Project 1: Classification Using Neural Networks

1. Artificial Intelligence Project 1: Classification Using Neural Networks 2008. 9. 24 Kim, Kwonill kikim@bi.snu.ac.krBiointelligence laboratory

2. Contents • Project outline • Description on the data set • Description on tools for ANN • Guide to Writing Reports • Style • Mandatory contents • Optional contents • Submission guide / Marking scheme • Demo (C) 2008, SNU Biointelligence Laboratory

3. Outline • Goal • Understand MLP deeper • Practice researching and writing a paper • Handwritten digits problem (classification) • To predict the classe labels (digits) of handwritten digit data set • Using Multi Layer Perceptron (MLP) • Estimating several statistics on the dataset • Data set • Variation of the ‘Handwritten digit data set’ • http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits (C) 2008, SNU Biointelligence Laboratory

4. Handwritten Digit Data Set (1/2) • Description • Digit database of 250 samples from 44 writers • http://archive.ics.uci.edu/ml/datasets/Pen-Based+Recognition+of+Handwritten+Digits • 16 attributes • (xt, yt), t = 1, … , 8 • 0 ~ 100 • Label (Class) • 0, 1, 2, … , 9 (C) 2008, SNU Biointelligence Laboratory

5. Handwritten Digit Data Set (2/2) • Constitution • Original data (./original) • Preprocessed data (*.arff, *.csv)  Use This!! • Total data (pendigits_total_set, 1099)= training data (pendigits_training, 749)+ test data (pendigits_test, 350) • Data description (pendigits.names) • For WEKA (*.arff) (C) 2008, SNU Biointelligence Laboratory

6. Tools for Experiments with ANN • Source codes - Choose anything!! • Free software  Weka (recommended) • MATLAB tool box (Toolboxes  Neural Network) • ANN libraries (C, C++, JAVA, …) • Web sites • http://www.cs.waikato.ac.nz/~ml/weka/ • http://www.faqs.org/faqs/ai-faq/neural-nets/part5/ (C) 2008, SNU Biointelligence Laboratory

7. Reports Style • English only!! • Scientific journal-style • How to Write A Paper in Scientific Journal Style and Format • http://abacus.bates.edu/~ganderso/biology/resources/writing/HTWsections.html (C) 2008, SNU Biointelligence Laboratory

8. Report Contents – Mandatory (1/2) • System description • Used software and running environments • Result graphs and tables • Analysis & discussion (Very Important!!) (C) 2008, SNU Biointelligence Laboratory

9. Report Contents – Mandatory (2/2) • Basic experiments • Changing # of epochs (Draw learning curve) • Various # of Hidden Units (C) 2008, SNU Biointelligence Laboratory

10. Report Contents – Optional • Various experimental settings • Normalization • Learning rates • Structure of MLP • Feature selection • Activation functions • Learning algorithm • … • Evaluation techniques • ROC curve • k-fold Crossvalidation • … (C) 2008, SNU Biointelligence Laboratory

11. Submission Guide • Due date: Oct. 17 (Fri.) 15:00 • Submit both ‘hardcopy’ and ‘email’ • Hardcopy submission to the office (301-417 ) • E-mail submission to kikim@bi.snu.ac.kr • Subject : [AI Project1 Report] Student number, Name • Length: report should be summarized within 12 pages. • If you build a program by yourself, submit the source code with comments • We are NOT interested in the accuracy and your programming skill, but your creativity and research ability. • If your major is not a C.S, team project with a C.S major student is possible (Use the class board to find your partner and notice the information of your team to TA(bhkim@bi.snu.ac.kr) by Oct. 1) (C) 2008, SNU Biointelligence Laboratory

12. Marking Scheme • 20 points for experiment & analysis • Extra 2 points for additional expriments • 10 points for report • 3 points for overall organization • Late work • - 10% per one day • Maximum 7 days (C) 2008, SNU Biointelligence Laboratory

13. References • Materials about Weka • Weka GUI guide (PPT) • Explorer guide (PDF) • Experimenter guide (PDF) (C) 2008, SNU Biointelligence Laboratory

14. WEKA Demo (C) 2008, SNU Biointelligence Laboratory

15. Matlab (C) 2008, SNU Biointelligence Laboratory

16. QnA (C) 2008, SNU Biointelligence Laboratory