Course outline - BE 175

Week 1, March 30–April 3

• Monday: Statistics review (slides, notes)
• Wednesday: Bayesian vs. frequentist approaches (slides, notes)
• 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 6–10

• Monday: Fitting & Regression (slides, notes)
  • Example Notebook
• Wednesday: Does my model work? Crossvalidation, bootstrap, and friends (slides, notes)
  • Example Notebook
• Lab: Statistics lab
  • Due April 24th at 11:59 pm
  • Implementation of Shaffer et al.

Week 3, April 13–17

• Monday: Fitting & Regression Redux, Regularization (slides, notes)
  • Example Notebook
• Wednesday: Dimensionality reduction - PCA and NMF (slides, notes)
  • Example Notebook: ipynb, html
• Lab: Reviewing dynamics
• Submit project idea, due April 17th at 11:59 pm

Week 4, April 20–24

• Monday: Finish PCA / NMF
• Wednesday: Partial Least Squares Regression (slides, notes)
• Lab: Review material

Week 5, April 27–May 1

• Monday: Finish PLSR
• Wednesday: Exam 1
• Lab: Project progress check-in

Week 6, May 4–8

• Monday: Dynamical models (slides, notes)
• Wednesday: Dynamical models continued
• Lab: PCA/PLSR lab
  • Due May 22nd at 11:59 pm
  • Implementation of Cosgrove et al.

Week 7, May 12 – 16

• Monday: Autodifferentiation
• Wednesday: Reproducible computational workflows (slides, notes)
• Lab: Content review
  • Review autodifferentiation
  • Discuss project plans

Week 8, May 18–22

• Monday: Double descent
• Wednesday: Neural networks
• Lab: Neural network lab
  • Review TBD
  • Due June 5th at 11:59 pm
  • Implementation of Masaeli et al.

Week 9, May 25–29

• Tuesday: Review
• Thursday: Exam 2
• Lab: TBD

Week 10, June 1–5

• Tuesday: Project presentations
• Thursday: Project presentations
• Lab: TBD

Finals Week, June 8–12

• Final project due June 11th at 11:59 pm