Course outline

Week 1, Jan 8 – 12

• Lecture 1: Statistics review
  • 📖: Can a biologist fix a radio?
  • 👂: Linear Digressions: The Normal Distribution and the Central Limit Theorem
  • 📺: But what is the Central Limit Theorem?
  • 📖: Understanding Moments
• Lecture 2: Fitting & Regression
  • Example Notebook
  • 👂: Linear Digressions: The assumptions of ordinary least squares
  • 📖: Points of Significance: Simple linear regression
  • 👂: Linear Digressions: Convex (and non-convex) optimization
• Lab: Programming and Git Primer, due Jan 18th 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, Jan 15 – 19

• Lecture 3: Fitting & Regression Redux, Regularization
  • Example Notebook
• Lecture 4: Does my model work? Crossvalidation, bootstrap, and friends
  • Example Notebook
• 📖: Points of Significance: Regression Diagnostics
• Lab: Implementation of Shaffer et al., due Jan 26th at 11:59 pm (submit through Bruin Learn!)

Week 3, Jan 22 – 26

• Blank due to updating lecture schedule.
• Lab: Implementation of Stone et al., due Feb 1st at 11:59 pm (on Bruin Learn)

Week 4, Jan 29 – Feb 2

• Lecture 7: Bayesian vs. frequentist approaches
  • 📺: Bayes theorem, the geometry of changing beliefs
  • 👂: Linear Digressions: Beware of simple metrics
  • 📺: The medical test paradox, and redesigning Bayes’ rule
• Lecture 8: Dimensionality reduction - PCA and NMF
  • Example Notebook
  • 📺: A visual linear algebra refresh
  • 📖: Points of Significance: Principal Components Analysis
• Lab: Lecture review, project selection check-in

Week 5, Feb 5 – 9

• Lecture 9: Partial Least Squares Regression
• Lab: Midterm review, finish selecting project

Week 6, Feb 12 – 16

• Lecture 11: Midterm review
  • Previous midterms: W18, W19, W20, F21, W22
  • 📺: Pre-recorded versions of lectures from 2020
• Lecture 12: Midterm exam
• Lab: Implementation of Cosgrove et al., due Feb 22th at 11:59 pm (on Bruin Learn)
• Final project proposals due Feb 16th at 11:59 pm
• Link for setting up final project repositories
  • Be sure to make your repository as a group!

Week 7, Feb 19 – 23

• Lecture 13: Equitable application of models, dynamical models
• Lecture 14: K-Means clustering
  • Example Notebook
• Lab: Implementation of Perelson et al., due March 7th at 11:59 pm (on Bruin Learn)

Week 8, Feb 26 – Mar 1

• Lecture 15/16: Gaussian Mixture Models
  • Example Notebook

Week 9, Mar 4 – Mar 8

• Lecture 17/18: Support vector machines
  • Example Notebook
  • 👂: Linear Digressions: Maximal Margin Classifiers
  • 👂: Linear Digressions: The Kernel Trick and Support Vector Machines
• Lab: Implementation of Masaeli et al., due March 21st at 11:59 pm (on Bruin Learn)

Week 10, Mar 11 – 15

• Lecture 19: Future directions, neural networks, and more
• Lecture 20: Selected project presentations
• Lab: Project presentations
• Final Project, due March 21st at 11:59 pm
  • Please submit your code through the project Github repository, and your final report on Bruin Learn.
  • The link for setting up this repository is up in week 6.