Application of stochastic process theory to design and analyze algorithms used in statistics and machine learning, especially Markov chain Monte Carlo and stochastic optimization methods. Emphasizes connecting theoretical results to practice through combination of proofs, numerical experiments, and expository writing. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas: Writing-Intensive Course, Creativity/Innovation.
FALL 2026 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| A1 |
Huggins |
CAS 326 |
TR 2:00 pm-3:15 pm |
meets w/MA586 Mts w/CAS MA586. CDS Students Only. |
FALL 2026 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| A2 |
Huggins |
EOP 260 |
F 9:05 am-9:55 am |
meets w/ MA586 Mts w/CAS MA586 |
FALL 2026 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| A3 |
Huggins |
SHA 201 |
F 10:10 am-11:00 am |
meets w/ MA586 Mts w/CAS MA586 |
Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.