Math 89S: The Emerging Science of Complex Data
Time: TH, 10:05-11:20
Location: Physics 205
Instructor: Paul Bendich
Office Hours: Physics 210, T 12-1, F 9-10.
Textbooks: There will be many handouts and other xeroxed readings throughout the course of the semester.
Assignments: In addition to regularly assigned response writings and problem sets, there will be a substantial research paper, which
will be accompanied by several benchmark assignments over the course of the semester.
| Date | Topics |
|---|---|
| Jan. 10 | Overview, Introduction, Motivating Examples |
| Jan. 15 | No Class |
| Jan. 17 | Intro to SLT |
| Jan. 22 | Basic Probability |
| Jan. 24 | Basic Prob., Intro to Bayesian Analysis |
| Jan. 29 | Bayesian Analysis |
| Jan. 31 | Supervised Learning |
| Feb. 5 | Lecture on Big Data |
| Feb. 7 | NN and Kernel Rules |
| Feb. 12 | Multi-dimensional Scaling |
| Feb. 14 | IsoMap | Feb. 19 | Linear Algebra Basics, I |
| Feb. 21 | Linear Algebra Basics, II |
| Feb. 26 | Principal Components Analysis |
| Feb. 28 | PCA: examples |
| Mar. 5 | Random Walks |
| Mar. 7 | PageRank |
| Mar. 19 | Annotated Bibliographies: Class Discussion |
| Mar. 21 | Collaboration in age of big data |
| Mar. 26 | Diffusion Maps: theory |
| Mar. 28 | Diffusion Maps: examples |
| Apr. 2 | Basic Topology |
| Apr. 4 | Basic Homology |
| Apr. 9 | Persistent Homology |
| Apr. 11 | TDA lecture |
| Apr. 16 | Student Presentations |
| Apr. 18 | Student Presentations |
| Apr. 23 | Student Presentations |