DSCI 473 - Introduction to Geometric Data Analysis
Time and Location: Jan 21 - Mar 16
Tuesday and Thursday 9:30am-10:45am in Clark C 361
Friday 10:00am-10:50am in Clark C 361
Office Hours: Thursday 11:00am-11:50am in Weber 218
[Syllabus]
Tentative Schedule:
Week 1:
Lecture: Course overview and introduction to geometric data analysis [Week 1 Notes]
[Homework 1] Due in class January 28
Lab 1: Python setup and introduction to Python and NumPy [Supplementary: Introduction to Python Notebook]
[Lab 1 Problems] Due before class January 31
Week 2:
Lecture: The singular value decomposition and low rank approximations [Week 2 Notes]
[Homework 2] Due in class February 4
Lab 2: SVD and low rank approximations in Python
[Lab 2 Problems] Due before class February 7
Week 3:
Lecture: Principal component analysis [Week 3 Notes]
[Homework 3] Due in class February 11
Lab 3: PCA in Python
[Lab 3 Problems] Due before class February 14
Week 4:
Lecture: Clustering [Week 4 Notes]
[Homework 4] Due in class February 18
Lab 4: Clustering in Python
[Lab 4 Problems] Due before class February 21
Week 5:
Lecture: Multidimensional scaling [Week 5 Notes]
[Homework 5] Due in class February 25
Lab 5: MDS in Python
[Lab 5 Problems] Due before class February 28
Week 6:
Lecture: Graph Laplacians and Laplacian eigenmaps [Week 6 Notes]
[Homework 6] Due in class March 4
Lab 6: Laplacian eigenmaps in Python
[Lab 6 Problems] Due before class March 7
Week 7:
Lecture: Support Vector Machines [Week 7 Notes]
[Homework 7] Due in class March 11
Lab 7: SVM n Python
[Lab 7 Problems] Due before class March 14 π
Week 8: Final presentations, in Clark C 361 (the usual classroom) [Final Project 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