| Topic | Date of Lecture |
| General Topics | |
| 1. Fitting Linear Statistical Models to Data by Least Squares: Euclidean | January 28 |
| 2. Fitting Linear Statistical Models to Data by Least Squares: Weighted | January 28,30 |
| x. Fitting Linear Statistical Models to Data by Least Squares: Multivariate | -- |
| 3. PCA | January 30, February4 | Discovery |
| 4. Introduction to Graphs | February 6 |
| 5. Random Graph Models | February 11 |
| 6. Geometric Graph Embeddings: Full Data , Partial Data | February 13 |
| 7. The Cheeger Constant and the Spectral Gap | February 18 |
| 8. Alignment Problems | February 20 |
| 9. Community Detection: Spectral Methods and SDP Relaxations | February 25 |
| 10. Visualization and Continuous Object Transformations | February 27 |
| 11. Dimension Reduction and Data Embedding Techniques , Summary of Discovery Thread | March 4 |
Background Material on Linear Algebra:
Linear Algebra and Its Applications, Fifth Edition,
by David C. Lay, Steven R. Lay, and Judi J. McDonald, Pearson, 2016.
This is the standard text for MATH 240 and 461. It (or an earlier edition)
covers all the linear algebra that you need for this course.
Chapter 6 covers some of the material from the first two lectures on fitting.
Additional Material on Graphs:
Spectral Graph Theory 2nd Edition, by F. Chung, AMS 1997.
See online information at:
Spectral Graph Theory .
Particularly useful:
Chapter 1.
Background Materials on MATLAB: