Difference between revisions of "DSC Capstone2020"
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This page is under Construction and should be completed by the end of day on 10/4/2020 | This page is under Construction and should be completed by the end of day on 10/4/2020 | ||
| − | * Weekly | + | * Weekly Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160 |
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* Discussion board: [https://piazza.com/ucsd/fall2020/dsc180 Piazza] | * Discussion board: [https://piazza.com/ucsd/fall2020/dsc180 Piazza] | ||
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| + | ==Overview== | ||
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| + | 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. | ||
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| + | 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 [http://gradcam.cloudcv.org Grad-CAM algorithm] in [https://pytorch.org PyTorch] and apply it to the [https://cocodataset.org COCO image data set]. | ||
Revision as of 10:12, 2 October 2020
DSC 180 Capstone Domain: Explainable AI (Section A01)
This page is under Construction and should be completed by the end of day on 10/4/2020
- Weekly Discussion: Wednesdays 12-12:50pm on Zoom at https://ucsd.zoom.us/j/9100475160
- Discussion board: Piazza
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.
