Details
Status: Available
Type(s): Bachelor Thesis, Master Thesis, or Practicum
Participants: 1 Student
Keywords: Intelligence, Deep Networks, Business Programming
Task Description & Requirements
Build components for the smart enhancement of the TU Information Systems (TISS). Topics include matching of data categories (e.g. project contents and research profiles, course contents and student profiles, etc.), recommender systems (e.g. “Students who did course A also subscribed course B”), smart search (e.g. by synonyms, proposed keywords, etc.) and others. Participating students have to have a good knowledge of machine learning methods in the area of text and data analysis, in particular, NLP, deep learning, recurrent networks (e.g. LSTMs) and, of course, word embedding methods such as BERT, GPT-2, etc. Programming is done with Python + Jupyter only; the embedding of the smart components in the service infrastructure is not part of the topic. A powerful GPU infrastructure for training and evaluation is provided. Please note that the present entry comprises a number of topics & can also be subscribed by groups of students. If performed as a diploma thesis, must include a small user-based evaluation of the outcome.