263-2920-00L  Machine Learning for Interactive Systems and Advanced Programming Tools

Semester Autumn Semester 2017
Lecturers O. Hilliges, M. Vechev
Periodicity yearly course
Course Does not take place this semester.
Language of instruction English


Abstract Seminar on the intersection of machine learning, interactive systems and advanced concepts in programming and programming tools.
Objective The seminar will cover a variety of machine learning models and algorithms (including deep neural networks) and will discuss their applications in a diverse set of domains. Furthermore, the seminar will discuss how domain knowledge is integrated into vanilla ML models.
Content Seminars often suffer from poor attention retention and low student engagement. This is often due to the format of the seminar where only one student reads papers in-depth and then prepares a long presentation about one or sometimes several papers. There is little reason for the other students to really pay attention or engage in the discussion.

To improve this the seminar will use a case-study format where all students read the same paper each week but fulfill different roles and hence prepare with different viewpoints in mind.

Student roles/instructions

The seminar is organized with each student taking one of the following roles on a rotating basis:

Conference Reviewer (e.g., reviewer of UIST/ICML/PLDI ): Complete a full critical review of the paper. Use the original review from and come to a recommendation whether the paper should be accepted or not.

Historian: Find out how this paper sits in the context of the related work. Use bibliography tools to find the most influential papers cited by this work and at least one paper influenced by the work (and summarize the two papers).

PhD student: Propose a follow-up project for your own research based on this paper - importantly the project should be directly inspired by the paper or even use/extend the method proposed.

Hacker: Implement a (simplified) version of the core aspect of the paper. Prepare a demo for the seminar. In case the complexity is too high perform an in-depth analysis of reproducibility of the paper.

Detective: Find out background information about the authors. Where did they work when the paper was published; what was their role; who else have they published with; which prior work of the authors may have inspired the current paper? Students may contact the authors (but need to adhere to politeness and courteous manners and stay on topic in their conversations).

All students (every week): Come up with alternative title; find a missing result that the paper should have included.
Prerequisites / Notice Participation will be limited subject to available topics.