Abstract:

The new curricula in Computer Science Engineering in the Faculty of Computer Science and Engineering has retained the course Intelligent Systems (IS) as an advanced course that is taught after the course of Artificial Intelligence. The IS course serves as an introductory one for more specific courses by exploiting their common ground: Data mining, Machine Learning, Bioinformatics, Robotics, Pattern recognition, Machine Vision, Nature Language Processing.
The IS course was introduced several years ago to the Computer science students. Since the beginning of its life-cycle, it offered interesting and modern topics that аrе revived each new academic year, including modeling the real world, Data Mining, Mechanical Robotics, Bioinformatics, pattern recognition, prediction.
All of the previous generations of students have accepted with interest this course because it introduces a challenge - a view of the technology development from a different angle. This course explains the connection of the essential sciences (statistic, physics, biology, etc) with the Information technology.
As part of the course, the students are obligated to develop projects that require active learning of the introduced methods as tools in building intelligent systems. These projects cover the topics from the IS course. Each of them is balanced to have a good starting point (learned concepts) and a research part. The students are expected to develop their own solution of the given problems and to present their work at the annual national Conference of Informatics and Information Technology. Also, the students decide by themselves who they are going to work with – in groups of 3 persons. They are supposed to work together on the chosen problems during the classes and in their own time. The Learning Management System (LMS) platform of the IS course offers the possibility for the collaborative learning during the whole project development process – using the forum, and notification system for the enrolled users. The students can have answers to their questions from the professor and the assistant during the developing process, in the on-line and off-line manner. The topics of the projects are in the following areas: Image processing, Text mining, Sequence aligning in Bioinformatics, Speech recognition.
Each year, more students decide to do their BSc thesis in this area (Image processing in Entomology, Bayesian learning for classification of biomarkers, DNA sequence aligning, Classification of brain signals during sleeping, Neural network as a controller in a robotic system, Mobile forest control, Checking receptor – compound compatibility, etc ). Usually, these theses are extensions of their individual work in the course –their practical projects.
As a sequence of the intelligent systems learning, we have introduced the Master studies Intelligent Systems Engineering, that includes the modules Bioinformatics and Robotics. The first M.Sc. theses are in their final stage, such as Multiple Kernel learning in Bioinformatics, Emotion detection through sound features in human – computer interaction, among others.
It is interesting to conclude that most of the students that have elected the IS course, have proceeded with the MSc thesis in this topic at our Faculty or in the well known University centers across Europe. We believe that anyone that has been in contact with this material, and has felt the challenge, doesn’t want to leave this area of interest.

Keywords:

Intelligent systems, course, active learning, collaborative learning.