Ruprecht-Karls-Universität Heidelberg
Institut für Computerlinguistik

Bilder vom Neuenheimer Feld, Heidelberg und der Universität Heidelberg

Imitation Learning

Module Description

Course Module Abbreviation Credit Points
BA-2010 AS-CL 8 LP
Master SS-CL SS-TAC 8 LP
Lecturer Artem Sokolov
Module Type Hauptseminar
Language English
First Session 17.02.2020
Last Session 21.02.2020
Time and Place daily, 10:00-16:00, INF 327 / SR 3
End of Commitment Period 21.02.2020

Prerequisite for Participation

  • Good Knowledge of Probability Theory
  • Knowledge of the following will be helpful: foundations of statistical machine Learning, reinforcement learning and neural networks


  • Regular attendance and active participation
  • Presentation or Implementation project


This module provides an introduction into theory and practice of learning from demonstrations with a focus on natural language processing use-cases. Closely related to structured prediction and reinforcement learning, imitation learning is particularly suited for sequence prediction tasks, where often good success metrics or intermediate rewards are hard to define, while in the same time it is easy to provide demonstrations of correct behavior. After taking this module you will be able to formulate imitation learning problems, understand deficiencies of some straight-forward approaches to it, map structured prediction tasks to imitation learning, and solve them using deep learning techniques.

Module Overview


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