Data Analysis (Fall 2015)

Course Slides:

  • 09/17: course introduction (slides)

  • 09/24: basic concept and naive bayes (slides)

  • 10/01: basic model evaluation (including parameter tuning) and k-nearest neighbors (slides)

  • 10/08: A tutorial to Matlab (slides)

  • 10/15: A tutorial to Matlab (Data) (Code)

  • 10/22: Decision Tree (slides)

  • 10/29: Decision Tree and linear regression(slides)

  • 11/05: Ridge Regression and LASSO

  • 11/12: Midterm Exam

  • 11/19: Clustering (slides)

  • 11/26: Clustering

  • 12/03: Dimension Reduction (PCA) (slides)

  • 12/10: Dimension Reduction (LDA)

  • 12/17: Support Vector Machine (slides)

  • 12/24: Support Vector Machine

  • 12/31: Questions for final project

  • 01/07: Final project presentation

  • 01/14: Final project presentation

Project:

  • Demo code (download)

  • Face Recognition Data set (download)

  • Hand Digit Recognition Data set (download)

  • Text Mining Data set (download)

  • Regression Data set (download)

Exams:

  • Mid-term exam

    • previous mid-term exam(download)

  • Final project