Public Presentation TEMPUS project (CD-JEP 16160/2001) Innovation of Computer Science Curriculum in Higher Education Artificial Intelligence Course Innovation in Teaching Methods Leonid Stoimenov, Vladan Mihajlovic Faculty of Electronic Engineering, University of Nis

Previous experience in AI course The professor discourse in old fashion, using chalk and blackboard The lectures are ordinary and boring The students listen the lecture without interest in the teaching The students take the notes as the reference exam preparation The students learn immediately before the exam The knowledge demonstrated on laboratory exercises is not included in total score

How to improve learning process? Make lectures interesting Inspire the students to listen the classes Motivate the students to learn during the semester Encourage the students to pass the exam in first term Increase the portion of the students practice work in the course

AI course organization Lectures Exercises Theoretical Practical (laboratory) Projects (homework) Final evaluation include Projects (40%) Final exam (60%) New web site

New AI course web site contents Lecture notes Practical problems and solution in LISP Exam results Information about project List of proposed project Information about finished projects Links to literature and interesting AI web sites

AI course web site

Lectures New topic that are actual in AI domain are included in the course The modern way of explain the old and new topics covered The students have the lecture notes in advance The students can participate actively in teaching process and pose the questions during the class

Exercises Theoretical exercises LISP – most important commands and simple examples AI algorithms and techniques Implementation of some AI algorithms Laboratory exercises 6 common AI exercises in applying theoretical knowledge The exercises are mandatory The students work individually

First Projects The first project Same task for all students (Victory, Puzzle) Implementation in LISP Checkpoints ones a week (include reports) End date is strictly defined

Second Project Interpretation of AI algorithms and techniques Applying of AI algorithms and techniques in other domains Results: Application Project documentation Rules: No checkpoints and reports Must be finished at the end of course

A* Search Algorithm

Time Series Prediction

Game: “The Balls”

Conclusions The students motivation to attend lectures is increased The students participate actively in teaching The students learn more during the semester Learning theoretical principles and its practical implementation in parallel make lessons easier to understand Analysis during last two years show that 80% of students pass the exam immediately after course is finished

Official AI course site: Contacts: Leonid Stoimenov – Vladan Mihajlovic – Aleksandar Milosavljevic –