DSC Capstone2020

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DSC 180 Capstone Domain: Explainable AI (Section A01)

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
1 Oct 7 Overview
2 Oct 14
3 Oct 21 Checkpoint #1 due
4 Oct 28
5 Nov 4 Checkpoint #2 due
6 Nov 11 Veterans Day (No Discussion)
7 Nov 18
8 Nov 25
9 Dec 2
10 Dec 9 Final Report due

Papers

Useful Links