Problem-Based Learning

1. Define Your Own Problem with Raw Data : Input, Method, Ouput
2. Problem : (You may discuss with your advisor or me in advance)
3. Background : Realted Studies (Show what you are going to do is worhty of being one project for this course)
4. Input: Preprocessing (How to gather? collect? clean? Miss Values? Data Format?)
Fearture Extraction?
5. Output: What do you expect the outcome? (Is the upcoming result significant or attractive? contribution? )
6. Methods (Via Weka?):
   • Classification (Choose classifiers: linear classifier, Decision Tree, Naive Bayes, k-NN, Deep Learning, ...)
   • Clustering
   • Association Rule
   • Deep Learning
   • Big-Data Problem: Cloud Computing?
   • Dimemsional Reduction Problem: Feature Selection? PCA?LDA?SVD?
   Classification (Choose classifiers, classifiers comparison)
7. Report: Paper Writing and Presentation for Conferences or Journals