Bachelor/Masterseminar (WS 2020/2021)

General Information

  • This seminar is open for bachelor- and master students (BA AI, MA AI, CitH).
  • You find administrative information at UnivIS.
  • Participants should sign up for the course in the virtual campus.
  • The course is usually offered in the winter term.
  • Course language is Geman by default.

Topic: 

The growing interest in machine learning in many application domains makes it necessary that machine learned models do not only have high predictive accuracy but also are transparent and comprehensible. Currently, we are on the way into the "third wave of AI" from "describe" over "learn" to "explain". There is a strong interdisciplinary interest in explanation generation for blackbox (mostly deep learning) models as well as a renaissance of interpretable machine learning approaches such as decision rules and inductive logic programming. In the seminar, we will discuss current research papers in the field.

Recommended Reading / Links / Topics

Previous Seminars

Bachelor/Master-Seminar: Explainable Artificial Intelligence: [WS 19/20]

KI-Seminare (KI gestern, heute, morgen):  [WS 18/19] [WS 17/18] [WS 16/17] [WS 15/16]

Bachelor Seminare: [WS 04/05] [WS 05/06] [WS 06/07] [WS 07/08] [WS 08/09] [WS 09/10]  [WS 10/11]    [SS 11] [WS 11/12] [WS 12/13] [WS13/14]

Master Seminare: [SS 05] [SS 06] [SS 08] [SS 09] [WS 09/10] [SS 10]  [WS 11/12] [WS 12/13] [WS 13/14] [SS20]

Reading Clubs:

  • WS 14/15: Cognitive Models for Number Series Induction Problems  [Archiv Page]
  • SS 2014: Experimenting with a Humanoid Robot - Programming NAO to (Inter-)Act  [Archiv Page
  • SS 2013: An introduction into statistic data analysis with R  [Archiv Page
  • SS 2012: Transfer Learning  [Archiv Page] 
  • 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]