PKU

Statistical Learning (统计学习)
Spring 2014

Course Information

Notice on Poster Requirement!

Synopsis (摘要)

This course is open to graduates and senior undergraduates in applied mathematics, statistics, and engineering who are involved in learning from data. It covers some topics statistical learning, featured with several in-class projects in computational advertisement, bioinformatics, and social networks, etc.
Prerequisite: linear algebra, basic probability and multivariate statistics, convex optimization; familiarity with R and Matlab (better enhanced by C/++).

Reference (参考教材)

The Elements of Statistical Learning. 2nd Ed. By Hastie, Tibshirani, and Friedman

Instructors:

Yuan Yao

Time and Place:

Tuesday 3:10-6:00pm;
Online public lecture: ebanshu
The second week: 理教 412 (classroom may change!)
2 hour lectures plus 1 hour discussion

Homework and Projects:

Irregular assigned homeworks with projects, and a final major project. No final exam.

Teaching Assistant (助教):

Xiong, Jiechao Email: xjctzdyh (add "AT 126 DOT com" afterwards)

Schedule (时间表)

Date Topic Instructor Scriber
02/25/2014, Tue Lecture 01: Introduction
Y.Y.
03/08/2014, Sat Lecture 02: Overview of Supervised Learning
Jinzhu Jia
03/18/2014, Tue Lecture 03: Linear Regression
    [Homework 1]:
  • Homework 1 [pdf]. Deadline: 03/25/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
03/25/2014, Tue Lecture 04: Linear Classification
    [Homework 2]:
  • Homework 2 [pdf]. Deadline: 04/01/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
04/01/2014, Tue Lecture 05: Application Studies: PCI Operation Effect Prediction and Computational Ad
Y.Y.;
Qing WANG
04/08/2014, Tue Lecture 06: Basis Expansion and Regularization
    [Homework 3]:
  • Homework 3 [pdf]. Deadline: 04/15/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
HUANG, Dongming; WANG, Kaizheng
04/15/2014, Tue Lecture 07: Kernel Smoothing and Local Polynomial Regression
    [Homework 4]:
  • Homework 4 [pdf]. Deadline: 04/22/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
04/22/2014, Tue Lecture 08: Graphical Models
Y.Y.
04/29/2014, Tue Lecture 09: Model Assessment and Selection
    [Homework 5]:
  • Homework 5 [pdf]. Deadline: 05/6/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
05/06/2014, Tue Lecture 10: Model Inference and Averaging
    [Homework 6]:
  • Homework 6 [pdf]. Deadline: 05/13/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
05/13/2014, Tue Lecture 11: Boosting and Trees
    [Invited Talk]
  • Speaker: Prof. Yiyuan She, Florida State University
  • Title: High Dimensional Robust Statistics: Theory, Methods and Applications
  • Bio: Dr. She received his PhD in Statistics from Stanford University in 2008. He is an Associated Professor in the Department of Statistics at Florida State University. Dr. She has been working on various areas in statistics, signal processing and machine learning and received the NSF CAREER award. His current research topics include high dimensional statistics, multi-regularization, outlier detection, econometrics forecasting, and network learning.
Y.Y.
Yiyuan She
05/20/2014, Tue Lecture 12: Support Vector Machines and Flexible Discriminant Analysis
    [Project Presentations]
Y.Y.
Students
05/27/2014, Tue Lecture 13: Neural Networks
    [Project Presentations]
    [Homework 6]:
  • Homework 7 [pdf]. Deadline: 06/03/2014, Tuesday. Mark on the head of your homework: Name - Student ID.
Y.Y.
Students
06/03/2014, Tue Lecture 14: Final Project Challenges
    [Final project description]:
  • [pdf]. Deadline: 06/21/2014, Saturday. A poster session will be held for peer reviews.
  • Notice on poster requirement:

  • [Links to all Posters]
Y.Y.
Students

Datasets (to-be-updated)

by YAO, Yuan.