The course is an introduction to the technology of data networks. Aims to familiarize students with the issues of packet routing within networks with an emphasis on Internet routing. The course also provides a first introduction to new network technologies, security, and network management issues. Finally, it brings students in touch with current cutting-edge issues in network technology, as well as network management and security issues in networks. After attending the course students should:

• Know and be able to apply the principles of structured cabling.
• Be familiar with the operation of network technologies such as Ethernet type networks.
• Be familiar with the basic principles of the Internet.
• Have a good understanding of the subject matter and understand the usefulness and operation of routing protocols.
• Understand the basic concepts of network management and security.
• Have a good understanding of the subject matter and understand the usefulness and implementation of network management systems.
• Be familiar with basic security issues arising from the connection of computers to networks and the basic principles of how to deal with them.
• Understand technological developments in a rapidly developing field.
• Be able to apply this knowledge in a real network environment.

Introduction to Computer Networks and World Wide Web. Protocols, packets transferring, Internet history, applications. Http, FTP, DNS, sockets. TCP, UDP, IP. Network security, wireless security, firewalls, VPNs, DDOS.

Wireshark, analysing protocols, packet addressing, Ethernet and ARP. Encapsulation and TCP/IP protocol stack, Ping, traceroute, RTT time, hop count, TTL, DNS, Fragmentation. ICMP, TCP Dump, TCP Sender Window, TELNET, FTP and TFTP, SMTP, DHCP. OSI, Switching, Multiplexing Synchronization, baseband/passband transmission, ADSL, ATM. Socket Programming, HyperText Transfer Protocol.

The familiarization of the student with the basic concepts of robotics with particular emphasis on solving the basic concepts of robotics kinematic problems (position and velocity) of robotic arms. After the end of the course, the student the student will have the ability to understand basic concepts of robotics, to perform kinematics, to understand robotics concepts, and to position, velocity, and acceleration analysis of robotic arms arms, design controllers in the form of introductory control techniques for robotic arms and to design controllers with an introduction to robotic arm control robotic arm trajectories.

Introduction to Robotics (History, Modern and Future Technology). Structure and classification of robots. The components of robots, manipulation, sensors, control, control architectures, representation, behavior, locomotion and navigation, ensemble robotics, learning, and the future and ethical implications of robotics. Kinematic (Forward and Inverse) Robot Arm Analysis. Robotic Arm Wheel Design. Static Robot Analysis (Jacobian Matrices and Force and Torque Transformations). Dynamic Analysis of Robots (Newton-Euler and Lagrange Models). Basic Robot Control Techniques (PID, Decentralized Control).

Simulation in Matlab & Simulink. In the practical part, we will apply robot interaction schemes in different environments (education and home use) using simulations and real robots - Robotic arms. Teaching with lecture demonstrations and examples on PC.

The aim of the course is for the student to acquire a comprehensive and as complete as possible view on the field of Machine Learning and Pattern Recognition, which is currently considered one of the most important for the field of Electrical/Computer Engineering. Key elements and problems of learning theory such as Neural Networks, pattern recognition, value prediction and clustering are given. The necessary mathematical background as well as the basic programming tools for the implementation and application of the relevant algorithms are given. Upon successful completion of the course the student will be able to:

• Know the most basic machine learning methods and their fields of application.
• Understand the basic types of problems to which machine learning can be applied.
• Analyse simple learning problems and apply appropriate methods to solve them.
• Implement basic learning models with appropriate programming tools.
• Evaluate the performance of learning models.

Present a complete view of the field of Pattern Recognition - Machine Learning. In addition to the most popular models and Machine Learning methods (supervised and unsupervised), the basic elements of the theory are also presented so that the student is aware of how they work how these models work, what are the real possibilities and what their limitations are. It also gives an overview of the extensive reference to the applications of Recognition Pattern-Machine Learning, e.g. Big Data problems/applications, computer vision, image analysis, recognition face recognition, character recognition, voice analysis, natural language processing, sentiment analysis; opinion extraction, robotics, bioinformatics, etc.

Τhe course will be accompanied by practical exercises of a laboratory nature so that the The student will have a better understanding of the concepts developed in the theoretical part. These exercises will involve the use of appropriate software tools (Matlab and Python), with emphasis on open-source tools. software that implements Recognition models Pattern-Machine Learning in different scenarios; use cases.

The basic aim of the course is for students to acquire a good level of knowledge about the fundamental principles and models of fuzzy logic and to understand the operation of fuzzy logic systems logic. The theory of fuzzy logic offers a a different way of dealing with real-world systems without mathematical standardization and the students have the opportunity to study complex systems that are difficult to mathematically their mathematical standardization. In the laboratory part the the first contact of the student with the development of intelligent techniques in MATLAB environment. The familiarization with the techniques of intelligent control give the student the ability to develop and design systems control systems.