CS4341 Introduction to Artificial Intelligence 
Project 4 - B 2003

PROF. CAROLINA RUIZ 

DUE DATE: Tuesday, December 16, 9:00 pm. 

• Project Description
• Project Assignment
• Report Submission and Due Date
• Grading Criteria
• Graduate Credit

For this project, you are allowed to work in groups of 2 students or individually.

• Assignment: Complete Part I of the assignment described in the data and code webpage (under "Documentation") by Tom Mitchell. Include answers to the all the questions in that assignment in your written report. For your convenience, here is a PDF version of the code documentation and of Mitchell's assignment. If you don't find the "xv" command on the ccc machines, use the "xloadimage" or the "gimp" command instead.

• Code: You can use
  • the C code for the neural network error backpropagation algorithm from Chapter 4 of the Mitchell's textbook, or
  • your own code.
  Your code must run on the CS or CCC Unix machines. Make sure that the code you use correctly computes the error function of the neural net when more than one output node is used. For that you need to read the code and understand it in detail.

• Training/Test Instances: Use the "quarter"-size collection of pictures provided at the CMU site. You can find copies of those pictures in our course public directory /cs/cs4341/Proj4/faces_4/ on the CCC machines.

  Also, thanks to Nathan Sheldon, here are the lists with the appropriate (not CMU) directory paths. I also left a copy of that file in /cs/cs4341/Proj4/proj4_lists.txt Furthermore, Nick Pinney split this file into parts. Here is his proj4_lists.tar.gz tar file. Thanks Nick. I untared this file in the /cs/cs4341/Proj4/ directory. So the directory currently contains the following files:

    all_test1.list
    all_test2.list
    all_train.list
    proj4_lists.txt
    straighteven_test1.list
    straighteven_test2.list
    straighteven_train.list
    straightrnd_test1.list
    straightrnd_test2.list
    straightrnd_train.list
  

• If you develop your own error backpropagation code, you should submit the source code of your program documented using the Departmental Documentation Standard.

• A file proj4_ann.txt with your written report for this part of the assignment. Your report should discuss the following issues:
  1. answers to all questions in Part I of Mitchell's assignment (for your convenience, here is a PDF version of the code documentation and of Mitchell's assignment),
  2. adaptation of the code (if any) or a description of your own code,
  3. the experiments you ran with the system,
  4. the topology (number of units in each hidden layer), number of iterations of the error backpropagation algorithm, and all other input parameters that you used for each of your experiments as well as the output printed out by the error backpropagation code, including the accuracy of each of the neural nets,
  5. strengths and the weaknesses of the system.
  Your report should also include a short user manual explaining how to install, run, and use your system (if different from the CMU package).

Sunglasses	(Q1-Q4)		20 points

Obtaining results	3
Q4:			
Code Modifications	5
Classification Accuracy	5
# of Epochs		5
Validation set		1
Test set		1

Face Recognition (Q5-Q8)	30 points

Obtaining results	3
Q8:                     7
     
Q7:
code modifications	8
#output nodes and 
output convention	8
Class Accuracy		1
# Epochs		1
Validation set		1
Test set		1

Pose Recognition (Q9-Q11)	30 points

Obtaining results	10
Code modification	6
Output endoding		6
# epochs		1
Validation set		1
Test set		1

Visualization	(Q12-Q13)	20 points 
Q13(a)			10
Q13(b)			10

Report				20 points
Q2-Q4			10
Q5			10

TOTAL:                         120 points