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
Module Type Vorlesung / Übung / Seminar
Language English
First Session 19.04.2023
Time and Place Wednesday, 15:15–16:45
INF 329, SR 26

Thursday, 15:15–16:45
INF 329 / SR 26
Commitment Period tbd.

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 manually 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.


  • Intro into Tasks, Motivation
  • Principal Component Analysis (PCA)
  • Clustering (with outliers)
  • Vanilla Autoencoders
  • Variational Autoencoders (VAEs)
  • Generative Adversarial Neural Networks (GANs)
  • (Autoregressive) Diffusion models
  • Self-Supervised Learning (SSL) for text
  • Interpretability in ML
  • Graph Neural Networks (GNNs)
  • Adversarial ML

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