计算机信息技术的飞速发展带来了医学、生物学、财经和营销等诸多领域的海量数据。理解这些数据是一种挑战,这导致了统计学领域新工具的发展,并延伸到诸如数据挖掘、机器学习和生物信息学等新领域。统计学习与推理侧重研究机器学习与推理的统计特性,本课程介绍从实际应用数据中自动提取规则、模式或结构的基本理论与方法, 使学生掌握基于统计模型的建模、参数识别、模型推理方面的能力。在数据挖掘、人工智能、自然语言处理有着广泛的应用。除了学习统计学习与推理的基本理论与方法外,本课程将提供大型数据分析与建模的课程设计训练,初步掌握解决大型实际系统建模与学习问题的能力。

本科课程适合智能信息处理、模式识别、大规模数据挖掘、生物信息学等专业的硕士研究生。

 

  Statistical Learning and Inference focuses on the statistical features of machine learning and inference. This course introduces basic theory and methods for extracting rules, structures and patterns in large scale data, requiring students to master system modeling, parameter identification and model inference based on statistical models. The statistical learning methods are applicable to broad areas such as data mining, artificial intelligence and natural language processing. The course features to provide project practice on large scale data to master capability of solving large scale practical problems through modeling and learning.

The course is suitable for the master degree students working on intelligent information processing, pattern recognition, data mining and bioinformatics.
  

 

Reference Books
1.Elements of Statistical Learning, Hastie T., R. Tibshirani, and J. Fiedman, Springer, 2001
2. The Nature of Statistical Learning Theory, Vapnik, V., Springer-Verlag, New York. 1996

Elements of Statistical Learning Webpage
http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html

Statistical Learning Resources:
http://www.statistics.com/resources/
http://mathworld.wolfram.com/topics/ProbabilityandStatistics.html

More Topics:
http://www.kernel-machines.org/
http://www.csie.ntu.edu.tw/~cjlin/libsvm/

http://www.cis.hut.fi/projects/ica/