Ruprecht-Karls-Universität Heidelberg
Institut für Computerlinguistik

Bilder vom Neuenheimer Feld, Heidelberg und der Universität Heidelberg

Methods for Learning without Annotated Data

Module Description

Course Module Abbreviation Credit Points
BA-2010[100%|75%] CS-CL 6 LP
BA-2010[50%] BS-CL 6 LP
BA-2010 AS-CL 8 LP
Master SS-CL, SS-TAC 8 LP
Lecturer Letitia Parcalabescu, Juri Opitz
Module Type Vorlesung / Übung / Seminar
Language English
First Session 27.04.2020
Time and Place Monday, 16:15-17:45, INF 327 / SR 3 Wednesday, 14:15-15:45, INF 327 / SR 3
Commitment Period tbd.


Lecture with hands-on exercises and coding sessions.

The course will be completely online during the COVID-19 pandemic.

Please enroll on the Moodle course. The password is communicated per email.

Prerequisite for Participation

  • good knowledge of statistical methods
  • incl. neural networks basic knowledge of linear algebra and calculus
  • advanced BA students or MA students


  • Surpassing 70% of points from exercises to be accepted for the final exam
  • Passing the final exam


Machine Learning algorithms (especially in Deep Learning) need large amounts of training data to perform well. However, high quality humanly annotated data is costly and sometimes impossible to collect.
In this course, we want to present an anthology of methods for coping with absent annotation in data.
The course will be organized as a 2h/week lecture and 2h/week tutorial session, where we will discuss general questions and homework assignments. Active participation in the exercises is mandatory for admission to the final exam.

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