Lectures

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.

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