Extended Seminar in Machine Learning and Data Mining

Interactive Machine Learning

The seminar page can be found here in TUCaN.

Who, when and where?

The seminar will be held as block seminar on January 8th and 9th, in Room A126. A detailed schedule will be made available as soon as the talks are scheduled. The kick-off meeting was on Tuesday, October 24, 2017, 17:10h in C110.

The seminar will be jointly held by Profs. Carsten Binnig, Johannes Fürnkranz, and Kristian Kersting.

The topic, paper, group and schedule assignments are available.


It is not necessary to have prior knowledge in artificial intelligence, but prior knowledge in data mining and machine learning is helpful. Participation is limited to 20 students. In case we have more students, students with prior knowledge in data mining and knowledge discovery will be preferred. The selection will be made at kick-off meeting.

For further questions feel free to send an email to ml-sem@ke.tu-darmstadt.de. No prior registration is needed, however, please stlll send us an email so that we are able to estimate beforehand the number of participants, and have your E-mail address for possible announcements. Also make sure that you are registered in TUCaN.


This year's topic of the seminar is Interactive Machine Learning, i.e. machine learning algorithms that are meant to be used interactively or co-actively with a human user. We will concentrate on recent papers published in workshops, journals, and conferences. A list of topics is available below. The topics will be assigned based on an on-line bidding process, which will be opened after the kick-off. The final assignment will be made a week later.

Although each topic is typically associated with a single paper, the point of the talk is not to exactly reproduce the entire contents of the paper, but to communicate the key ideas of the methods that are introduced in the paper. Thus, the content of the talk should exceed the scope of the paper, and demonstrate that a thorough understanding of the material was achieved. See also our general advices on giving talks.

Students are expected to give a 20 (!) minute talk on the material they are assigned, followed by 10 minutes of questions. Note that the comparably short period of time forces you to get the most important points of your topic across. You are not expected to present everything.


The talks are expected to be accompanied by slides. In case you do not own a laptop, please send us the slides in advance, so that we can prepare and test the slides. The talk and the slides should be in English.

Extended Seminar?

What is "Extended" about this seminar? Students are not only expected to give a short talk, but also to prepare a small write-up. The write-up will be prepared in groups, each group will cover one theme, consisting of four topics. The final write-up must be concise and short, and should give a short overview of the theme (not necessarily limited to the studied papers).

In addition, we will also do a peer reviewing process, as it is usually done at scientific conferences. This means that you also have to read (some) of the other write-ups and provide feedback by filling out a review form.

Because they are more work for students, students receive 4 CPs for Extended Seminars (instead of 3 CPs for regular seminars).

Topics and Schedule

All papers should be available on the internet or in the ULB. Note that Springer link often only works on campus networks (sometimes not even via VPN). If you cannot find a paper, contact us.

Session 1: Active Learning (Jan. 8th, 13:00h)

Session 2: Hyperparameter Optimization (Jan 8th, 15:30h)

Session 3: Interactive Machine Learning (Jan 9th, 10:00h)

Session 4: Interaction with Expert Knowledge (Jan 9th, 13:30h)


Papers were distributed via bidding. Students could bid for papers using this form. All students received a paper in one of the two most preferred categories. A better choice was often not possible (e.g., if you bid only for a single paper and set all others to "Don't Want It", it is unlikely that you received your paper).


The slides, the presentation, the answers given to questions in your talk will influence the overall grade, as will the write-up and the reviews. Furthermore, it is expected that students actively participate in the discussions, and this will also be part of the final grade. 
To achieve a grade in the 1.x range, the talk and write-up needs to exceed the contentual recitation of the given material and include own ideas, own experience or even demos. An exact recitation of the papers will lead to a grade in the 2.x range. A weak presentation and lack of engagement in the discussions may lead to a grade in the 3.x range, or worse. For the wríte-ups it is important that they provide a coherent view (like a survey paper), and do not simply consist of a concatenation of four paper summaries.
Please read also very carefully our guidelines for giving a talk.
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Knowledge Engineering Group

Fachbereich Informatik
TU Darmstadt

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Hochschulstrasse 10

D-64289 Darmstadt

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