|
Date
|
Topic
|
Suppl. Reading (Chapters)
|
Labs |
Handouts |
8/25
|
Intro and logistics |
|
|
intro |
8/27
|
Basic visualizations and tasks, data types, examples,
ethical considerations |
Ward: chapter 1 |
|
basicTasks |
9/1
|
Data preparation (cleaning, imputation, data set integration) |
Ward: chapter 2 |
lab1 |
dataPrep |
9/3
|
D3, AI-assisted coding for VIS applications (design, debugging, refactoring) |
see zoom recording for AI-coding demo |
|
D3 |
9/8
|
Big data and data reduction (distance/sim metrics, intro to clustering) |
Aggarwal 2.4.3.1, 2.4.1, 6.3.1 |
|
dataRed |
9/10
|
High-D data: concept, subspaces, dimension reduction, PCA |
see above |
|
dimRed |
9/15
|
Cluster analysis: hierarchical, density, model, embedding, temporal |
Aggarwal 6.4-5 |
lab2a |
cluster |
9/17
|
Perception and cognition (human visual system, color, contrast) |
Ward: chapter 3 |
|
percept |
9/22
|
(class canceled) |
|
|
|
9/24
|
Visual design and aesthetics |
Ward: chapter 4, Munzner: chapter 5 |
|
design |
9/29
|
Visualization of multivariate and high-D data: linear methods, projections |
Ward: chapter 8 |
|
high-D-Vis-lin |
10/1
|
Vis. of multivariate and high-D data: non-linear methods, embeddings |
Ward: chapter 8 |
lab2b |
high-D-Vis-nlin |
10/6
|
Visualization and AI: mutual support and capabilities (VIS4AI, AI4VIS) |
|
|
Vis+AI |
| 10/8 |
|
|
|
|
10/13
|
no class (Fall break) |
|
|
|
10/15
|
Midterm |
|
|
|
10/20
|
|
|
|
|
| 10/22 |
|
|
|
|
10/27
|
|
|
|
|
11/29
|
|
|
|
|
11/3
|
|
|
|
|
11/5
|
|
|
|
|
11/10
|
|
|
|
|
11/12
|
|
|
|
|
11/17
|
|
|
|
|
11/19
|
|
|
|
|
11/24
|
|
|
|
|
11/26
|
no class (Thanksgiving) |
|
|
|
12/1
|
|
|
|
|
12/3
|
|
|
|
|
12/15
|
Final exam: 8:30 - 11:00 pm |
Location: Frey 301 |
|
|
