Prerequisites: MET CS 521 and at least one of MET CS 577, MET CS 622, MET CS 673 or MET CS 682; or consent of instructor. Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN's, Neural Nets and Deep Learning, Transformers, Recurrent Neural Nets, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptron's, backpropagation, attention, and transformers. Each student creates a term project.
FALL 2026 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| A1 |
Mohan |
CAS 204A |
M 6:00 pm-8:45 pm |
|
FALL 2026 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| O2 |
Alizadeh-Shabdiz |
|
ARR 12:00 am-12:00 am |
Students are assigned to class sections of about 20 with a member of the teaching team.
Student visa holders must contact their advisor for approval before registering for any online class. |
SPRG 2027 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| A1 |
Alizadeh-Shabdiz |
|
W 6:00 pm-8:45 pm |
|
SPRG 2027 Schedule
| Section |
Instructor |
Location |
Schedule |
Notes |
| O2 |
Alizadeh-Shabdiz |
|
ARR 12:00 am-12:00 am |
Students are assigned into class sections of about 20 with a member of the teaching team.
Please note any prerequisite(s). Completion of the prerequisite course or consent of the instructor is required.
F1 student visa holders should contact the CS Dept at metcs@bu.edu prior to registering for any online courses. |
Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.
