9.520 2003春季课程：统计学习理论及应用(Statistical Learning Theory and Applications, Spring 2003)
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设计并建立一个与人类视觉系统有相同功能的系统，但不会没有耐性，而且更为精确。（图片由麻省理工学院大脑与认知科学系Poggio实验室提供。）
Designing and building a system that will function the same way as a human visual system, but without getting bored, and with a greater degree of accuracy. (Image courtesy of Poggio Laboratory, MIT Department of Brain and Cognitive Sciences.)
Designing and building a system that will function the same way as a human visual system, but without getting bored, and with a greater degree of accuracy. (Image courtesy of Poggio Laboratory, MIT Department of Brain and Cognitive Sciences.)
课程重点
支持向量机（Support vector machines）已被证明是十分有用的分类网路。司机利用支持向量机来避开行人，是这项技术在全球范围首先被广泛应用的领域之一。
Support vector machines have proven to be very useful in classification networks. These SVMs are now being used by drivers for pedestrian avoidance. This is one of the first truly universal applications of this technology.
本课程是为了计画在计算神经科学领域工作的高年级研究生开设。作业集中在一些使电脑更有效解决问题的功能。可供学生选择的专题题目是基于这领域仍未解决的问题。课程结束后，学生应当可以解决这些问题的一二，也能对其他问题架构解决方法。
This course is for upper-level graduate students who are planning careers in computational neuroscience. The assignments focus on some of the functions needed to make problem-solving more efficient for computer systems. The project topics students can choose from are based on unsolved problems in the field today. By the conclusion of this course, students should be able to solve one or two of these problems, and should be able to frame an approach to the rest of them.
Support vector machines have proven to be very useful in classification networks. These SVMs are now being used by drivers for pedestrian avoidance. This is one of the first truly universal applications of this technology.
本课程是为了计画在计算神经科学领域工作的高年级研究生开设。作业集中在一些使电脑更有效解决问题的功能。可供学生选择的专题题目是基于这领域仍未解决的问题。课程结束后，学生应当可以解决这些问题的一二，也能对其他问题架构解决方法。
This course is for upper-level graduate students who are planning careers in computational neuroscience. The assignments focus on some of the functions needed to make problem-solving more efficient for computer systems. The project topics students can choose from are based on unsolved problems in the field today. By the conclusion of this course, students should be able to solve one or two of these problems, and should be able to frame an approach to the rest of them.
课程描述
由基于稀疏资料的多变数函数逼近理论入手，从现代统计学习理论的观点关注有指导学习的问题。导出一些基本工具，如正则化包括用于回归和分类的支持向量机。用稳定性理论和VC理论推导泛化边界。讨论增强（boosting）和特征提取（feature selection）等相关问题。检视在一些领域的应用：电脑视觉、电脑图形学、文本分类和生物资讯学。课程计划包括期末专题和实作应用和练习，与课程主题描述技术的实际应用快速增长并行。
Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.
Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.
