Study networks that can automatically adapt or reconfigure their parameters or protocol stacks with respect to dynamic environment and network usage. This subject covers the following topics: overview and fundamental concepts of intelligent networks as well as underlying technologies and variety of tools that can be employed to realize such networks, The concepts discussed in the course include network topologies and protocols, quality-of-service as well as cross-disciplinary topics such as applied statistics, artificial Intelligence and self-adaptive technologies.

Guide students through the method of researching.  The subject covers the following topics: Scientific method in research, problem statement, analysis, hypothesis, synthesis, verification, sources of references, documentation and presentation.

Students present and discuss about progress of their research topics to the audiences. The subject also provides additional special talks from visiting speakers concerning conducting research, writing scientific report, publications, sources of research information. 

Linear algebra and numerical methods for engineering to the students. The subject covers the following topics: matrices and system of linear equations, vector space, orthogonality, linear transformations, eigenvalue problems, error analysis, roots of equations, linear algebraic equations, optimization, curve fitting, numerical differentiation and integration with ordinary differential equations.

Study concepts, algorithms, and theories related to computational intelligence. The subject covers the following topics: Refreshment in classification (supervised learning), clustering (unsupervised learning), reinforcement learning, evolutionary computing, fuzzy logic, and nature-inspired algorithms, e.g. swarm intelligence, ant colony optimization.

Study of fundamental concepts of learning and well-known machine learning algorithms. The subject covers the following topics: learning theory (bias/variance trade offss; Vapnik-Chervonenkis theory; large margins); supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); introduction to reinforcement learning.