Spezialvorlesung "Logic, Language and Learning"

Prof. Dr. Luc De Raedt

Co-organizer : Dr. Stefan Kramer

Credit points (Kreditpunkte):  6

Exam day: Monday February 17, 10-12 o'clock, Room: HS 101 00-026

Repetition Exam day: Wednesday April 23, 10-12 o'clock, Room: 101 01-009/013
* NEW: The exam has been marked. You can obtain your marks by personally contacting the secretariat of the Machine Learning Lab in Building 79 or by phone +49 761 203 8006.

This course will deal with Computational Logic, Natural Language Processing, Machine Learning, and theintersections of these three areas. The first part of the course will be devoted to Prolog, probably the most popular language for programming artificial intelligence applications. Prolog is based on first order logic. Hence, programming simply takes the form of declaring axioms in first order logic. Executing Prolog programs is then based on the orem proving. The course will introduce the basic concepts of logic programming and Prolog, and present example programs from Machine Learning and Natural Language Processing. The Prolog part of the course will largely be based on the book "Simply Logical" by Peter Flach, Wiley, 1994. 

The second part of the course will introduce Inductive Logic Programming (ILP), the study of Machine Learning and Data Mining within representations offered by logic programming and Prolog. In this part, we will use our own slides, but useful material on the topic can be found in the book "Relational Data Mining" edited by Saso Dzeroski and Nada Lavrac, Springer, 2001. The third part of the course will present Natural Language Processing in logic programming representations (as introduced in the first part) and ILP applications to Natural Language Processing. An introduction to Natural Language Processing (NLP) is given in "Natural Language Understanding" by James Allen, Addison-Wesley, 2nd Edition, 1995.

Lecture slides

Lecture 1: .pdf.gz, .pdf

Lecture 2: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 3: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 4: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 5: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 6: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 7: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 8: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 9: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 10: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 11: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 12: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 13: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 14: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 15: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 16: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 17: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 18: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 19: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 20: .pdf (2 per page), .pdf (6 per page), .ppt

Lecture 21: .pdf (2 per page), .pdf (6 per page), .ppt

Please note that the Powerpoint presentations can only be downloaded from inside uni-freiburg.de.

Other Materials

Multi-Relational Data Mining and Inductive Logic Programming (Caveat : this is a unfinished draft only given in order to make the course material more understandable. Sections with a * have not been handled in the course. Furthermore, most of the figures and examples can be found in the slides.)

Raymond J. Mooney, "Inductive Logic Programming: Selected Papers from the 6th International Workshop", S. Muggleton (Ed.), pp.3-22, Springer Verlag, Berlin, 1997. Also appeared in "Proceedings of the 6th International Inductive Logic Programming Workshop", pp. 205-224, Stockholm, Sweden, August 1996.[.ps.gz]

Raymond J. Mooney,"Machine Learning". To appear in Oxford Handbook of Computational Linguistics, R. Mitkov (Ed.), Oxford University Press. [.ps.gz],[.pdf]

Postscript slides from Raymond J. Mooney's invited talk on "Relational Learning for Natural Language Parsing and Information Extraction" (ICML-97). 

Assignments

Assignment 1 (due October 30)

Assignment 2 (due November 6)

Assignment 3 (due November 13)

Assignment 4 (due November 20)

Assignment 5 (due November 27)

Assignment 6 (due December 18)

Assignment 7 (due January 8)

Assignment 8 (due January 29)

Assignment 9 (due February 5)

Assignment 10 (due February 12)

Projects

Project 1 (due December 11)

Project 2 (due January 26)