36464/36664: Applied Multivariate Methods

This is a rough schedule for the course and will be updated regularly. Please check this frequently for adjustments. Announcements will be posted here and made in class. It will be up to you to keep up to date on all class announcements and web announcements made for the course. Readings and lab assignments will also be made here as well as homework and exam postings. As part of your homework grade, your will be asked to scribe the notes from class once per semester, and these will be posted online. You should type these in .tex (LaTex format only) and convert to .pdf and include code and plots as necessary. All code should be put in the appendix. Scribing is a form of taking notes. Each of you will scribe once during the semester and this will count as a homework grade. Please use LaTex to prepare scribe notes, and please use the template. Below you will find the completed scribe notes. If you are not familiar with Latex (please see http://www.latex-project.org/ for more information and downloading for your OS). This is a great way to write up reports and display mathematical equations and graphical plots. Josh Jelin and Stefan Khoo have given permission for their scribing template to be used by anyone in the course. Check out Josh and Stefan's Scribe template

Please use the homework template to submit homework's (compile to pdf format. If you have not used Latex before, you may find \url{https://pangea.stanford.edu/computing/unix/formatting/latexexample.php} helpful for getting started.

The notes will be due to Rafael, Nick, and myself (via email) one week after the lecture (by 10 am that day).


  1. Introduction to Applied Multivariate Methods (with a review of matrix algebra)
    (Tuesday January 14 1:30PM)
    [scribe notes] [Ch1:Multivariate Notes] [Lecture1 Slides]

  2. More Multivariate Analysis
    (Thursday January 16 1:30PM) [scribe notes] [Lecture2 Slides] [Howework 1] [Howework 1 Solutions] [Longer Proof of Theorem, Slides 17--19]

    Assigned reading:

  3. The Multivariate Normal Distribution and Properties
    (Tuesday January 21 1:30PM) [scribe notes] [Lecture3 Slides]

    Assigned reading:

  4. Contour Plots and Intro to PCA
    (Thursday January 23 1:30PM) [scribe notes] [Ch2: PCA Notes] [PCA R code] [Lecture4 Slides] [Howework 2] [Howework 2 Solutions] [Howework 2 SAIPE data]

    Assigned reading:

  5. More PCA
    (T January 28 1:30PM) [Tues scribe notes] [Lecture5 Slides]

    [Kairavi's and Julian's tex files]

    (Th January 30 1:30PM) [Lecture6 Slides] [Howework 3] [Howework 3 Solutions] [Thurs scribe notes]

    Assigned reading:

  6. Factor Analysis
    (T/Th February4/6 1:30PM) [scribe notes] [Ch3: Factor Analysis Notes] [Lecture7 Slides] [scribe notes]

    [Lecture8 Slides] [scribe notes] [Howework 4] [Homework 4 Solutions]

    Assigned reading:

    Take home exam 1 will cover material up to this point! You MAY NOT work together on the take home exam.

  7. Introduction to classification methods: unsupervised and supervised learning
    (Tuesday Feb 11 1:30PM) [Lecture9 Slides] [scribe notes]

  8. LDA and QDA
    (Thursday Feb 13 1:30PM) [Lecture10 Slides] [scribe notes]

    [Howework 5] [Homework 5 Solutions]

  9. Exam One (take home): Will be posted Thursday Feb 20 and due Friday Feb 28 at 11:59 pm.

    Exam One has been posted to blackboard. Good luck and remember you cannot work with others!

  10. Introduction to Regression and Classification Trees
    (T/Th Feb 18 and Feb 20 1:30PM) [Ch5: Trees Notes] [Lecture11 and 12 Slides] [scribe (lecture 11) notes]

    [scribe (lecture 12) notes]

  11. Optional office hours during class: Take home exam
    (Tues Feb 25 1:30PM)

  12. No class: Take home exam
    (Thurs Feb 27 1:30PM)

  13. Classification Trees
    (Tues March 4 1:30PM) [Ch5: Trees Notes] [Lecture13 Slides] [scribe notes]

    Please read Chapter 8 on regression and classification trees.

  14. Bootstrapping and Bagging
    (Thursday March 6 1:30PM) [Lecture 14 Slides] [scribe notes]

    [Howework 6] [Howework 6 Solutions] [Howework 6 Code]

  15. Random Forests
    (Tues March 18 1:30PM) [Lecture15 Slides]

    [Ch7: Clustering]

    [Lecture16 Slides] [scribe notes]

    [Howework 7]

    [Howework 7 Solutions]

    [Howework R code]

    [Howework CV source code]

  16. Introduction to Clustering: Part II
    (Tues March 25 1:30PM) [Lecture17 Slides] [scribe notes]

    Take home exam 2 will cover material up to this point! You MAY NOT work together on the take home exam. Take home exam two will be posted to blackboard on Friday March 28 and will be due at 11:59 pm on Friday April 4. Please follow the instructions as posted on the exam. Please see class emails for clarification questions on the take home exam. There have been no typos found thus far.

  17. No class
    (Thurs March 27 1:30PM) [Lecture18 Slides]
  18. Advanced Data Analysis Class on PCA and Factor Analysis
    [Lecture19 R Code] [scribe notes]

    (Tues April 1 1:30PM)

  19. Advanced Data Analysis Class on Recommendation Systems
    (Thurs April 3 1:30PM) [Recommender Systems Slides] [Recommender Systems R Code] [Recommender Systems Data] [scribe notes]

  20. Hierarchical clustering
    (Tues April 8 1:30PM) [Lecture 20 Slides] [scribe notes]

    [Howework 8] [Howework 8 Solutions]

  21. Intro to Bayesian Methods
    [Bayes Notes] [Lecture 21 Slides] (Tuesday April 15 1:30PM) [scribe notes]

  22. Intro to MCMC
    [Lecture 21 Slides] [Howework 9] [Howework 9 Solutions] (Thurs April 17 1:30PM) [scribe notes] [scribe notes]

  23. More MCMC and Intro to PlA2 Dataset
    [Lecture 22 Slides] (Tues April 22 1:30PM) [scribe notes]
  24. Convergence Diagnostics and More on MCMC
    [Lecture 23 Slides] (Thursday April 25 1:30PM) [scribe notes]
  25. Image Analysis and MCMC
    [Lecture 24 Slides] (Monday April 28 5:30PM--Make Up Lecture) [scribe notes]

    Detailed Schedule

    Here is the estimated class schedule. It is subject to change, depending on time and class interests.

    Tues Jan 14 1. Introduction and why multivariate models?
    Thurs Jan 16 2. More about multivariate models
    Tues Jan 21 3. More about multivariate models
    Thurs Jan 23 4. Contour plots and Intro to PCA.
    Tues Jan 28 5. PCA
    Thurs Jan 30 6. PCA
    Tues Feb 4 7. Factor Analysis
    Thurs Feb 6 8. Factor Analysis
    Tues Feb 11 9. Introduction to classification methods.
    Thurs Feb 13 10. LDA and QDA
    Tues Feb 18 11. Introduction to Regression and Classification Trees
    Thurs Feb 20 12. No class: Take home exam
    Tues Feb 25 13. No class: office hours
    Thurs Feb 27 14. No class: take home exam
    Tues Mar 4 15. Classification Trees
    Thurs Mar 6 16. Bootstrapping and Bagging
    Tues Mar 11 (Spring break, no class)
    Thurs Mar 13 (Spring break, no class)
    Tues Mar 18 17. Random Forests
    Thurs Mar 20 18. Intro to Clustering .
    Tues Mar 25 19. Intro to Clustering II
    Thurs Mar 27 20. No class
    Tues Apr 1 21. R Lab
    Thurs Apr 3 22. R Lab
    Tues Apr 8 23. Intro to Bayesian Analysis
    Thurs Apr 10 (Spring carnival, no class)
    Tues Apr 15 24. Intro to Bayesian Analysis
    Thurs Apr 17 25. Intro to Gibbs Sampling
    Tues Apr 22 26. Bayesian special topics: TBD
    Thurs Apr 24 27. Bayesian special topics: TBD
    Tues Apr 29 28.Bayesian special topics:TBD
    Thurs May 1 29. TBD