Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI

Recent Trends in Deep Learning and AI (Wintersemester 2022/2023)

Lecturer: Prof. Dr. Gerard de Melo (Artificial Intelligence and Intelligent Systems) , Maximilian Schall (Artificial Intelligence and Intelligent Systems) , Tolga Buz (School of Entrepreneurship)
Course Website: https://moodle.hpi.de/course/view.php?id=386

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.2022 - 31.10.2022
  • Examination time §9 (4) BAMA-O: 13.12.2022
  • Teaching Form: Project seminar
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English

Programs, Module Groups & Modules

IT-Systems Engineering MA
  • ISAE: Internet, Security & Algorithm Engineering
    • HPI-ISAE-K Konzepte und Methoden
  • ISAE: Internet, Security & Algorithm Engineering
    • HPI-ISAE-T Techniken und Werkzeuge
  • ISAE: Internet, Security & Algorithm Engineering
    • HPI-ISAE-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
Data Engineering MA
Digital Health MA
Software Systems Engineering MA


Artificial Intelligence and Deep Learning are hot topics in research with many fast-moving developments. Recent advances such as Transformers, CLIP, DALL·E 2, and GPT-3 show that deep learning can tackle many complex problems, not only for image and text but also for multimodal problems.

This seminar aims to build upon recent research in artificial intelligence and deep learning. A small selection of the potential topics which could be explored in this seminar are:


Multilingual Vision-and-Language models
Vision-and-Language models have achieved impressive success in learning multimodal representations, which have noticeably improved the performance of those models in tasks like VQA, image captioning, and retrieval. We aim to explore new strategies to extend this success to non-English low-resource languages.


Zero-Shot/Few-Shot NLP
Natural language processing (NLP) includes various tasks such as text classification, summarization, translation, etc. In some cases, we have very little training data or no training data at all, and it is expensive to create training data manually. Therefore we study zero-shot/few-shot methods for NLP tasks with no/little training data.


Artificial Intelligence for Multimodal Behavior and Wildlife Conservation
Computer vision techniques can allow us to track behavior of various sorts. We can use this for human behavioral analysis in the humanities, but also to bring a positive impact on wildlife conservation. Examples of the latter can range from helping wildlife researchers by automatically detecting animal behaviors and predicting locations of animals to detecting poachers.

And Many Other Interesting Topics

Course language: English


  • Strong interest in artificial intelligence and machine learning
  • Experience with Python and perhaps also with machine learning and deep learning frameworks


Potential topics are presented on the first day and can be worked on alone or in a team.

Students will work on these projects throughout the semester, supported by regular meetings with their mentor. Towards the end of the semester, each team will give a presentation.


  • 20% Final Presentation
  • 80% Project (8-Page Paper and Code Submission)

Important criteria for the evaluation of the project include the project effort, the quality of the paper, and the reproducibility of the code. Further details will be given in class.


18th of October 9:15: Presentation of the topics (A1.1)



25th of October (End of day): Top 3 of the topics send to Maximilian Schall 

27th of October: Confirmation of topic selection and teams

31st of October: Registration at the Studienreferat (Moodle for HPI students | E-Mail to the Studienreferat for non-HPI students)

Weekly: Individual meeting with your supervisor

13th of December: Mid-Term Presentation

7th of February: Final Presentation

28th of February: Submission until end of day

Until 31st of March: Grading finished