Vu Dinh (Ewing 312)
  Class times: MWF 9:05am-9:55am, ISE Lab 222
  Office hours: Tuesday 3pm-4pm and Wednesday 4:30pm-5:30pm, Ewing Hall 312, or by appointment.

• Syllabus

• Reference
  • An Introduction to Statistical Learning, James, Witten, Hastie, and Tibshirani.
    The pdf of the book is available for free at http://www-bcf.usc.edu/~gareth/ISL/
  
  
  • The course provides an introduction to the fundamental techniques used in data science. The main objective of the course is to develop a good practical knowledge and a mathematical understanding of the common tools that are used to analyze modern datasets. The course also provides hands-on experience in data analysis through practical homework and class projects.

• Lectures
  • Lecture 1 (Wed, 02/13).
  • Lecture 2 (Fri, 02/15).
    Note: PAC learning and generalization error bounds
    Simulations:
    
    • Mock dataset: quadratic boundary.
    • Fitting by SVM.
    • Code for generating mock examples.
  • Lecture 3 (Mon, 02/18).
  • Lecture 4 (Fri, 02/22).
    Note: Generalization bounds using covering number
    
  • Lecture 5 (Mon, 02/25).
  • Lecture 6 (Wed, 02/27).
  • Lecture 7 (Mon, 03/04).
    Lab 1
    
  • Lecture 8 (Wed, 03/06).
  • Lecture 9 (Fri, 03/08).
  • Lecture 10 (Mon, 03/11).
  • Lecture 11 (Wed, 03/13).
  • Lecture 12 (Fri, 03/15).
  • Lecture 13 (Mon, 03/18).
    Note: Model consistency of lasso
    Further readings:
    
    • Zhao and Yu (2006).
    • Wainright (2009).
    • Sparsity, the lasso, and friends (Ryan Tibshirani).
  • Lecture 14 (Wed, 03/20).
  • Lecture 15 (Fri, 03/22).
  • Lecture 18 (Fri, 03/29).
  • Lecture 19 (Mon, 04/08).
  • Lecture 20 (Wed, 04/10).
    Sample codes:
    
    • Dogs vs. Cats .
    • MNIST.
  • Lecture 21 (Fri, 04/12).
  • Lecture 22 (Mon, 04/15).
  • Lecture 23 (Wed, 04/17).
  • Lecture 24 (Fri, 04/19).
  • Lecture 25 (Mon, 04/22).
  • Lecture 26 (Wed, 04/24).
  • Lecture 27 (Fri, 04/26).
  • Lecture 28 (Mon, 04/29).
  • Lecture 29 (Wed, 05/01).
  • Lecture 30 (Fri, 05/03).
  • Lecture 31: Convolutional neural networks (Mon, 05/06).
  
  
  
  • Homework
    • Homework 1 (due 03/08).
      hw1.csv
      
    • Homework 2 (due 03/29).
      
    • Homework 3 (due 05/10).