PKU

Statistical Learning (统计学习)
Fall 2015

Course Information

Synopsis (摘要)

This course is open to graduates and senior undergraduates in applied mathematics, statistics, and engineering who are interested in learning from data. It covers hot topics in statistical learning, also known as machine learning, featured with various in-class projects in computer vision, pattern recognition, computational advertisement, bioinformatics, and social networks, etc. An emphasis this year is on deep learning with convolutional neural networks.
Prerequisite: linear algebra, basic probability and multivariate statistics, convex optimization; familiarity with R, Matlab, and/or Python, Torch for deep learning, etc.

Reference (参考教材)

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

An Introduction to Statistical Learning, with applications in R. By James, Witten, Hastie, and Tibshirani

Instructors:

Yuan Yao

Time and Place:

Monday 3:10-6:00pm;
The second week: 理教 310 (classroom change!)
2 hour lectures plus 1 hour discussion

Homework and Projects:

Weekly homeworks, monthly mini-projects, and a final major project. No final exam.

Teaching Assistant (助教):

SUN, Xinwei; XIONG, Jiechao; YUAN, Huizhuo; WU, Bingzhe.
Email: statlearning_hw (add "@ 126 DOT com" afterwards)

Schedule (时间表)

Date Topic Instructor Scriber
09/14/2015, Mon Lecture 01: Introduction
Y.Y.
09/21/2015, Mon Lecture 02: Overview of Unsupervised Learning
    [Homework 1]:
  • Homework 1 [pdf]. Deadline: 09/28/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
09/28/2015, Mon Lecture 03: Linear Regression
    [Homework 2]:
  • Homework 2 [pdf]. Deadline: 10/12/2015, Monday. Mark on the head of your homework: Name - Student ID.
    [Mini Project 1]:
  • Project 1 [pdf]. Deadline: 10/12/2015, Monday. Team work with no more than FIVE (5) collaborators.
Y.Y.
10/12/2015, Mon Lecture 04: Linear Classification
    [Homework 3]:
  • Homework 3 [pdf]. Deadline: 10/19/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
10/19/2015, Mon Lecture 05: Basis Expansion and Regularization
    [Homework 4]:
  • Homework 4 [pdf]. Deadline: 10/26/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
10/26/2015, Mon Lecture 06: Model Assessment and Selection
    [Homework 5]:
  • Homework 5 [pdf]. Deadline: 11/02/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
11/02/2015, Mon Lecture 07: Kernel Smoothing Methods
    [Homework 6]:
  • Homework 6 [pdf]. Deadline: 11/09/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
11/09/2015, Mon Lecture 08: Support Vector Machine and Flexible Discriminant Analysis
    [Discussion Session]:
  • Huizhuo YUAN, SVM in R
  • R codes for SVM from Chapter 9, An Introduction to Statistical Learning, with applications in R.
    [Homework 7]:
  • Homework 7 [pdf]. Deadline: 11/16/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
11/16/2015, Mon Lecture 09: Undirected Graphical Models
    [Homework 8]:
  • Homework 8 [pdf]. Deadline: 11/30/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.
11/23/2015, Mon Lecture 10: Neural Networks
    [Homework 9]:
  • Homework 9 [pdf]. Deadline: 11/28/2015, Monday. Mark on the head of your homework: Name - Student ID.
Y.Y.

Datasets (to-be-updated)

by YAO, Yuan.