Stat 542: Lectures


Contents for Stat542 may vary from semester to semester, subject to change/revision at the instructor’s discretion. The contents below are from Spring 2019. UIUC students can access lecture videos [Here]. Please send your comments to liangf AT illinois DOT edu.

Index
[Week 0: Prerequisite] [Week 1: Introduction]
[Week 2: Linear Regression] [Week 3: Variable Selection and Regularization]
[Week 4: Regression Trees] [Week 5: Nonlinear Regression]
[Week 6: Clustering Analysis] [Week 7: Latent Structure Models]
[Week 9: Discriminant Analysis] [Week 10: Logistic Regression]
[Week 11: Support Vector Machine] [Week 12: Classification Trees]
[Week 13: Recommender System] [Week 14: Brief Introduction to Deep Learning]

[Frequently Asked Questions]

ESL = Elements of Statistical Learning; ISLR = An Introduction to Statistical Learning


    • Week 2: Linear Regression [Back_to_Index]
      • Reading: chap 3 (ISLR); chap 3.1-3.2 (ESL)
      • Notes:
        [W2.1_LinearRegression_MLR.pdf]
        [W2.2_LinearRegression_Geometry.pdf]
        [W2.3_LinearRegression_Practice.pdf]
      • Code: W2_LinearRegression [Rcode] [Python_1] [Python_2]
      • Contents:
        • 1. Multiple linear regression
          • 1.1 LS setup
          • 1.2 LS principle
          • 1.3 LS estimate
          • 1.4 LS output
        • 2. Geometric interpretation
          • 2.1 Basic concepts in vector spaces
          • 2.2 LS and projection
          • 2.3 Properties of LS regression: R-square
          • 2.4 Properties of LS regression: linear transformation
          • 2.5 Properties of LS regression: rank deficiency
        • 3. Practical issues
          • 3.1 Analyzing data with R
          • 3.2 Interpret LS coefficients
          • 3.3 Hypothesis testing
          • 3.4 Handle categorical variables
          • 3.5 Collinearity
          • 3.6 Assumptions and outliers