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invited @ conferences and workshops invited @ schools @ foreign institutions tutorials








Invited talks at conferences and workshops




  • Computational scientific discovery and inductive databases.
    AM-2002. In Proc. ICDM International Workshop on Active Mining. Maebashi City, Japan, December 2002.

  • Relational Data Mining.
    In Proc. Theory and Applications of Relational Structures as Knowledge Instruments (TARSKI). Prague, November 2002.

  • Learning in rich representations: Inductive logic programming and computational scientific discovery.
    ICML-2002. In Proc. Nineteenth International Conference on Machine Learning.
    ILP-2002. In Proc. Twelfth International Conference on Inductive Logic Programming.
    Sydney, Australia, July 2002.

  • Relational reinforcement learning for agents in worlds with objects.
    AAMAS-2. In Proc. AISB Symposium on Adaptive Agents and Multi-Agent Systems. London, UK, April 2002.

  • Discovery support system for medicine and genetics (D. Hristovski, B. Peterlin, S. Dzeroski)
    In Proc. 2nd Congress of Genetic Society of Slovenia with International Participation, Bled, September 13 - 17, 2000.

  • Environmental applications of machine learning.
    AIRIES-98. Workshop on Artificial Intelligence Research In Environmental Science. Victoria, BC, Canada, October 1998.

  • Inductive logic programming applications: An overview.
    CompulogNet. Area Meeting on Computational Logic and Machine Learning. Manchester, UK, June 1998.

  • New AI techniques applied to modelling (I. Bratko and S. Dzeroski).
    In Proc. Second International Conference Design to Manufacture in Modern Industry, pages 359-372, University of Maribor, 1995.

  • Inductive logic programming for knowledge discovery in databases: an overview.
    AAAI'93 Workshop on Knowledge Discovery in Databases, Washington DC, July 1993.

  • Background knowledge and declarative bias in inductive concept learning (N. Lavrac and S. Dzeroski).
    In Proc. Third International Workshop on Analogical and Inductive Inference, pages 51-71, Springer, Berlin, 1992.







Invited talks at schools




  • An introduction to relational data mining.
    Summer School on Relational Data Mining. University of Helsinki, Helsinki, 2002.

  • Automated ecological modelling with machine learning.
    Classical and Automated Ecological Modelling. Biotechnical Faculty, University of Ljubljana, Slovenia, September 1999.

  • An overview of inductive logic programming applications.
    Inductive Logic Programming Tutorial Day. Madison, Wisconsin, July 1998.

  • An overview of inductive logic programming applications.
    Summer School on Inductive Logic Programming and Knowledge Discovery in Databases. Prague, Czech Republic, September 1997.

  • Dynamical system identification with machine learning.
    Let's Face Chaos through Non-linear Dynamics, International Summer School, University of Ljubljana, Slovenia, September 1993.

  • Environmental applications of machine learning.
    EAIA-98. International Summer School on KDD and Data Mining: Methods and Applications.Caminha, Portugal, September-October 1998.

  • Inductive logic programming.
    MLKDD-98. I Brazilian School on Machine Learning and KDD. Rio de Janeiro, September-October 1998.

  • Dynamical system identification with machine learning.
    Let's Face Chaos through Non-linear Dynamics, International Summer School, University of Ljubljana, Slovenia, September 1993.







Talks at foreign research institutions




  • Relational reinforcement learning for agents in worlds with objects.
    Stanford University, Stanford, April 2003.

  • Computational scientific discovery and inductive databases.
    Istituto di Sziencia e Tecnologie dell' Informazione (ISTI), Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy, March 2003.

  • Is combining classifiers better than selecting the best one?.
    Osaka University, Osaka, Japan, December 2002.

  • Relational data mining.
    NTT Communication Science Laboratories, Kyoto, Japan, December 2002.

  • Is combining classifiers better than selecting the best one?.
    Department of Computer Science, Katholieke Universiteit, Leuven, June 2002.

  • Discovery ordinary and partial differential equations.
    Oxford University, Oxford, UK, April 2002.

  • Theory revision in equation discovery.
    Albert-Ludwigs Universitšt, Freiburg, February and March 2002.

  • Is combining classifiers better than selecting the best one?.
    Austrian Research Institute for Artificial Intelligence, Vienna, February 2002.

  • Equation discovery for dynamic systems identification.
    Stanford University, Stanford, September 2001.

  • Environmental applications of machine learning.
    NASA Ames Research Center, Moffet Field, California. September, 2000.

  • Equation Discovery.
    Katholieke Universiteit Leuven, Leuven, Belgium. December 1998.

  • Applications of Inductive Logic Programming: An Overview.
    Imperial College, London, UK. October 1998.

  • Relational Reinforcement Learning.
    University of Porto, Porto, Portugal. October 1998.

  • Environmental applications of machine learning.
    Department of Computer Science, University of York, York, UK. April 1998.

  • Slovene natural language resources and how to learn from them using ILP.
    XEROX Research Center Europe, Grenoble, France. January 1998. With T. Erjavec.

  • Machine learning applications in biological monitoring of river water quality.
    School of Computing, Staffordshire University, Stafford, UK. November 1997.

  • Slovene natural language resources and how to learn from them using ILP.
    Department of Computer Science, University of York, York, UK. November 1997.

  • Knowledge discovery for diterpene structure elucidation from 13C NMR spectra.
    Department of Computer Science, University of Cyprus, Nicosia, Cyprus. October 1996.

  • Diterpene structure elucidation from 13C NMR spectra with machine learning.
    Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH) Science and Technology Park of Crete, Heraklion, Greece. May 1996.

  • Knowledge discovery for diterpene structure elucidation from 13C NMR spectra.
    Centrum voor Wiskunde en Informatica, Amsterdam, Netherlands. April 1996.

  • Numerical constraints and learnability in inductive logic programming.
    Institute for Applied Information Technology, German National Research Center for Computer Science, Sankt Augustin, Germany. May 1995.

  • Learning first-order clausal theories.
    Department of Computer Science, University of California at Santa Cruz, Santa Cruz, CA, USA. November 1994.

  • Introduction to inductive logic programming.
    Robotics Laboratory, Stanford University, Palo Alto, CA, USA. November 1994.

  • First order jk-clausal theories are PAC-learnable!
    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium. December 1993.

  • Dynamical system identification with machine learning.
    Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium. November 1993.

  • Inductive logic programming for knowledge discovery in databases: an overview.
    AT&T Bell Laboratories, Murray Hill, NJ, USA. July 1993.

  • PAC-learnability of restricted logic programs.
    Department of Computer Science, Tokyo Institute of Technology, Tokyo, Japan. June 1992.

  • PAC-learnability of restricted logic programs.
    Artificial Intelligence Laboratory, University of New South Wales, Sydney, Australia. May 1992.

  • Handling noise in inductive logic programming.
    The Turing Institute, Glasgow, Scotland. December 1991.

  • The role of probability estimates in machine learning.
    The Turing Institute, Glasgow, Scotland. November 1991.

  • Machine learning and inductive logic programming.
    Institute for Information Science, Faculty of Mathematics and Natural Sciences, University of Skopje, Macedonia. May 1991.

  • Learning restricted Horn clauses from noisy examples.
    The Turing Institute, Glasgow, Scotland. January 1991.







Tutorials




  • Multi-Relational Data Mining (S. Dzeroski and L. De Raedt).
    KDD-2003. Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, August 2003.

  • Relational Data Mining.
    SDM-2003. SIAM International Conference on Data Mining. San Francisco, USA, May 2003.

  • Relational Data Mining.
    IEEE International Conference on Data Mining. Maebashi City, Japan, December 2002.

  • Inductive Logic Programming (N. Lavrac and S. Dzeroski).
    Fourth Scandinavian Conference on Artificial Intelligence, Stockholm, Sweeden, 1993.