Bachelor Seminar Wissenschaftliches Arbeiten

General information

• TISS: (link)
• contact: Sagar Malhotra (email)
• meeting link: (zoom)
• everything important will be announced in TUWEL/TISS.

Format

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

Attend the mandatory first meeting either in person or remotely (details on TUWEL).

Option 1: our suggestions

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

Option 2: your own idea + one of our suggestions

You choose your own topic to work on. This can be some existing machine learning paper/work or an own creative idea in the context of machine learning. We strongly encourage you to start from existing papers from the following venues: NeurIPS, ICML, ICLR, COLT, AISTATS, UAI, JMLR, MLJ. Importantly, your idea has to be specific and worked out well. Nevertheless, choose one of our suggestions as well.

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

• What is the problem?
• Why is it an interesting problem?
• How do you plan to approach the problem? / How have the authors of your topic approached the problem?

Select topics and write a short description of them together with the answers to the questions (~3 sentences should be sufficient) in TUWEL.

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

2. Bidding and assignment phase

You and your fellow students will act as reviewers and bid on the topics 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 topic, write a first draft of your report and give a 5-minute presentation. Feel free to go beyond the given material.

You will schedule two meetings with your supervisor to discuss your progress, but do not hesitate to contact him/her if you have any questions.

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 topic.

6. Submission phase

Give a final presentation and submit your report.

General resources (freely available books)

• Understanding machine learning: from theory to algorithms. Shai Shalev-Shwartz and Shai Ben-David (pdf)
• Foundations of machine learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (pdf)
• Foundations of data science. Avrim Blum, John Hopcroft, and Ravindran Kannan (pdf)
• Mathematics for machine learning. Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong (pdf)
• Mining of massive datasets. Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman (pdf)
• Reinforcement learning: an introduction. Richard Sutton and Andrew Barto (pdf)
• Deep learning and neural networks. Ian Goodfellow and Yoshua Bengio and Aaron Courville (pdf)

Topics (tentative)

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