DSCI 475 - Topological Data Analysis
Time and Location: MW 10:00am-10:50am, March 18 - May 12, in Forestry Building 217
Office Hours: MW 11:00am-11:50am in Weber 129
[Syllabus]
Tentative Schedule:
Week 1:
March 18: Course overview and introduction to topology, topological data analysis, and machine learning. [Slides]
March 20: Lab: python introduction and setup. [Jupyter Notebook]
Week 2:
March 25: Clustering, k-means clustering, hierarchical clustering, dendrograms. [Slides by Henry Adams and Lara Kassab]
March 27: Lab: clustering in python. [Jupyter Notebook], [Solutions]
Week 3:
April 1: Dimensionality reduction - PCA, multidimensional scaling.
April 3: Lab: dimensionality reduction in python. lJupyter Notebook]
Week 4:
April 8: Data visualization - Reeb graphs and Mapper. [Slides]
April 10: Lab: Mapper in python. [Jupyter Notebook]
Week 5:
April 15: Persistent homology of point clouds, Vietoris-Rips complexes, the bottleneck and Wasserstein distances. [Slides]
April 17: Lab: persistence in python with Ripser. [Jupyter Notebook], [Solutions]
Week 6:
April 22: Persistence and machine learning - persistence landscapes and persistence images. [Slides]
April 24: Lab: landscapes and persistence images in python. [Jupyter Notebook], [Texture Examples]
Week 7:
April 29: Persistence and machine learning - SVM, kNN, logistic regression. [Slides]
May 1: Lab: persistence and machine learning in python. [Jupyter Notebook]
Week 8: Final presentations, in the usual classroom (Forestry 217) [Final Presentation Guidlines]
May 9, 8-9:30am: Final presentations
May 10, 10-10:50am: Final presentations