Undergraduate
studies

Upon successful completion of this course students will be able to:
1. Compiles structured software requirements analysis documents
2. Designs software architectural designs based on modeling languages and diagrams
3. Produces code based on the design stage, which will correspond to the corresponding diagrams
4. Uses the Rational Unified Process software lifecycle model

Upon completion of the course students are expected to:
1. know the basic concepts of Artificial Intelligence (AI) and Expert Systems (ES),
2. be able to identify, describe and represent simple logical problems;
3. develop search algorithms,
4. focus, deepen, distinguish and demonstrate ways of solving problems with selected methods of artificial intelligence (e.g. genetic algorithms, fuzzy logic),
5. design and develop approaches for handling uncertainty and fuzziness in rule based systems; 6. know and identify the semantic networks, as well as apply inferences to them,
7. possess basic knowledge in Machine Learning and Artificial Neural Networks.

Upon successful completion of the course, the students:
• will have obtained an in-depth understanding of the design methodology of modern digital circuits
• will be able to design and develop digital circuits using a hardware description language (VHDL)
• will have obtained skills in using modern digital circuit design, simulation and debugging environments
• will be able to apply performance optimization techniques in digital circuits
• will become familiar with the FPGA (reconfigurable) technology
• will have learnt how to design digital circuits using FPGA
• will have been updated about current research issues in the field of digital circuit design

With the successful completion of the course, the student should:
1. Understands the basic principles and concepts of high-speed networks (wired and wireless).
2. It distinguishes high-speed networks from the rest and identifies their key features.
3. It designs a high-speed network consisting of heterogeneous/hybrid networks, crosslayer design.
4. Simulate and manage errors and failures of the high-speed network.
5. Evaluates the operating parameters and performance of a network.
6. It can set up the operation of a network to achieve the desired performance.
7. It can select and compose known networking technologies to create a network with specific operating specifications.
8. Detect new trends in the evolution of computer networks.

With the successful completion of the course students will be able to:
1. Identify the basic concepts of the telecommunications and wireless networks sector.
2. They understand mobile platform architectures.
3. Know Internet of Things (IoT) and Smart Technologies, Game Machines, Sensor Networks, M2M.
4. They understand the concepts Of File System and Process.
5. Identify issues with the security of the Operating System and the File System.
6. They perform Requirement Analysis and Software Design.
7. Handle Software Development and Debugging Tools.
8. Design and develop AR/AR/VR, VR IoT,smart services.
9. They develop fixed computer and mobile applications as well as network applications.

Upon the successful completion of the course, the students will be able to:
• Understand topics related to Data Analytics (DA)
• Apply data preparation techniques for DA purposes
• Choose among various DA methods, such as clustering and classification, in order to solve DA problems
• Exploit on the above knowledge, which is required in a Data Scientist’s portfolio

Upon completion of the course, students will be able to:.
1. Know and understand basic properties that characterize systems..
2. Identify and understand the concepts of Systemic Analysis..
3. Analyze Systems identifying ways of change and utilizing the content of Applied Systems Theory..
4. Define and recognize the Systems by type, as well as the meaning of a Metasystem..
5. Understand the use of Systemic Methodologies..
6. Distinguish the appropriate methodology for each type of Systems..
7. Deepen in the content and applications of Systemic Methodologies and Multimethologies for the development of information systems..
8. Familiarize with the creation of Systems Analysis models..
9. Achieve distinction of dynamic systemic levels..
10. Focus on applications within a real work environment using relevant software..
11. Practice in the implementation of remote systems analysis management.

Upon successful completion of the course the student will be able to:
• Recognize the structured aspects of semi-structured decision problems that can be partially modeled mathematically.
• Identify the basic techniques of multi-criteria decision analysis and apply them to problems whose semi-structured nature is due to the existence of multiple and conflicting criteria for evaluating alternatives.
• Identify analytical data-driven decision support models.
• Understand the Data Envelopment Analysis (DEA) method in the frame of measurement of the performance of decision-making units.
• Use appropriate software to solve linear programs and interpret the results.

With the successful completion of the course, the student will:
• Possess advanced knowledge regarding algorithms, techniques and methods for digital content creation, compression and networking, including lossless/lossy compression of images/audio/video/graphics (JPEG, JPEG2000, MPEG-4), wireless networking (e.g., Bluetooth) and digital rights management (e.g., DRM solutions for the music industry).
• Understand how digital signal processing theory, color theory, psychoacoustics, information theory, compression and networking are combined to create and operate multimedia systems.
• Possess the skills to analyze real world problems (involving open-source data), for which it is required to design/develop/implement systems that process/analyze multimedia content, conduct feasibility studies, select the appropriate algorithms/techniques and assess/compare the effectiveness of competing solutions.
• Deal with the computational burden and complexity imposed by data stemming from real-world problems in a team environment.
• Possess advanced Python/Matlab/GNU Octave programming skills for the implementation of algorithms, techniques and methods for the processing of multimedia content.
• Identify and re-use existing open-source implementations related to multimedia content processing, like the ffmpeg library of functions.
• Identify concepts related to multimedia content processing in the neighboring fields of Signal Processing, Information Theory, Data Compression and Data Networking.

With the successful completion of the course, the student will be able to:
• Understand and explain the main Bioinformatics objectives
• Understand the functionality of popular molecular databases and genome browsers an use diverse internet resources for sets of genes and proteins, with an emphasis on the NCBI portal functionality.
• Use scoring matrices, understand dynamic programming concepts and perform sequence alignment operations on DNA and protein sequences using the Needleman-Wunsch and Smith-Waterman algorithms.
• Understand the BLAST method, interpret BLAST results and perform BLAST searches on the NCBI portal.
• Understand position scoring matrices and Hidden Markov modeling.
• Understand and explain the main stages of multiple sequence alignment and conduct comparative alignment studies.
• Understand and explain the molecular clock hypothesis, define positive and negative selection, and understand different types of phylogenetic trees.
• Understand basic type of repeating DNA elements along with the respective analysis procedures.
• Understand fundamental concepts related to next-generation sequencing technology.
• Describe the main RNA categories.
• Understand steady-state RNA measurement techniques.
• Explain rotein analysis and proteomics.
• Possess Python/Matlab/GNU Octave programming skills to implement bioinformatics algorithms.

Upon successful completion of the course the student is expected to be able to:
• organize his/her teaching approach by detecting the students’ level and presenting the unit’s concepts using techniques that include models of exploratory learning and collaborative teaching,
• compose activities suitable for the students’ familiarization with basic algorithmic control structures and data structures,
• look for learning objects available online on respective digital repositories and incorporate them in the teaching process as elements of visualization, simulation, investigation, experimentation etc.,
• look for educational/learning scenarios kai teaching practices deemed as optimal as well as structure his/hers own,
• evaluate his/her students and his/her teaching approach by applying appropriate research tools and quantitative indicators.

Upon successful completion of the course the students would be able to:
• Know methods and tools to identify vulnerabilities
• Determine possible vulnerabilities in an information system
• Understand common vulnerabilities of information systems and applications
• Identify possible attacks through log file analysis
• Examine and assess the security issues of a process of an organisation
• Identify and classify the methodologies of risk analysis
• Design and implement a security policy in an organisation

Upon completion of the course students are expected to:
1. understand and describe the basic principles of collaborative systems and methods of cooperation,
2. devise and implement team support models, evaluating the common purpose and roles,
3. know and apply shared data synchronization procedures and techniques,
4. use standard teamwork support tools,
5. apply voting methods,
6. utilize collective intelligence through user-based navigation mechanisms, collaborative navigation and collaborative filtering systems,
7. design and implement collaborative systems programming,
8. monitor, record and evaluate the “functional behavior” of the Social Networks, composing the collected data for the implementation of their analysis techniques,
9. be able to design and develop software collaboratively

By completing this course, students are expected to:
• Obtain a deep understanding of the graph-theoretical representation of social networks.
• Obtain a deep understanding of the statistical properties of social networks that emerge from their graph-theoretical representation.
• Analyze and determine the structural information contained within a social network.
• Develop semi-supervised learning algorithms for node classification in social networks.
• Develop algorithms for the computation of graph-theoretic node centrality measures in digital social networks.
• Develop algorithms for community detection in social networks.
• Develop algorithms for the extraction of the connected components of a social network.
• Analyze the textual information conveyed within a digital social network through the utilization of Corpus Vectorization, Topic Modelling and Sentiment Analysis Algorithms.
• Obtain a deeper understanding of algorithmic mechanisms for the generation of artificial networks.
• Perform algorithmic analysis of co-authorship networks.
• Obtain a deeper understanding of information diffusion models in social networks.
• Understand the fundamental concepts of opinion formation models in social networks.

Upon successful completion of this course students will be able to:
1. Explain and apply advanced object-oriented software design principles (π. χ. single responsibility, open-closed etc)
2. Create source code following a software design pattern
3. Critically analyze source code and refactor it based on software design patterns
4. Distinguish and develop solutions to recurring software development problems using software patterns and principles.

Contribution of the course to the coverage of technological requirements:
1. Students are exposed to modern efficient source code design techniques for effective software
2. Students learn effective development and reorganization techniques for increased efficiency
3. Students learn to evaluate and identify software cases where a software development template is required

Upon completion of the course, students will be able to:
1. Distinguish among the different categories of Parallel Systems and evaluate their advantages and disadvantages
2. Evaluate the performance of a parallel application using appropriate tools and appropriate performance metrics
3. Create new algorithms for the parallel computer systems being studied
4. Describe what a critical section, mutual exclusion, a semaphore and atomic instructions are
5. Distinguish when mutual exclusion is required in parts of a parallel application
6. Describe mutual exclusion implementation algorithms
7. Distinguish in what kind of parallel architecture a specific programming model can be used
8. Develop a parallel application using any of the programming models POSIX Threads, OpenMP, MPI and a programming model for graphics cards (GPU)
9. Distinguish between different types of data dependencies and the effect they have on parallelization
10. Explain what vectorization is and what benefits it offers
11. Explain parallel algorithms for implementing basic data structures
12. Distinguish which data structures should be used on a case-by-case basis to achieve high performance in a parallel program
13. Describe the architecture of GPUs
14. Describe programming models for GPU programming
15. Describe performance optimization techniques for parallel applications

Upon completion of the course, students will have developed the following skills:
1. They will be able to identify the parts of a serial application that have increased chances of achieving better performance when parallelized
2. They will be able to develop a parallel application using the appropriate algorithms, tools and programming models for the computer system in use
3. They will be able to evaluate the performance of the parallel application they use or have created
4. They will be able to identify parts of parallel application that can be optimized
5. They will be able to apply techniques to optimize performance at these parts
6. They will be able to design new parallel algorithms for the computer system in use

After the successful completion of the course the students:
- understand and process texts and terminology of academic and professional context
- Write summaries of extended texts
- Manage oral discourse, deliver public speeches, or lectures and present oral texts of interdisciplinary content (including Informatics)
- translate to and from English specific terminology through scientific articles and texts
- identify and utilize fundamental theories and empirical data through a variety of bibliographic databases

Upon successful completion of the course students are expected to:
• Have an exceptional knowledge of the French language in written and oral form.
• Compose specialized text.
• Translate text of general and specialized content to and from French.
• Comprehend scientific articles.
• Collect information from such sources and incorporate it in the scientific paper they are expected to present.
• Know basic functions and capabilities of software for machine translation.
• Be able to translate text concerning the scientific field of Informatics.