Course outline - BE 275

Week 0, Sept 23 – 27

• Lecture 1: Statistics review
• Lab: Programming and Git Primer, due Oct 3 at 11:59 pm
  • Repository link
  • 👂: Linear Digressions: Jupyter Notebooks, A Data Scientist’s Best Friend
  • 👂: Linear Digressions: Git for Data Scientists

Week 1, Sept 30 – Oct 4

• Lecture 2: Fitting & Regression
  • Example Notebook
• Lecture 3: Fitting & Regression Redux, Regularization
  • Example Notebook
• Lab: Implementation of Shaffer et al., due Oct 10 at 11:59 pm
  • Repository link

Week 2, Oct 7 – 11

• Lecture 4: Does my model work? Crossvalidation, bootstrap, and friends
  • Example Notebook
• Reading: Points of Significance: Regression Diagnostics
• Lecture 5: Reproducible computational workflows
• Lab: Implementation of Stone et al., due Oct 17 at 11:59 pm
  • Repository link

Week 3, Oct 14 – 18

• Lecture 6: Bayesian vs. frequentist approaches
• Lecture 7: Dimensionality reduction - PCA and NMF
  • Example Notebook

Week 4, Oct 21 – 25

• Lecture 8: Partial Least Squares Regression
• Lecture 9: Dynamical models
• Lab: Implementation of Cosgrove et al., due Oct 31 at 11:59 pm
  • Repository link

Week 5, Oct 28 – Nov 1

• Lecture 10: Finish dynamical models, midterm review
• Lecture 11: Midterm review
• Lab: Midterm review, finish selecting project

Week 6, Nov 4 – Nov 8

• Lecture 12: Midterm exam
• Lecture 13: K-Means Clustering
  • Example Notebook
• Lab: Implementation of Perelson et al., due Nov 28 at 11:59 pm
  • Repository link
• Final project proposals due Nov 7 at 11:59 pm
  • Link to set up final project repository
    • Be sure to add any team members.

Week 7, Nov 11 – 15

• Lecture 14: Gaussian Mixture Models
  • Understanding Gaussians
  • Example Notebook
• Lecture 15: Support vector machines
  • Example Notebook
• Lab: Attend Research Day!

Week 8, Nov 18 – 22

• Lecture 16: Finish support vector machines
• Lecture 17: Building larger models, meta discussion
• Lab: Implementation of Masaeli et al., due Dec 12 at 11:59 pm
  • Repository link

Week 9, Nov 25 – 29

• Lecture 18: Building larger models, meta discussion
• No second lecture, lab session, or office hours due to Thanksgiving.

Week 10, Dec 2 – Dec 6

• Lecture: Project presentations
• Lab: Further project implementation
• Final Project, due Dec 11 at 11:59 pm (submit through project Github repository)