Advanced Statistical Computing

Bios 8366 at VUMC Biostatistics

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 approximately 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. High Performance Python
  10. Bayesian Computation
  11. MCMC
  12. Introduction to PyMC3
  13. Hamiltonian Monte Carlo
  14. Model Building with PyMC3
  15. Model Checking
  16. Multilevel Modeling
  17. Introduction to Variational Bayesian Methods
  18. Model Comparison
  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. Machine Learning Visualization
  28. Introduction to PyTorch
  29. Neural Networks
  30. Eager Execution and Keras
  31. Convolutional Neural Networks
  32. Bayesian Neural Networks
  33. Advanced Data Visualization (Plotly)
  34. Advanced Data Visualization (Bokeh)
  35. Database Programming
  36. Parallel Processing