Postgraduate (Master’s):


Course title: Intelligent Systems (BUNI)


Lecturers:  prof. dr. Igor Kononenko and prof. dr. Marko Robnik Šikonja
Assistant:  dr. Petar Vračar

The course purpose: The goal of the course is the students to become acquainted with the field of intelligent systems, which includes a collection of tools and approaches for solving problems which are difficult or unpractical to tackle with other methods.

Student's obligations:

·         on time finished and positively graded homeworks (web quizes and other reports)

·         on time finished and positively graded two seminar works 

·         written exam



The exercises grade is a joint grade for two seminar works, each seminar work has to be finished on time and graded positively.  The precondition for positive exercises grade is also that you achieve at least half of the points from homeworks (web quizes and other reports). We take into account only on time finished homeworks. The exercises grade is valid only the current year. If in the current year the student does not pass the exam, the next year he/she has to do the exercises (homeworks and seminar works) all over again.

Exam is written (and optionally the oral part). The precondition for the written exam is the positive grade from exercises. At the written part of the exam, it is allowed to use an A4 sheet of paper, hand written with an ordinary pencil (that can be rub out - photocopies and prints are not allowed) and signed with a ball-point pen (name, surname and the inscription student  number). At the end of the exam, this sheet of paper has to be given to assistant together with the written exam. The precondition for the (optional) oral exam is positive grade from written exam.


Final grade
Final grade is combined from the exercise grade (50%) and the exam grade (50%). Both parts need to be positive.


Approximate Course Contents:

1.      Intelligence, artificial intelligence (AI) and human-machine interaction: basic philosophical questions about intelligence and AI, the role of AI

2.      Machine learning and data mining, overview of basic algorithms.

3.      Data preprocessing, discretization, visualization.

4.      Intelligent data analysis

5.      Basic principles of machine learning (ML), evaluation of learning, combining ML algorithms

6.      Parallel distributed processing and artificial neural networks

7.      Evolutionary computation and genetic algorithms

8.      Basic principles of modelling: learning as modelling, model quality, model evaluation

9.      Statistical modelling: Bayesian reasoning, linear models, regression models, multivariate models, causal reasoning, non-parametric models, stochastic processes

10.  Decision support systems: classical decision theory, utility functions, game theory, multi-parameter decision models, uncertainty and risk management, group decision making, quality of decision models

11.  intelligent robots and agents: overview and state-of-the-art, agent architectures and agent theory, software agents, learning agents, mobile agents, multiagent systems

12.  Natural language processing: deterministic and stochastic grammars, information extraction, machine translation

13.  Cognitive modelling: humans as intelligent systems, cognitive architectures

14.  Reinforcement learning: basic approaches and algorithms, Q learning, TD learning



Basic literature:

 I. Kononenko in M. Robnik Šikonja: Inteligentni sistemi. Založba FE in FRI, Ljubljana, 2010


I. Kononenko, M. Kukar: Machine Learning and Data Mining, Horwood publ., 2007.

S.J. Russell, P. Norvig: Artificial Intelligence: A Modern Approach, 3rd ed. Prentice Hall, 2009.

Additional literature:

·         Bratko: Prolog Programming for Artificial Intelligence, Third edition, Addison-Wesley, 2000.

·         G. Luger:  Artificial Intelligence: Structures and Strategies for Complex Problem Solving (6th ed.), Addison-Wesley Pearson Education, Boston, 2009

·         D. Jurafsky, J. H. Martin. Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Pearson Education Inc., 2009.

·         Y. Shoham, K. Leyton-Brown. Multiagent systems: algorithmic, game-theoretic, and logical foundations. Cambridge University Press, 2009.

·         L. Busoniu, R. Babuska, B. De Schutter, D. Ernst. Reinforcement Learning and Dynamic Programming using Function Approximators. Taylor & Francis CRC Press, 2010

·         W.N. Venables, B.D. Ripley: Modern Applied Statistics with S, 4th edition. Springer, 2002.

·         R.O. Duda, P.E. Hart, D.E. Stork: Pattern Classification, 2nd Edition, Wiley 2001.

·         E. Turban, J.E. Aronson: Decision support systems and intelligent systems. Prentice-Hall, 2004.



Course title: Machine Learning

Lecturer: Prof. Dr. Igor kononenko
Assistant: Dr. Petar Vračar
Basic goal of the course: To know the basic principles and methods of machine learning (ML)





During exercises you will practice methods and techniques, described in lectures. Students also have to solve homeworks (web quiz) and every student gets a mark for homeworks and the mark has to be at least 50% in order to be able to get the mark for the course.

Research works and MLDM (Machine Learning and Data Mining) Workshop:

Each student (or at most two in a group) autonomously implements a research work, writes a report in a form of a workshop paper (in English) and defends the research work to his supervisor and presents the paper at the MLDM Workshop, which is organized last two week of the semester during the exercises. The mark for the research work counts as the mark for the exercise OC (provided that the homeworks were positively (>=50%) completed). Positive mark for the exercises is a necessary condition for the student to be able to apply for the exam. The mark for the exercises is valid only the current year. If in the same year the student does not pass the exam, the next year he/she has to complete the exercises and homeworks all over again.

Every student (or a group of two), has to prepare a report in a form of the scientific paper (6-8 pages) and the format is specified with the MLDM workshop paper writing guide. The student submits the PDF before the deadline and then the review period begins. Every student receives three papers to review, completes a review form for each of them where the necessary corrections of each paper are also required. The reviews are then distributed to the authors of the papers and the final version of the paper has to be submitted one week before the workshop begins. At the workshop, each student presents the paper in a short lecture (10 min + 5 min discussion).

All the students have to be present at the workshop.

The final mark is the average of the marks for the exam (I) and the exercises (V):

O = (I+V)/2, where I >= 50% and V >= 50%.



Exam is divided into written and the oral part. At the written part of the exam, from the literature it is allowed to use an A4 sheet of paper, hand written with an ordinary pencil (that can be rub out) and signed with a ball-point pen (name, surname and the inscription student  number) (photocopies and prints are not allowed). At the end of the exam, this sheet of paper has to be given to assistant together with the written exam.




·         Reliability of single predictions in classification and regression (paper: Comparison of approaches for estimating reliability of individual regression predictions )

·         Explaining classification of a single instance (paper: Explaining instance classifications with interactions of subsets of feature values  )

·         Deep neural networks (paper: Exploring strategies for training deep neural networks )

·         Learning from impalanced data sets (Book chapter: Use of Prediction Reliability Estimates onImbalanced Datasets )

·         Learning as compression (PhD by Andrej Bratko: Text mining using data compression models )

·         Web user profiling (paper: Web User Profiles with Time-Decay and Prototyping )

·         Recommendation systems (paper: Towards the Next Generation of Recommender Systems  )

·         Archetypal Analysis (PhD by Ercan Canhasi: Graph-based models for multi-document summarization)

·         Active Learning (paper: Active Learning Literature survey )