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Spezialvorlesung 

Machine Learning and Data Mining

Prof. Dr. Luc De Raedt

Co-Organisator: Albrecht Zimmermann
  • Lectures:

    • Wednesday 9-11 o'clock (Room: SR 01-009/13, Building: 101)
    • Thursday 9-10 o'clock (Room: SR 01-009/13, Building: 101)

  • Exercises:

    • Thursday 10-11 o'clock (Room: SR 01-009/13, Building: 101)

  • Credit points (Kreditpunkte):

    • 6

  • Language:

    • English

  • Overview:

    The ability to learn is central to intelligence. Therefore machine learning is one of the key areas in artificial intelligence. Machine learning is concerned with building agents that can learn in one way or another. Various techniques for learning will be surveyed in this course. This includes:
     
    • symbolic learning 
    • neural networks 
    • genetic algorithms 
    • decision trees 
    • Bayesian networks 
    • versionspaces 
    • inductive logic programming 
    • computational learning theory 

    The most popular applications of machine learning are in the analysis of data. In recent years, this has been the key topic of data mining. This is a new science, which aims at discovering (nuggets of) knowledge from data. To realize this, it combines machine learning, statistics and database techniques. Special emphasis is laid on analysing large datasets. Techniques from data mining and their applications will also be surveyed in this course. This includes :
     
    • the knowledge discovery process 
    • association rule discovery 
    • handling large data sets 
    •  ... 

    This course is especially relevant to BioInformatics as the analysis and interpretation of
    (large amounts of) data is one of the key problems in BioInformatics. Furthermore, techniques from the field of machine learning and data mining complement the more traditional data analysis techniques from statistics. Various applications in BioInformatics will be discussed in the course.
    Throughout the course the book by  Tom Mitchell, Machine Learning, McGraw Hill, 1997 will be used and complemented by other materials.
     
  • Reading:

    • Machine Learning, Tom Mitchell, McGraw Hill (1997)
    • other materials provided during the course
    • The Datamining course's website at the University of Helsinki links to the most important pattern mining papers and has additional slides
    • Consider also the extracts (here and here) from Mannila/Toivonen/Goethals' book on datamining

  • Further Reading:


  • Additional information about the course:

    • During the course, you will find up to date information here, as well as on the specific slides and recordings, and exercise web page.
    • Lecture on Thursday, 12/02/04, Wednesday, 12/08/04, and Thursday, 12/09/04, will be given in pool room 029 in building 082.
    • The written exam is scheduled for April 6th, 10-12, in 082 00-006, the lecture hall in the Mensa/Pool building.
    • The results of the final examination are on-line. For viewing the corrected exams come to Albrecht Zimmermann's office 079-1005.