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Paper Summary

Written on 11.08.22 by Rebekka Burkholz

Dear students,

As agreed in our last meeting, you have time till September 1st, 2022 to submit your short 2-page summary of your paper. Please use the submission system of this website to upload your summary in pdf format:

https://cms.cispa.saarland/peml/submissions/

You can choose any format… Read more

Dear students,

https://cms.cispa.saarland/peml/submissions/

You can choose any format of your choice but please keep in readable and do not use a font size that is smaller than 10pt.

Good luck with your preparations!

All the best,

Aleksandar and Rebekka

Second round of presentations

Written on 20.06.22 (last change on 11.08.22) by Rebekka Burkholz

Dear Students,

As agreed in today's meeting, we will continue with the second round of presentations as follows:

Group 1 - Introduction - 27.06.22

Yufeng Jiang Introduction to Convolutional Neural Networks
Max… Read more

Dear Students,

As agreed in today's meeting, we will continue with the second round of presentations as follows:

Group 1 - Introduction - 27.06.22

Yufeng Jiang Introduction to Convolutional Neural Networks
Max Biwersi A Gentle Introduction to Graph Neural Networks
Severin Adler Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Group 2 - Methods and Applications - 04.07.22

Mohamad Altamer A Few Useful Things to Know About Machine Learning
Leonard Butz Deep Physical Neural Networks Trained with Backpropagation
Albert Klimenko Zero-Shot Text-to-Image Generation
Xudong Zhang Machine Learning and Phone Data Can Improve Targeting of Humanitarian Aid

Group 3 - ML and Society - 11.07.22

Thomas Boisvert Deep Learning: A Critical Appraisal
Emily Ries Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Zoi Giagtzoglou Delphi: Towards Machine Ethics and Norms

Learning about Machine Learning

Written on 03.05.22 by Aleksandar Bojchevski

Dear Students,

As promised here are a few optional additional resources that cover some machine learning basics:

3Blue1Brown's introductory video explaining neural networks. The entire video series on neural networks, as well as his linear algebra and calculus series are also excellent.
… Read more

Dear Students,

As promised here are a few optional additional resources that cover some machine learning basics:

3Blue1Brown's introductory video explaining neural networks. The entire video series on neural networks, as well as his linear algebra and calculus series are also excellent.
StatQuest's playlist of short self-contained videos on many different machine learning concepts (cross validation, bias/variance, regression, NNs, etc.) which often come with detailed step by step simple examples. You can start with the introduction video.
The MIT 6.S191: Introduction to Deep Learning lecture series.
Bishop's Pattern Recognition and Machine Learning book with a detailed mathematical treatment of ML (you probably don't need to go into so much details, but this book is a nice reference).

There are plenty of other resources online that you can easily find with a quick search. If you come across some resource that you find particularly valuable or helpful don't hesitate to share it with your fellow students.

Paper Assignment

Written on 02.05.22 (last change on 30.05.22) by Aleksandar Bojchevski

Dear Students,

The paper assignment is complete and almost all students got their first choice. The first round of presentations will be as follows:

Group 1 - Introduction - 30.05.22

Mohamad Altamer A Few Useful Things to Know About Machine Learning
Yufeng Jiang … Read more

Dear Students,

The paper assignment is complete and almost all students got their first choice. The first round of presentations will be as follows:

Group 1 - Introduction - 30.05.22

Mohamad Altamer A Few Useful Things to Know About Machine Learning
Yufeng Jiang Introduction to Convolutional Neural Networks
Max Biwersi A Gentle Introduction to Graph Neural Networks
Severin Adler Dropout: A Simple Way to Prevent Neural Networks from Overfitting

Group 2 - Methods and Applications - 13.06.22

Leonard Butz Deep Physical Neural Networks Trained with Backpropagation
Albert Klimenko Zero-Shot Text-to-Image Generation
Xudong Zhang Machine Learning and Phone Data Can Improve Targeting of Humanitarian Aid

Group 3 - ML and Society - 20.06.22

Thomas Boisvert Deep Learning: A Critical Appraisal
Emily Ries Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
Zoi Giagtzoglou Delphi: Towards Machine Ethics and Norms

The next step is to read the paper and prepare the first (practice) presentation. You shouldn't underestimate the workload -- we suggest that you start with the first read through of the paper as soon as possible. Remember you are free to use other resources (blogs, videos, lectures, etc.) to help you better understand the material. We also highly encourage you to consult the resources that we shared in the kick off meeting on how to prepare a good presentation. We will also share a few additional resources on machine learning basics in the next few days.

We ask you to submit your presentation here at least 3 days before your presentation date (i.e. by 27.05 for group 1, by 10.06 for group 2, and by 17.06 for group 3). You are still allowed to fine tune and improve your presentation after this deadline but the submission should not significantly deviate from what you present. You are allowed to use any software for your presentation (PowerPoint, Keynote, Google Docs, etc.) but please submit a PDF export.

Your presentation should be 15 minutes long. We highly recommended that you do at least one practice run to make sure that the story flows nicely and that you are on time!

If you have any questions do not hesitate to get in touch.

- Rebekka and Aleksandar

Paper choice

Written on 26.04.22 by Rebekka Burkholz

Please indicate your preferences for a paper that you are going to present here till May 1st, 2022.

Kick-off meeting

Written on 24.04.22 by Rebekka Burkholz

Hello everyone,

We are excited to meet you at our kick-off meeting on Monday, Apr 25th, 2022, at 16:15 on Zoom. We will share the slides that were presented on the meeting in the Materials section.

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Classic Contributions to Machine Learning

Machine learning has not only prepared the modern breakthroughs in computer vision and natural language processing. It has also a successful history of enabling data driven insights. We will discuss classic concepts and seminal papers in the field that cover different techniques ranging from linear regression to deep learning. We will discover joint underlying principles and challenge some of them by modern observations related to overparameterization.
Are the simplest models always the best choice? And is counting parameters really the best way to measure model complexity?
Join the seminar if you enjoy thinking about these kind of questions.

Course Organization

The goal of the proseminar is to learn giving scientific presentations. You will study a given topic, summarize it, and present it to the other participants.

First, we will provide advice and resources that will help you prepare and deliver a scientific presentation. After you have received a topic, you will give an initial practice presentation and receive detailed feedback from all participants. You will have the chance to incorporate this feedback before giving a final graded presentation. At the end of the semester you also need to submit a short 2-page summary.

In the first part of the semester, we will have practice presentations for 2-3 topics each week. The second part of the semester will consist of the final presentations. Attendance is mandatory and the meetings will be in person or virtual. The exact meeting dates will be determined in the kick-off meeting.