Difference between revisions of "DSC Capstone2020"

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| [[DSC180F20W2|Literature review]]
| [[DSC180F20W2|Literature review]]

Revision as of 00:08, 14 October 2020


DSC 180 Capstone Section A01: Explainable AI


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 learn about saliency and attention maps, then get to know a large publicly available image data set (COCO) 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.

All work in this section is to be done in groups of 2-4 people.

You can use the DSMLP cluster, which already has the COCO data set installed.


Week Date Discussion Topic Homework Tasks
1 Oct 7 Overview None for week 1
2 Oct 14 Saliency Maps Literature review
3 Oct 21 Image Data Set COCO Dataset
4 Oct 28 Replication Checkpoint #1 due
5 Nov 4 Description of methods
6 Nov 11 Veterans Day (No Discussion) Implementation of methods
7 Nov 18 Replication Checkpoint #2 due
8 Nov 25 Implementation of Grad-CAM
9 Dec 2 Implementation of Grad-CAM
10 Dec 9 Final Replication Report due, Capstone Project proposal due


Useful Links

Direct CNN Visualization