这个课程的重点将放在与统计学习理论架构中有关的监督式学习问题。课堂一开始将回顾古典统计技术，包括如何用再生核希尔伯特空间理论理论中的正规化理论解释稀疏值多变量函数估计。接下来会详细探讨Vapnik-Chervonenkis理论及它过去是如何验证分类与回归技术，如调控网络和支持向量机原理。另外课程中挑选了一些如提升法、特点选取和多类别分类等概念的相关主题是为了加强这门课的理论部分。我们也将在课程期间分析数种学习技巧在一些领域上的应用如计算机视觉、计算机绘图、数据库搜索和时间数列分析与预测。课堂中也将会以物体辨识的神经生物学为重点，简略地讨论脑如何从经验中学习的学习理论。我们计划强调同学能够亲自参与应用和练习，以期能灵活的运用课堂中所学的各项主题。

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.