CMSE 802: Methods in Computational Modeling - Spring 2025#
Week |
Date |
Topic |
Topic description |
Homework Deadlines |
|---|---|---|---|---|
1 |
1/14 T |
1 |
Course overview |
|
1/16 R |
2 |
CLI and scripting |
||
2 |
1/21 T |
3 |
Version control |
|
1/23 R |
4 |
Version control continued |
||
3 |
1/28 T |
5 |
Getting data and coding standard |
|
1/30 R |
6 |
Learning with AI |
||
4 |
2/4 T |
7 |
Modeling Intro |
|
2/6 R |
8 |
Finite Difference Methods |
||
2/7 F |
HW 1 Due: Project plan and resource identification |
|||
5 |
2/11 T |
9 |
FDM and High Performant Python |
|
2/13 R |
- |
No class |
||
6 |
2/18 T |
10 |
Linear algebra and Linear Regression |
|
2/20 R |
11 |
Optimization 1 |
||
7 |
2/25 T |
12 |
Optimization 2 |
|
2/27 R |
13 |
Unit Testing |
||
2/28 F |
HW 2 Due: Initial implementation and progress report |
|||
8 |
3/4 T |
- |
Spring Break |
|
3/6 R |
- |
Spring Break |
||
9 |
3/11 T |
14 |
Linear Algebra 2 |
|
3/13 R |
15 |
Dimensionality Reduction |
||
10 |
3/18 T |
16 |
Statistical Analysis and Linear Regression |
|
3/20 R |
17 |
Machine learning: Classification |
||
11 |
3/25 T |
18 |
Machine learning: Model Interpretation |
|
3/27 R |
19 |
Machine learning: unsupervised |
||
12 |
4/1 T |
20 |
Artificial neural network 1 |
|
4/3 R |
21 |
Artificial neural network 2 |
||
4/4 F |
HW 3 Due: Model selection and model interpretation |
|||
13 |
4/8 T |
22 |
Convolutional neural network |
|
4/10 R |
23 |
Transfer learning |
||
14 |
4/15 T |
24 |
Transformers and language modeling |
|
4/17 R |
25 |
TBD |
||
4/18 F |
Projects Due: Final refinements and presentation preparation |
|||
15 |
4/22 T |
26 |
Project Presentations |
|
4/24 R |
Class Review |