BOOK READING_1_Pattern Recognition And Machine Learning

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BOOK READING_1_Pattern Recognition And Machine Learning

【书本】Christopher M. Bishop,《Pattern Recognition and Machine Learning》, 2006年出版.

【时间】每周五早上10:30.

【地点】科研2#400

【相关的网络资源】

1. Christopher M. Bishop维护的BOOK WEB PAGE；与这边书相关的两个报告视频：

(1) Introduction To Bayesian Inference;

(2) Graphical Models and Variational Methods.

2. INRIA针对这本书的建立的Reading Group.

3. Song-Chun Zhu, UCLA,Stat Model and Learning. ANDPattern Recognition and Machine Learning.

4.Sargur N. Srihari,Machine Learning and Probabilistic Graphical Models Course. 内容与《PRML》相近，且配有video lectures。

Sargur N. Srihari,Introduction to Pattern Recognition.内容编排主要按照R. O. Duda的《Pattern Classification 2nd. Edition》。

5. Yuan (Alan) Qi, Purdue University, 2009-2012,Statistical Machine Learning.

6. Glossary of Data Modeling, Training, Tutorial. 包含统计学中各种"鲜活"的例子和demo.

7. Andrew Ng. Stanford, CS229 Machine Learning. 课程体系完整，有视频(163公开课)、lecture notes、补充材料、Student Projects, 资料非常齐全.

8. Fei Sha, (1) Selected topics in machine learning, 包括：structured predictions, latent variable modeling, distance metric learning, transfer learning, deep architecture, compressed sensing. (2) A tutorialabout parameter estimation, model selection, probability graph models.

9. 统计之都. 国内较好的一个统计学相关的网站，包括各种相关的学术新闻、统计模型、经典理论、典故、人物事迹等。

10. David MacKay,Information Theory, Pattern Recognition and Neural Networks. lecture video. Information Theory (MLSS2009).

11. Mehryar Mohri. Foundations of Machine Learning. 不错的资料，新书《Foundations of Machine Learning》, MIT Press, 2012,即将面世。

12. Yishay Mansour, Tel Aviv University. Machine Learning: Foundations(Fall 2010/2011). Lecture Notes写的比较好。

13. Shai Shalev-Shwartz (HUJI),Introduction to Machine Learning.lecture notes相当完整，简直就是一本书.

14. Maria Florina Balcan (1)Machine Learning Theory. Boosting, PAC Model, Bounds, Semi-supervised Learning, Kernels, Fourier-based Learning, Unlabeled data in the Learning Process, Active Learning, The weighted majority algorithm, et al. (2)Connections between Learning, Game theory, and Optimization, Fall, 2010.

15.Avrim Blum, CMU, 15-859(B), Spring, 2009. Machine Learning Theory. PAC, Mistake-bound model, Winnow Algorithm, VC-Dimension, Margin, Cryptographic hardness results, Fourier-based algorithms, Membership query algorithms, Learning finite-state environments, offline->online optimization, Bandit problems, MDPs and Reinforcement learning.

16.Peter Barlett, UCBerkeley, Stat 241B, 2008,Statistical learning theory. Minimax Risk, Soft-margin SVMs, Convex loss versus 0-1 loss, Adaboost, Concentration inequalities, Glivenko-Cantelli classes and Rademacher averges, VC-Dimension, Online bandit problems, Universal portfolios.

17. Max Welling, Machine Learning Class notes, 非常不错。

18. 张兆翔. 北航，http://irip.buaa.edu.cn/~zxzhang/Teaching.html

Chapter 1 Introduction

报告人：苏松志

时 间1：2012-04-06

时 间2：2012-04-13

Chapter 2 Probability Distribution

报告人：蔡国榕

时 间：2012-04-20

Chapter 3 Linear Models for Regression

苏松志

Demo: Bias and Variance Trade-off

[ 注1]page.159的Figure 3.10和Figure 3.11貌似是错误的！（害我折腾到深夜，网络资源中各位大牛的ppt竟然都照搬书本）.

Chapter 4 Linear Models for Classification

张洪博

Chapter 6 Kernel Methods

吕艳萍

http://mi.eng.cam.ac.uk/~at315/MVRVM.htm

OpenKernel Libraryhttp://www.openkernel.org/

CVPR 2012 Tutorial, All you want to know about Gaussian Processes. by Raquel Urtasun, and Neil Lawrence

Chapter 7 Sparse Kernel Machines

吕艳萍

ch8：张苗辉

ch9：杨柳

ch11：陈思

ch14：曹海