Artificial Intelligence Institute, University of Groningen, The Netherlands

Kunstmatige Intelligentie 2 (KI2) Artificial Intelligence 2 Academic year: 2008 - 2009

This course is intended to introduce topics not handled in the introductory course (KI1) and other advanced topics in AI.

Compulsory course in the 3rd study year, period 2, semester 1, 5 ECTS, course code KIB.KI203.

The lectures will take place on Mondays, 9.00 - 11.00 hrs, in room 267 of Bernoulliborg.

The first assignment is individual. For the programming assignments, you may form groups of 2 students and work together on the same program. However, the two questions at the end of the programming assignments must be answered separately and individually by each student. Every assignment has two deadlines. All assignments should be completed before the first deadline. Assignments completed in between the two deadlines will only get half of the corresponding number of points. Submissions after the second deadline will not be considered.

The assignments can be submitted directly on paper to the student assistant in charge. They can also be submitted by e-mail at the following address: ki2 at ai.rug.nl.
Please specify clearly the student name and number in your submissions.

The practical sessions will take place on Fridays, 13.00 - 15.00 hrs, in room 228 of Bernoulliborg.

The programs will be evaluated on: correct functioning of the executables, style and efficiency of the code, clarity of the comments. All students must separately and individually also answer the two questions regarding the general underlying principles of the assignment.

All the lecture slides are part of the compulsory literature for the exam. In addition, you must study also the materials mentioned below in the table. Note that all AIMA chapters and page numbers are from to the second edition of the book (green cover, 2002).

Successful completion of all the assignments will give you 30 points. The final exam is worth 70 points. A minimum of 35 points must be obtained in the written exam in order to pass. The final grade will be the total number of points accumulated in the written exam and the assignments divided by 10 and rounded to the nearest integer or half-integer.

In order to be able to take the exam, you must register. Participation in the lectures does not register you also automatically for the exam. Bring a calculator with you to the exam.

Available from the secretariat.

Given the importance of probabilistic modeling and learning in modern AI, there exists some overlap between KI2 and some topics treated in the Machine Learning (ML) course (optional masters course, code LIX004M05). The areas of overlap are two. First, Bayesian learning is a fundamental topic covered more in depth at KI2 (with programming assignment) and also theoretically treated at ML due to its importance. Second, Hidden Markov Models (HMMs) are introduced at KI2 in the general framework of Markov processes, however they are treaded more in depth at ML with accent on the Expectation - Maximization (EM) training algorithm and with application examples in linguistics. KI2 is a good preparatory course for ML (not a prerequisite). A student taking both courses will have the advantage of a better understanding of these essential topics as they are presented from two different perspectives.