Seminar on Theoretical Aspects of Machine Learning Algorithms

General information


Date Deadline
14.10. 17:00 first goto meeting
29.10. spotlight and abstract
11.11. bidding
03.12. 14:00 progress presentation goto
08.12. draft report
15.12. reviewing your peers
19.01. 14:00 final presentation goto
28.01. final report


This seminar simulates a machine learning conference, where the students take on the role of authors and reviewers. It consists of multiple phases.

1. Proposal phase

Option 1: our suggestions

You select two projects/papers (i.e. two bullet points) from one of the topics below. You will work with the material mentioned in the overview and the project-specific resources.

Option 2: your own projects

You choose two different own project ideas to work on. This can be some existing machine learning paper/work or an own creative idea in the context of machine learning. Importantly, it has to be specific and worked out well.

Independent of the option you chose, understand the fundamentals of your projects and try to answer the following questions:

Send an email to Maximilian Thiessen with the subject “Seminar on Theoretical Aspects of Machine Learning Algorithms (proposal)”, containing your name, the two selected projects and a short description of your projects together with the answers to the questions (~3 sentences shoud be sufficient).

We can only accept your own proposals if you can answer the mentioned questions and have a well worked out project idea.

Attend the mandatory first meeting on 14.10 at 17:00 ( There you will have a chance to introduce yourself and pitch your projects. We will give preferences to students who can already present some details of their projects.

Until 29.10. (AoE), record a short (~30 seconds) spotlight talk for both your topics and upload it to TUWEL. Also, write an abstract on both topics and upload them to

2. Bidding and assignment phase

You will also act as reviewers and bid on the projects of your peers you want to review. Based on the biddings, we (in the role as chairs of the conference) will select one of each student’s proposals as the actual project you will work on for the rest of this semester. You do not need to work on the other project, anymore. Additionally, we will also assign two different projects from other students to you, which you will have to review later in the semester.

3. Working phase

Now the actual work starts. Gather deep understanding of your project, write a first draft of your report and give a 5-minute presentation. Feel free to go beyond the given material.

4. Reviewing phase

You will again act as a reviewer for the conference by writing two reviews, one for each draft report assigned to you.

5. Writing phase

Based on the reviews from your peers (and our feedback) you will further work on your project.

6. Submission phase

Give a final presentation and submit your report.

General resources (freely available books)


You should have access to the literature and papers through Google scholar, DBLP, the provided links, or the TU library.

Submodular optimisation

chapter 1-3 of “Learning with submodular functions: a convex optimization perspective” by Francis Bach, 2013.

Papers and projects:

Clustering and dimensionality reduction

chapter 1 and 2 of “Dimension reduction: a guided tour” by Christopher Burges, 2010, and chapter 22 (the introduction section before 22.1 and section 22.5) of “Understanding machine learning”.

Papers and projects:

Graph kernels and graph neural networks

chapter 1/introduction of “Graph representation learning” (GRL) by William L. Hamilton, 2020 (pdf).

Papers and topics:

Scalable kernel methods

chapters 1 and 2 of “Learning with kernels” by Bernhard Schölkopf and Alex Smola, 2002 (pdf).

Papers and projects:

Causal inference

Overview: chapter 1 to 3 of “Elements of causal inference” by Jonas Peters, Dominik Janzing, and Bernhard Schölkopf, 2017 (pdf).

Papers and projects:

Semi-supervised learning

first chapter/introduction of “Semi-supervised learning” (SSL) by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, 2006 (pdf).

Papers and projects:

Active learning

chapter 1 “Automating inquiry” of Burr Settles’ “Active learning” book, 2012.

Papers and projects: