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https://github.com/networks-learning/hcml-seminar-2024
Human-Centric Machine Learning Seminar @ RPTU (Winter 2024)
https://github.com/networks-learning/hcml-seminar-2024
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Human-Centric Machine Learning Seminar @ RPTU (Winter 2024)
- Host: GitHub
- URL: https://github.com/networks-learning/hcml-seminar-2024
- Owner: Networks-Learning
- Created: 2024-10-14T19:52:47.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-10-23T15:43:56.000Z (25 days ago)
- Last Synced: 2024-10-23T19:36:22.089Z (25 days ago)
- Homepage:
- Size: 617 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Human-Centric Machine Learning Seminar @ RPTU (Winter 2024)
The Human-Centric Machine Learning seminar course focuses on reading and presenting recent research articles in human-centric machine learning, with a particular emphasis in
societal aspects of machine learning (fairness, accountability, transparency), human-ai collaboration and causality. The papers will cover both theoretical foundations, applications as well as empirical studies.
The seminar course is open for Master students.
Important dates:
- Kickoff meeting: 24-10-2024 Thursday, 15:00 - 16:00
- Registration deadline: 31-10-2024
- Acceptance notification: 06-11-2024
- Topic selection: 27-11-2024
- Paper assignment: 04-12-2024
- Practice presentations: 08-01-2025 -- 31-01-2025
- Final presentations: 12-02-2025 -- 29-03-2025
Note: If you have any questions about the seminar, feel free to drop an email to
one of the instructors.
## Course work
As a student, you are expected to read and present one research paper in one of the topics of human-centric machine learning and ask questions during the presentation of a different paper in the same topic by another student. There are various topics available for you to choose from; you can find more information in the "Available Topics"
section below. To help with your presentation, you will do a practice presentation in front of the instructions, where they will give you feedback.
The presentations will be scheduled on 3-4 days during the Winter semester. As a student, you are expected to attend all the presentations and actively engage in discussions.
## Available topics
A list of available topics and papers is available [here](https://docs.google.com/document/d/12UGu9BenZFOmmGjMhGPl5RchiT5mX953bBMhKUt8LzY/edit?usp=sharing).
## Prerequisites
You are expected to be familiar with the fundamentals of Machine Learning. Only those students who have already passed one of the fundamental Machine Learning courses during the course of their master studies will be accepted to the seminar.
## Registration
Fill out this form: [https://forms.gle/puBPTUMJFRJZhePu7](https://forms.gle/puBPTUMJFRJZhePu7)
You are required to enter your name, email address, student number and the list of the courses from the "intelligent systems" specialization that you have already passed, along with your obtained grade. We consider you fit for the seminar if you are a master's student and have passed courses about the fundamentals of machine learning with a satisfactory grade.
There are a limited number of research topics available, so our registrations are also limited. The final list of accepted registrations will be notified via
email.
Deadline: 31-10-2024
Acceptance notification for the course will be sent by email on 06-11-2024
## Topic selection
After receiving an email of acceptance, please rank your preferred topics by completing this [form](https://prettyform.addxt.com/a/form/?vf=1FAIpQLScZgq7-Th9-V3AV2-RQgZxI3yNtRL9ubCWMr0hPG1XZMeGIAg)
Make sure you rank all available topics based on your preferences by 27-11-2024.
Assignment of papers will be notified on 04-12-2024.
## Presentation schedule
The paper assignment and presentation schedule will be announced at a later date.
## Location:
- Kickoff meeting: 24-10-2024 Thursday, 15:00 - 16:00
- Location: Building G26, Room 111.
## Instructors
- Dr. Manuel Gomez-Rodriguez ([email protected])
- Ivi Chatzi ([email protected])
- Eleni Straitouri ([email protected])
- Dr. Suhas Thejaswi ([email protected])
- Stratis Tsirtsis ([email protected])