2008年我院已成功举办

机器学习、统计学和计算机视觉中美法联合暑期讲习班

2008.06.30----2008.07.11

计算机视觉和信息科学研究院

    研讨班面向国内外所有从事计算机视觉、模式识别、机器学习、以及相关基础学科(应用数学、统计、计算机)领域的本科毕业班学生、研究生、博士后、及青年教工。我们邀请了多位活跃在计算机视觉和模式识别方面的教授和专家开设八门短期课程(具体见拟定的课程计划)，系统化地介绍计算机视觉、模式识别和机器学习的基础和前沿理论、原理、方法、最新进展。
    研讨班的目的是通过研讨班的教学与讨论，期望提升国内相关领域学者对国际研究前沿的熟悉，建立与国际相关教授与专家的交流与合作关系，同时也为研讨班学员将来的进修、留学、与就业增加机会。
  

组织委员会:

   Prof. 朱松纯, 美国洛山矶加州大学教授.
   Prof. Veronique Prinet, 中国科学院自动化研究所教授.
   Prof. Xuming He, 美国伊利诺斯大学香槟分校教授.
   Prof. Jian-Feng Yao, 雷恩大学教授.

美国教授的课程:

   Prof. Bin Yu,Department of Statistics, University of California, Berkeley,
   Prof. Ji Zhu, Department of Statistics, University of Michigan,
   Prof. Chengxiang Zhai, Department of CS, University of Illinois, Urbana Champaign,
   Prof. Yuguo Chen, Department of Statistics, University of Illinois, Urbana Champaign,
   Prof. Song-Chun Zhu, Departments of Statistics and CS, University of California, Los Angeles.

法国教授的课程:

   Prof. Bill Triggs, Laboratoire Jean Kuntzmann, Grenoble,
   Prof. Ivan Laptev, Inria, Rennes,
   Prof. Cordelia Schmid, Inria, LEAR Project, Alpes.

短期课程:

   第一周：（6月30日—7月4日）

   Short course 1:
   Machine Learning: Kernels, Boosting, Random Forest, Variable Selection
   Topics: 1.Kernel Methods I
                 2. Kernel Methods II
                 3. Random Forest and Boosting
                 4. Clustering
                 5. Variable Selection
   Professor Ji Zhu, University of Michigan

   Short course 2:
   Machine Learning: Statistical Models for Information Retrieval and Text Mining
   Topics: 1. Overview of Information Retrieval Models
                 2. Statistical Language Models for Information Retrieval
                 3. Feedback in Information Retrieval
                 4. Contextual Probabilistic Latent Semantic Analysis
                 5. Applications of Topics Models in Text Mining
                 Professor Chengxiang Zhai, University of Illinois, Urbana Champaign

   Short course 3:
   Statistics: Topics on sequential Monte Carlo and Markov Chain Monte Carlo
   Topics: 1. Introduction to Monte Carlo Methods
                 2. Sequential Importance Sampling
                 3. Sequential Monte Carlo for Hidden Markov Models
                 4. Markov Chain Monte Carlo - I
                 5. Markov Chain Monte Carlo - II
   Professor Yuguo Chen, University of Illinois, Urbana Champaign

   Short course 4:
   Vision: visual recognition and human motion reconstruction
   Topics: to be advised
   Professor Bill Triggs, Laboratoire Jean Kuntzmann (LJK) in Grenoble

周末休假计划:

    计划周末（7月5-6日）去庐山 度假。从莲花山出发，大概有3.5小时的车程。庐山因其夏季凉爽宜人的天气而闻名。

   第二周：（7月7日-7月11日）

   Short course 5:
   Machine Learning: Topics on Sparsity
   Topics: 1. Sparsity through model selection
                 2. Sparsity through L1 constrained L2 penalization (Lasso)
                 3. Theoretical results on Lasso
                 4. Group and hierarchical sparsity
                 5. Understanding the visual pathway through sparsity
   Professor Bin Yu, University of California, Berkeley

   Short course 6:
   Vision: Image features and Object recognition (Tentative)
   Topics: 1. Local features I
                 2. Local feautres II
                 3. Efficient image search
                 4. Category recognition I
                 5. Category recognition II
   Professor Cordelia Schmid, Inria, LEAR Project, Alpes.

   Short course 7:
   Vision: human action recognition
   Topics: 1. Human actions in computer vision
                      Why is it important, applications, ...
                      Why is it hard
                      Overview of existing methods and their focus
                 2. 2D, 3D structure models, motion priors,
                      tracking, applications in Graphics
                 3."Silhouette world", Active Shape Models
                      Motion History
                 4. Space-time volume representations
                      Action similarity, matching, alignment
                 5. Local methods, space-time interest points
                     action recognition "in the wild"
   Professor Ivan Laptev, Inria, Alpes.

   Short course 8:
   Vision and Learning: Manifold Learning and Image Parsing
   Topics: 1. Pursuing Manifolds in the Universe of Image Patches by Information Projection
                 2. Information Scaling and Regimes of Statistical Models of Image
                 3. Primal sketch: Integrating Textures and Structures
                 4. Active Basis Model for Object Recognition and Learning
                 5. Image Parsing with Data Driven Markov Chain Monte Carlo
                 6. Swendsen-Wang Cuts for fast MCMC computing
                 7. Stochastic Image Grammar and top-down/bottom-up inference
   Professor Song-Chun Zhu, UCLA

合作单位:

   National Science Foundation, USA,
   PRA(Sino-French Advanced Research Project),
   LIAMA (Sino-french lab, Inst. Of Automation), CAS,
   Lotus Hill Institute,
   Center for Statistics Science, AMSS, CAS of China,
   The Virtual Center for U-China Collaboration among Statisticians.