DSC Capstone2020
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DSC 180 Capstone Domain: Explainable AI (Section A01)
- Instructor: Jurgen Schulze
- Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160
- Piazza Discussion Board: https://piazza.com/ucsd/fall2020/dsc180
Overview
In this capstone domain we are going to study how we can make machine learning systems more user friendly by exploiting additional knowledge we can derive from the system and present it to the user. These types of systems are called Explainable AI.
The example we are going to use in this class is object recognition in images. We are first going to go over the basics of CNNs, then learn about saliency and attention maps, and finally we are going to implement the Grad-CAM algorithm in PyTorch and apply it to the COCO image data set.
The quarter will end with a proposal for your capstone project, which you will be working on in the winter quarter.
This class will be entirely remote.
Schedule
| Week | Date | Discussion Topic | Replication Tasks |
|---|---|---|---|
| 1 | Oct 7 | Overview | |
| 2 | Oct 14 | Literature review | |
| 3 | Oct 21 | Description of data | |
| 4 | Oct 28 | Checkpoint #1 due | |
| 5 | Nov 4 | Description of methods | |
| 6 | Nov 11 | Veterans Day (No Discussion) | Implementation of methods |
| 7 | Nov 18 | Checkpoint #2 due | |
| 8 | Nov 25 | Implementation of Grad-CAM | |
| 9 | Dec 2 | Implementation of Grad-CAM | |
| 10 | Dec 9 | Final Report due |
Papers
- Learning Deep Features for Discriminative Localization
- Grad-CAM: Why did you say that?Visual Explanations from Deep Networks via Gradient-based Localization
