Regularization Methods for Machine Learning 2016

Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can see and detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is softwares that are trained rather than programmed to solve a task. Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high- dimensional learning. Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design n

洞悉智能之运作机理及其在机器中的模拟之道，乃自古以来的夙愿，亦堪称现代科学界的一大难题。学习，及其原理与计算实现，构成了这一探索活动的核心。近期，我们首次成功开发出能够解决过去数十年被视为遥不可及的复杂任务的智能系统。现代摄像头能够识别人脸，智能手机可识别语音指令，汽车能够识别并检测行人，而自动柜员机能够自动读取支票。在这些成功故事的背后，往往是机器学习算法，即那些被训练而非编程以解决特定任务的软件。在众多现代计算学习方法中，我们专注于正则化技术，这是高维学习的关键所在。正则化方法能够以统一的方式处理一类庞大的多样化方法，同时提供设计工具以应对（n）