DSCI 475 - Topological Data Analysis
DSCI 475 - Topological Data Analysis
Time and Location: MW 10:00am-10:50am, March 20 - May 10, in Forestry Building 217
Office Hours: MW 11:00am-11:50am in Weber 129
[Syllabus]
Tentative Schedule:
Week 1:
March 20: Course overview and introduction to topology, topological data analysis, and machine learning. [Slides]
March 22: Lab: python introduction and setup. [Jupyter Notebook]
Week 2:
March 27: Clustering, k-means clustering, hierarchical clustering, dendrograms. [Slides by Henry Adams and Lara Kassab]
March 29: Lab: clustering in python. [Jupyter Notebook], [Solutions]
Week 3:
April 3: Dimensionality reduction - PCA, multidimensional scaling.
April 5: Lab: dimensionality reduction in python. [Jupyter Notebook], [Solutions]
Week 4:
April 10: Data visualization - Reeb graphs and Mapper. [Slides]
April 12: Lab: Mapper in python. [Jupyter Notebook]
Week 5:
April 17: Persistent homology of point clouds, Vietoris-Rips complexes, the bottleneck and Wasserstein distances. [Slides]
April 19: Lab: persistence in python with Ripser. [Jupyter Notebook]
Week 6:
April 24: Persistence and machine learning - persistence landscapes and persistence images. [Slides]
April 26: Lab: landscapes and persistence images in python. [Jupyter Notebook], [Textures Example]
Week 7:
May 1: Persistence and machine learning - SVM, kNN, logistic regression. [Slides]
May 3: Lab: persistence and machine learning in python. [Jupyter Notebook]
Week 8:
May 8: Final presentations
May 10: Final presentations