attern Recognition

Notices:

  1. (Sep 24) Course page is online.
  2. (Oct 11) Project description is available.
  3. (Oct 18) Assignment 1 is available.
  4. (Jan 13) Final Grading, for assignment 1, assignment 2, assignment 3 and course project, is available.

Information| Schedule | Assignment | Resources

INTRODUCTION TO THE COURSE

Pattern Recognition is an important topic and finds applications in diverse areas such as healthcare, education, agriculture, environment, and transportation. It is closely associated with topics like machine learning and data mining.

The primary aim of this course is to attract the reader towards pattern recognition and provide a platform for understanding the basics and gaining an insight into several important topics associated with machine recognition. As a course taught for the first-year graduates, this class deals with the fundamentals of characterizing and recognizing patterns and features of interest of numerical data.

INFORMATION
Instructor Dr.Shuang LIANG
Office Room 314, Jishi Building, Jiading Campus
Email sherrill.leung@gmail.com

TA Chen ZHANG
Office Room 316, Jishi Building
Tel 137-9549-7153
QQ 695129095
Email isaackinger@gmail.com

Times and Locations

  • 18:30-21:05, Thursday
  • Room 334, Jishi Building, Jiading Campus
  • Office Hours

  • 16:00-17:00, Friday
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    SCHEDULE

    Course Outline

  • Pattern recognition systems
  • Classification
  • Feature selection
  • Clustering
  • Matching
  • Other topics if possible
  • Course Schedule

    Schedule Contents Slides
    Sep 13 Introduction pdf
    Sep 20 Pattern Recognition Systems pdf
    Sep 27 prerequisites pdf
    Sep 29 (substitute for Oct 04) Bayesian Classifier pdf
    Oct 11 Bayesian Decision Theory pdf
    Oct 18 Bayesian Decision Theory under Gaussian pdf
    Oct 25 Parametric Models pdf
    Nov 1 Non-Parametric Models pdf
    Nov 8 Bayesian Belif Networks pdf
    Nov 15, 22 Hidden Markov Models
    References: Website, Example in Chinese
    pdf
    Nov 29 Undirected Graphical Model pdf
    Dec 20 Feature Reduction and Selection pdf

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    ASSIGNMENT
    PROJECT

    The purpose of the project is to enable the students to get some hands-on experience in the design, implementation and evaluation of pattern recognition algorithms by applying them to real-world problems. You are encouraged to relate your research interests and research topics for the project. You can use your own data for your project topic, or download data sets from open data resources. In either case, you should get prior approaval before starting your project.

    You are free to use any programming language and any opensource toolkit. You can write the codes yourself or use any code that is available in the public domain. In case you use somebody else's code, you are required to properly cite its source and know the details of the algorithms that the code implements.

    You are required to work in groups of two or three, and submit a project proposal, a final report written in a conference paper format, and make a presentation during the mid-term and final weeks. When preparing your report, please use the IEEE conference format. Tentative schedule of the project is as follows:

  • Project proposal (due Nov 15, 2012): Submit a 1-2 page proposal that describes the problem you would like to tackle, objective of the study, background of the problem, related work, etc. Also provide a short list of related references. (This could contribute to the "introduction" part of your final report)
  • Mid-term progress presentation (Dec 6, 2012): Make a 15 minute presentation about your progress with the project, such as the propposed algorithms, hardware/software tools and data that you plan to utilize, and the evaluation strategies that you plan to use. Also provide plans for the rest of the semester. A mid-term progress report is not compulsory, but encouraged and will be bonused. (This could contribute to the "methodology" part of your final report)
  • Final report: Submit a readable and well-organized report that provides proper motivation for the task, proper citation and discussion of related literature, proper explanation of the details of the approach and implementation strategies, proper performance evaluation, and detailed discussion of the results. Highlight your contributions and conclusions. Also submit well-documented software with your report.
  • Presentation: Make a 20 minute presentation of your work to the class. The presenter should not be the same as that of the mid-term progress presentation. Each student is expected to attend all presentations. Each team member should also provide a written description of her/his own contributions to the project.

  • Grading Policy

    Class participation

    5%

    Please do NOT be absent for more than 5 times, otherwise you will fail.

    Assignment x 3

    45%

    Individual homework

    Project

    50%

    Teamwork by 2 or 3 people

    Bonus

    10%

    For being active in class

    Plagiarism is not allowed!

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    RESOURCES
  • Matlab tutorial
  • Text

    R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification,

    2nd edition, John Wiley & Sons, Inc., 2000.

    S.Theodoridis, K. Koutroumbas, Pattern Recognition,

    3rd edition, Academic Press, 2006.

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