Pattern Recognition (Fall 2020)

Administrative Matters

Instructor: Dr. Ying Shen (yingshen@tongji.edu.cn)

Evaluation: assignments (30%), project (60%), participation (10%)

Latest Notices

When you submit your homework/project, pleas compress all the files into a .zip file named like "ID_name_assignment1.zip"

Lecture Slides

	Slides	Related Materials
	Introduction Data Tutorial 01 Tutorial 02	1. UCI Machine Learning Repository 2. A tutorial on Principal Components Analysis 3. Feature selection techniques in bioinformatics
	Model evaluation and selection
	Linear model Tutorial 03 Lab 1 Assignment 1 due date: Oct. 6	fisherLDAExperiment.m
	Decision Tree Tutorial 04	Decision tree implemented in matlab code Source codes directory
	Neural network Assignment 2 due date:
	SVM Tutorial 05 Lab 2
	Naive Bayes Tutorial 07
	Ensumble Learning
	Clustering Analysis Tutorial 06 Assignment 3 due date:
	Dimension Reduction
	*Matlab Tutorial Examples for Matlab Tutorial	A matlab tutorial document

Assignments

Notes:

1. Compress all files into a .zip file whose name is composed of student name and ID. (such as "ID_name_assignment1.zip")

2. Plagiarism is forbidden and resubmission is not allowed.

3. For the programming assignments, you can use any programming language as you like.

4. All the documents you hand in, including comments in the source codes, should be in English.

5. Send your solutions to prtjsse@163.com

Marks

Final Projects

Notes:

1. Compress all files into a .rar or .zip file whose name is composed of student name and ID (such as "ID_name_project.zip").

2. All the documents you hand in should be in English.

Requirement details for the program and the report:

Project contents

You can choose any related topics which you are interested in as your final project.

The team size should not be greater than 3 students.

Program (25 points)

You should submit your program for the final project and a readme file with instructions on how to run the program.

Report (35 points)

Your report should be written in English and contain the following contents:

1. Describe the problem you want to solve;

2. A survey on the selected topic and related methds;

3. The structure (or workflow) and functionality of your program;

4. Performance evaluation;

5. Advantages and disadvantages of your method;

Marking

Program:

Origninality of the selected topic or applied method (published since 2010) (5');

Performance (8')

Complexity(7')

Report:

1. (5'); 2. (8'); 3.(5'); 4. (7'); 5. (3'); Clarity (2')

Main References

《机器学习》

周志华

清华大学出版社

Other Related Materials

Pattern Classification

Richard O. Duda, Peter E. Hart, David G. Stork

模式识别

张学工

清华大学出版社

Created on: Sep. 11, 2018

Last updated on: Dec. 25, 2019