DBMS Praktikum/ AI Tools Lab SS 2020
The AI Tools lab (DBMS-Praktikum) will be remote, and starts on April 28 at 10 am via Zoom (link will by send after registration).
Lecturers: Prof. Dott. Ing. Roberto V. Zicari and Todor Ivanov
Instructor: Todor Ivanov (aitoolslabss2020@gmail.com)
The practical course will be part of the Ethical Implications of AI.
Course start/end: Tuesday, 28.04.2020 to Tuesday, 16.06.2020
Time: Tuesday 10:00 – 12:00
Location: remote via Zoom (link will by send after registration)
Languages: The languages of the lab are English and German.
Credit Points: Students can receive 8 CPs. Link in QIS/LFS
Module names: DB-MPR, M-DS-PR-K, M-DS-PR-A, M-DS-PR-B, DB-PR, M-SIW-PRA, M-SIW-PRB, DB-PR
Registration: You have to register in the form below. Deadline for registration is 23.04.2020.
It is recommended that participants do have basic knowledge in Machine Learning!
Lab Description
The hands-on lab will investigate the features and usability of emerging AI/ML tools focused on Fairness, Bias, Transparency, Explainability and similar Ethical aspects in AI. The goal is to practically apply the tools in real use cases with data sets either provided by the tool vendors or developed by the teams. Based on their experience the participants will perform an assessment of the tools they use as well as feature comparison. All outcomes of the investigations will be reported in the final presentation. In particular the tools that we look at are Google What-If, IBM AI Fairness 360, IBM AI Explainability 360, Captum PyTorch and MS InterpretML.
How to get credit points:
- regular participation to the online meetings
- working on a project topic
- final presentation of the project results
Course Schedule (preliminary)
| Date | Topic | Lab Materials |
| 28.04.2020 | Course Organization and Introduction | [slides] |
| 05.05.2020 | Project Description | [slides] |
| 12.05.2020 | ||
| 19.05.2020 | ||
| 26.05.2020 | ||
| 02.06.2020 | ||
| 09.06.2020 | ||
| 16.06.2020 | Final presentations | Project repositories: Team1 (slides), Team2, Team3 (slides), Team4 (slides), Team5 (slides), Team6 (slides), Team7, Team8 (slides), Team9 (slides), Team10 |
Additional Materials
Materials
- Databricks University Resources
- Data Science Teaching Initiative
- The Open Source Data Science Masters
- IBM Big Data University/ Cognitive Class – free online courses on Big Data technologies
- Cloudera Quickstart VM
- HortonWorks Quickstart VM
Books & Papers