Advanced Statistical Computing

Bios 8366 at Vanderbilt University

Course Synopsis

Grading and Assignments
Final Project
Textbook and Reading Materials
Software Requirements
Version Control with Git


Time and Place: 2016 lectures are held on Tuesdays and Thursdays at 9:00-10:30 am in the Biostatistics Large Classroom (Room 11105), 2525 West End Avenue.

Office hours: By appointment

The following syllabus is a statement of intent; content and order may change at any time.

The following materials will be divided into 25 lectures.



The following links will display static Jupyter notebooks of each lecture:

  1. Jupyter and IPython
  2. Plotting and Visualization
  3. Univariate and multivariate optimization
  4. Combinatorial optimization
  5. Introduction to Pandas
  6. Data wrangling with Pandas
  7. Expectation maximization
  8. Bootstrapping
  9. Performance Python
  10. Bayesian computation
  11. Markov chain Monte Carlo
  12. PyMC3
  13. Theano and Hamiltonian Monte Carlo
  14. Model building with PyMC3
  15. Model checking
  16. Variational inference
  17. Multilevel modeling
  18. Model compariaon
  19. Gaussian processes
  20. Dirichlet processes
  21. Scikit-Learn
  22. Clustering
  23. Model selection and validation
  24. Support vector machines
  25. Decision trees
  26. Boosting
  27. Neural Networks