Course outline - BE 175

Week 1, March 31 – April 4

Monday: Statistics review (slides, notes)
Wednesday: Bayesian vs. frequentist approaches (slides, notes) (pre-recorded)
Lab: Programming primer
- Due April 10th at 11:59 pm
- Recommended: Git & Github Fundamentals Tutorial
- 👂: Linear Digressions: Jupyter Notebooks, A Data Scientist’s Best Friend
- 👂: Linear Digressions: Git for Data Scientists

Week 2, April 7 – 11

Monday: Fitting & Regression (slides, notes)
- Example Notebook
Wednesday: Dynamical models (slides, notes)
Lab: Statistics lab
- Due April 24th at 11:59 pm
- Implementation of Shaffer et al.

Week 3, April 14 – 18

Monday: Does my model work? Crossvalidation, bootstrap, and friends (slides, notes)
- Example Notebook
Wednesday: Fitting & Regression Redux, Regularization (slides, notes)
- Example Notebook
Lab: Exam review

Week 4, April 21 – 25

Monday: Reproducible computational workflows (slides, notes)
Wednesday: Exam review, buffer
Lab: Regression lab
- Due May 8th at 11:59 pm

Week 5, April 28 – May 2

Monday: Exam 1
Wednesday: Dimensionality reduction - PCA and NMF (slides, notes)
Lab: Project selection check-in, computational reproducibility review

Week 6, May 5 – 9

Monday: Partial Least Squares Regression (slides, notes)
Wednesday: K-Means clustering (slides, notes)
- Example Notebook
Submit project idea, due May 8th at 11:59 pm
Lab: PCA/PLSR lab
- Due May 22nd at 11:59 pm
- Implementation of Cosgrove et al.

Week 7, May 12 – 16

Monday: Gaussian Mixture Models (slides, notes)
- Example Notebook
Wednesday: Support vector machines (slides, notes)
- Example Notebook
Lab: TODO

Week 8, May 19 – 23

Monday: Continue SVMs
Wednesday: Equitable application of models
Lab: SVM lab
- Due June 5th at 11:59 pm
- Implementation of Masaeli et al.

Week 9, May 26 – 30

Monday: Memorial Day
Wednesday: Exam review, buffer
Lab: Exam review

Week 10, June 2 – 6

Monday: Exam 2
Wednesday: Project presentations
Lab: Project presentations

Finals Week, June 9 – 13

Final project due June 11th at 11:59 pm