Data Visualization

STAT 890 / 442,  CM 462,




Course Outline
Important Dates

Fall 2006
Department of Statistics and Actuarial Science
University of Waterloo

Instructor: Ali Ghodsi
Room: B2 350
Time: MWF 1:30-2:20
Office Hours:  Monday  3:00pm --4:00pm or by appointment  (MC 6081G)



Kernel PCA:

Locally Linear Embedding (LLE):


MDS, Landmark MDS and Nystrom Approximation:

Semidefinite Embedding (SDE):

Landmark SDE:

Action Respecting Embedding (ARE):

Clustering (Impossibility Theorem):

K-means Clustering:

Metric Learning:

Spectral Clustering:




Sept 11 and Sept 13 Lecture 1 and 2 Motivation
Sep 18 and Sept 20 Lecture 3 and 4 Principal Components Analysis (PCA)
Sep 22 Lecture 5 PCA, Kernel function
Sep 25 Lecture 6 Dual PCA, Kernel PCA
Sep 27 and Sep 29 Lectures 7 and 8 Centering, Locally Linear Embedding  (LLE)                      Slides (Examples are taken from this paper.)
Oct 4 Lecture 9 Locally Linear Embedding 
Oct 6 Project Discussion  
Oct 9 Thanksgiving  
Oct 11 and 13 Lectures 10 and 11 Multidimensional Scaling (MDS), Isomap                           Slides
Oct 16 Lecture 12 Nystrom Approximation, Landmark MDS                         
Oct 18 Lecture 13 Landmark MDS
Oct 20, 23 and 25 Lectures 14, 15 and 16 Unified Framework, Semidefinite Embedding (SDE)
OCT 27 Lecture 17 Landmark SDE
Oct 30 Lecture 18 Action Respecting Embedding (ARE)
Nov 1 Lecture 19 Clustering
Nov 3 and 6 Lectures 20 and 21 Combinatorial Algorithms, K-means clustering
Nov 8 and 10 Lectures 22 and 23 Mixture Models
Nov 13 and Nov 15 Lectures 24 and 25 Learning a Metric (Class-Equivalence Side Information)
Nov 17 Lecture 26 Learning a Metric (Partial Distance Side Information)


Assignment 1                Data for Assignment 1     

Assignment 2                Data for Assignment 2     code

Assignment 3                Clarification

Assignment 4

Important Dates:

November 20          Presentations will start
October 23        Proposal due
November 3        Take-home exam
December 20        Final project reports due

Project Report:

Final project reports (up to 8 pages of PDF) are worth 25% of your final grade .You are encouraged to chose a topic related to your research area. However,  you cannot  borrow part of an existing thesis work, nor can  you re-use a project from another course.  

Due Date:  Final project reports are due December 20 .Hand in your report to Joan Hatton at MC 6028 by 4:00 pm.

Academic Dishonesty:


Plagiarism is an act of “using ideas, plots, text and other intellectual property developed by someone else while claiming it is your original work.”1


1. Tec Encyclopedia.

Evidence of  copying or plagiarism will cause a failing mark in the course.

Please attach this cover page to your report.

I use this marking scheme to mark the projects.