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Handwritten Digit Classification Using Neural Networks

This project aims to predict handwritten digit labels using Multi-Layer Perceptron (MLP) and estimating statistics on a hand digit dataset. Original and preprocessed data will be used for experiments with tools like Weka and MATLAB. Reports must follow a scientific journal style and include system descriptions, analysis, and basic experiments on MLP structure and settings. Submission guidelines include hardcopy and email submission by October 17. Marking scheme allocates points for experiments, analysis, report quality, and organization.

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Handwritten Digit Classification Using Neural Networks

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  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

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