- The first official session of the seminar for the registered students (details, topic assignments, team members) will be on 19.10.2018 at 02.09.023, 13:00-15:00
- Interested applicants must send an email to "shafaei (@) in.tum.de" (set the subject of your email to: OLAD) with a copy of their CV and a brief explanation of their motivation and expectations from this seminar, no later than 1th of July 2018. All of the applications will be examined carefully and the selected candidates will be notified on 2th of July.
Students will investigate the role and the future of the Online machine learning approaches, fundamental tools for analyzing online methods and applications to real problems in autonomous driving. Moreover, different scenarios and use cases which are critical in autonomous driving will be examined and future research directions will be enlightened by the end of this seminar.
In our first session (19.10.2018) students will form their group (of two members) and we will discuss the open topics. Each group will get one topic according to their own preference. Groups will collect the necessary materials (state of the art scientific works like publications) within the context of their topic and will perform their research based on the collected resources and will demonstrate their result by implementing the findings of one of the selected papers. Each group will be given 40 minutes on their specified date (are listed in below) to represent the results and afterwards will write and submit a scientific paper (IEEE template) with minimum 6 pages, explaining the contribution of their work in this seminar.
|A- Challenges of deploying online learning applications in autonomous driving||11.01.2019||Presentation|
|B- Advantages of online learning-based solutions for autonomous driving||11.01.2019||Presentation|
|C- Interactive reinforcement learning in autonomous driving||18.01.2019||Presentation|
|E- Online convex optimization in autonomous driving, applications and challenges||18.01.2019||Presentation|
|G- Online learning for comfort functions of the passengers in autonomous driving and the challenges||25.01.2019|
|H- Minimax analysis in autonomous driving applications, state-of-the-art and open challenges||25.01.2019||--|
|I- Ensuring safety in online learning-based applications (safety critical applications)||01.02.2019||Presentation|
|J- Service oriented architecture (SOA) and the integration of online learning-based applications||01.02.2019||Presentation|
|K- Avoiding malicious learning with the help of online learning methods||08.02.2019||Presentation|
|L- Anomaly detection with online learning||08.02.2019||Presentation|