DSCI 473 - Introduction to Geometric Data Analysis
Time and Location: Jan 21 - Mar 16
T/R 9:30am-10:45am in Clark C 361
F 10:00am-10:50am in Clark C 361
Office Hours: MW 11:00am-11:50am in Weber 129
[Syllabus]
Tentative Schedule:
Week 1:
Lecture: Course overview and introduction to geometric data analysis. [Notes]
Lab 1: Python setup and introduction to python and numpy. [Jupyter Notebook]
Week 2:
Lecture: The singular value decomposition and low rank approximations. [Notes]
Lab 2: SVD and low rank approximations in python. [Jupyter Notebook]
Homework 2
Week 3:
Lecture: Principal components analysis. [Notes]
Lab 3: PCA in python. lJupyter Notebook]
Homework 3
Week 4:
Lecture: Clustering. [Notes]
Lab 4: Clustering in python. [Jupyter Notebook]
Homework 4
Week 5:
Lecture: Multidimensional Scaling. [Notes]
Lab 5: MDS in python. [Jupyter Notebook]
Homework 5
Week 6:
Lecture: Graph Laplacians and Laplacian eigenmaps. [Notes]
Lab 6: Laplacian eigenmaps in python. [Jupyter Notebook]
Homework 6
Week 7:
Lecture: Geometric classification: support vector machines and linear discriminant analysis . [Notes]
Lab 7: SVM and LDA in python. [Jupyter Notebook]
Homework 7
Week 8: Final presentations, in Clark C 361 (the usual classroom) [Final Presentation Guidlines]
Tuesday Mar 11: Review/work on projects
Thursday Mar 13, 9:30am-10:45am: Final presentations
Friday Mar 14, 10-10:50am: Final presentations