Reading Club Kognitive Systeme (SS 12)

General Information

  • For a general course description please read the corresponding pages from from the WIAI module guide.
  • You find administrative information  at UnivIS.
  • Participants should sign up for the course in the virtual campus.
  • This course addresses master students and doctoral students.

Topic: Transfer Learning

 In classical machine learning, induced hypothesis are restricted to the same domain (feature space, distribution) as the training experience. In contrast, transfer learning addresses the challenge to learn in such a way, that the learning system can profit from learning experience in one domain in the context of another domain. Transfer learning is related to the cognitive science approaches on analogical reasoning where knowledge form a base domain is transferred to a target domain.

 In the reading club we will read, present, and discuss current papers on transfer learning an related areas.

Some references:

Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 10, OCTOBER 2010

Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, Andrew Y. Ng. Self-taught learning: transfer learning from unlabeld data. Proceeding ICML '07 Proceedings of the 24th international conference on Machine learning. 

AI Magazine, Spring 2011, 32(1)

Further readings, see [VC Course]

Reading Club Topics:

  • Basic Concepts of Transfer Learning
  • Transfer Learning in Games
  • Learning and Planning
  • Cognitive Aspects of Transfer Learning

Block Seminar:


  • 08:30 Begrüßung
  • 08:40 Martina Milovec: Cognitive aspects of positive and negative transfer learning  [pdf]
  • 09:20 Christian Reißner: Categorization of Transfer Learning Techniques [pdf]
  • 10:00 Pause
  • 10:15 Christoph Stocker: Transfer Learning through Analogy in Games: Capabilities of a HTN-based Approach [pdf]
  • 10:55 Christian Massny: Transfer Learning and its application to learning for endgames in chess [pdf]
  • 11:35 Pause
  • 11:45 Claudia Fischer & Michael Reuß: Learning from Planning Experience - Learning Track of International Planning Competition [Teil Fischer pdf] [Teil Reuß pdf]
  • 12:30 Abschlussdiskussion

Previous Topics

  • SS 2011: Emotion Mining in Images and Text  [Archiv Page] 
  • SS 2010: Aspects of Cognitive Robotics [Archiv Page] 
  • SS 2009: Reading Club Decision Support Systems [Archiv Page
  • WS 08/09: Algebraic Foundations of Functional Programming (together with Theoretical Computer Science) [Archiv Page]  
  • SS 2008: Similarity (together with Statistics) [Archiv Page]
  • SS 2007: Automated Theorem Proving with Isabelle (together with Theoretical Computer Science) [Archiv Page]
  • SS 2006: Support Vector Machines [Archiv Page]