Biostat 257
Course Schedule
BIOSTAT 257 tentative schedule and handouts (expect frequent updates)
Location and time: CHS 41-268A, Tuesday and Thursday @ 1pm-2:50pm.
BruinLearn: https://bruinlearn.ucla.edu/courses/160238
Slack channel: https://uclabiostat25-0hr8566.slack.com
Invitation link: https://join.slack.com/t/uclabiostat25-0hr8566/shared_invite/zt-1rcvhwe0m-3IYNRDkMMDO8s78FHRhNvQ
Readings:
- 50 years of data sicence by David Donoho (2015)
- Julia: a fresh approach to numerical computing by Bezanson, Edelman, Karpinski, and Shah (2017)
- Julia for biologists, published in Nature Methods (2023).
- What every computer scientist should know about floating-point arithmetic by David Goldberg
- Top 10 algorithms in the 20th century
Metropolis, Simplex, Krylov, Matrix decomposition, Fortran, QR algorithm, Quicksort, FFT, Integer relation, FMM
| Week | Tuesday | Thursday | Homework |
|---|---|---|---|
| 1 | 4/4 course introduction and logistics [slides: Binder, qmd, html] | 4/6 computer languages [slides: Binder, qmd, html], Julia intro. [slides: Binder, qmd, html] | HW1 [ipynb, qmd, html] |
| 2 | 4/11 plotting in Julia [slides: Binder, qmd, html], Jupyter Notebook [slides: Binder, qmd, html] | 4/13 computer arithmetic [slides: Binder, qmd, html], algo. intro. [slides: Binder, qmd, html] | |
| 3 | 4/18 BLAS [slides: Binder, qmd, html], NLA on GPU [slides: Binder, qmd, html] | 4/20 triangular systems [slides: Binder, qmd, html], GE/LU [slides: Binder, qmd, html] | HW2 [ipynb, qmd, html] |
| 4 | 4/25 Cholesky [slides: Binder, qmd, html], QR (GS, Householder, Givens) [slides: Binder, qmd, html] | 4/27 Sweep operator [slides: Binder, qmd, html], summary of linear regression [slides: Binder, qmd, html] | HW3 [ipynb, qmd, html] |
| 5 | 5/2 condition number [slides: Binder, qmd, html], iterative methods intro [slides: Binder, qmd, html] | 5/4 conjugate gradient [slides: Binder, qmd, html] | HW4 [ipynb, qmd, html] |
| 6 | 5/9 easy linear system [slides: Binder, qmd, html], eigen-decomposition and SVD [slides: Binder, qmd, html] | 5/11 optimization intro. [slides: Binder, qmd, html] | HW5 [ipynb, qmd, html] |
| 7 | 5/16 optimization in Julia [slides: Binder, qmd, html] | 5/18 Newton-Raphson, Fisher scoring, GLM, nonlinear regression (Gauss-Newton) [slides: Binder, qmd, html], quasi-Newton [slides: Binder, qmd, html] | |
| 8 | 5/23 KKT [slides: Binder, qmd, html], constrained optimization [slides: Binder, qmd, html] | 5/25 EM algorithm [slides: Binder, qmd, html] | HW6 [ipynb, qmd, html] |
| 9 | 5/30 MM algorithm [slides: Binder, qmd, html] | 6/1 LP [slides: Binder, qmd, html], QP | HW7 [ipynb, qmd, html] |
| 10 | 6/6 SOCP, SDP, GP | 6/8 concluding remarks [slides: Binder, qmd, html] |