exposing participants to both theory and applications. Past projects include applications such as automated anuran recognition from frog calls, uncovering criminal networks from crime locations, human-object interaction recognition, modelling of group dynamics in virtual worlds, speaker-independent speech recognition and license plate recognition among others. On the other hand, past contributions to the theory of ML have been steered towards topics such as functional data analysis, wavelet-based density estimation, non-linear dimensionality reduction, kernel methods and anomaly detection to name a few. Short video highlights of such projects can be found on AMALTHEA's YouTube channel located at https://goo.gl/2JhYoF. Finally, additional information about these topics and their outcomes can be found on the project's web site located at http://www.amalthea-reu.org.