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Algorithms and Complexity
ΥΠ25 - Algorithms and Complexity
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Algorithms and Complexity
Course id: ΥΠ25
Type: Core Course
Semester: 6
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT110/
Activities
Lectures (Theory): 3,0
Lab lectures: 0,0
ECTS credits: 5,0
Learning Outcomes
The objective of this course is for students to become familiar with the design and analysis of algorithms for the solution of basic problems. Students will learn:
Basic algorithm design techniques,
basic techniques for measuring and evaluating the performance of algorithms,
complexity classes like P, NP and EXP
NP-Completeness and NP-Hardness and techniques to deal with it.General Skills
Independent work
Promoting free, creative and deductive thoughtCourse Content
1st week (lecture): Introduction.
2nd week (lecture): Analysis of Algorithms
3rd week (lecture): Graph Algorithms
4th week (lecture): Greedy Algorithms Ι
5th week (lecture): Greedy Algorithms ΙΙ
6th week (lecture): Divide & Conquer Ι
7th week (lecture): Divide & Conquer ΙΙ
8th week (lecture): Dynamic Programming Ι
9th week (lecture): Dynamic Programming ΙΙ
10th week (lecture): Networks, Max Flow Min Cut
11th week (lecture): NP & Intractability Ι
12th week (lecture): NP & Intractability II
13th week (lecture): Dealing with NP-CompletenessLearning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
eclass platform, youtube channel
Course Organization
Assessment
Ι. Written Examination entailing
Theory
ExercisesLiterature
Jon Kleinberg & Eva Tardos. Algorithm Design. Pearson Education, 2013.
Cormen, Leiserson, Rivest & Stein. Introduction to Algorithms. MIT Press. Third Edition. 2009. -
Artificial Intelligence
ΥΠ23 - Artificial Intelligence
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Artificial Intelligence
Course id: ΥΠ23
Type: Core Course
Semester: 6
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT231/
Activities
Lectures (Theory): 3,0
Lab lectures: 0,0
ECTS credits: 5,0
Learning Outcomes
The course provides an introduction in fundamental concepts of Artificial Intelligence and an understanding of selected methods for
- Algorithms for solving problems by searching
- Knowledge representation and reasoning
- Planning
- Machine learning
After successfully completing this course, students should be able to understand and apply appropriate methods from each category to real-world Artificial Intelligence problems.General Skills
- Adaptation in new conditions
- Independent work
- Team work
- Decision making
- Promoting free, creative and deductive reasoningCourse Content
- Introduction to Artificial Intelligence
- Solving problems by searching
- Adversarial search
- Markov Decision Processes
- Constraint Satisfaction Problems
- Introduction to Bayesian Networks
- Structured Knowledge Representation and Reasoning
- Introduction to Machine LearningLearning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
eclass course web page
use of AI frameworks and librariesCourse Organization
Assessment
- Final examination (60%)
- Assignments (individual or in teams) (40%)Literature
1. W. Ertel, Introduction to Artificial Intelligence
2. Stuart Russell and Peter Norvig. Artificial Intelligence: A Μodern Approach, Prentice Hall, 4th Edition (2020)Journals (indicative list):
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks and Learning Systems
Engineering Applications of Artificial Intelligence
Expert Systems with Applications
Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Neural Computing and Applications
International Journal of Computer Vision
Conferences (indicative list):
Neural Information Processing Systems
International Conference on Learning Representations
AAAI Conference on Artificial Intelligence
Computer Vision and Pattern Recognition
International Conference on Computer Vision
International Joint Conference on Artificial Intelligence -
Distributed Systems
ΥΠ21 - Distributed Systems
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Distributed Systems
Course id: ΥΠ21
Type: Core Course
Semester: 5
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT138/
Activities
Lectures (Theory): 3,0
Lab lectures: 2,0
ECTS credits: 5,0
Learning Outcomes
The objective of the course is to familiarize students with the concept, the architecture and basic services in a distributed system, as well as the development, installation and management of distributed applications. Laboratory hand-on experience helps students to master web-based application programming using J2EE and Web Services.
General Skills
Independent work
Promoting free, creative and deductive thought
Decision making
Work in teamsCourse Content
Distributed Systems- Definitions and basic principles
DS Architecture,Cloud computing
Basic Tools and Services: Name Service, File Service
DS management algorithms
Logical time, mutual exclusion
Synchronization – replication
Web-based DS – J2EE architecture
Web Services: Architecture and technologies, standards/protocols (WSDL, SOAP, UDDI), REST calls.
Component-based IS and agile IS
DS design.Learning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
eclass, youtube channel
Course Organization
Assessment
The final grade is computed as follows: written examination 50%, group programming projects 50% (usually 2, contributing 30% and 20% of the final grade respectively). To pass the course, a student should have a passing grade (at least 5) in BOTH the written exams and the group projects individually.
Literature
A. Tanenbaum, “Distributed Operating Systems”, Prentice Hall, 1995.
S.Weerawarana, F. Curbera, F. Leymann, T. Storey, D. Ferguson, “Web Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging, and More”, Prentice Hall. 2005.IEEE Transactions on Parallel and Distributed Systems
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Human Computer Interaction
ΥΠ20 - Human Computer Interaction
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Human Computer Interaction
Course id: ΥΠ20
Type: Core Course
Semester: 7
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT203/
Activities
Lectures (Theory): 3,0
Lab lectures: 0,0
ECTS credits: 5,0
Learning Outcomes
- To equip students with basic knowledge in the study of methods, techniques and tools required
for the design and evaluation of user interfaces and more specifically:
- Design of user interfaces: interaction and usability,
- Cognitive psychology and its role in: analysis, design and evaluation,
- Learning user-centered design principles, methods and techniques used,
- Learning rapid Prototyping techniques,
- Learning how to manage time and constraints in IS projects,
- Learning usability evaluation techniques and how they can be used,
- Understanding natural user interface principles and design methods.General Skills
- Search, analysis and synthesis of data and information with the use of the assorted technologies
- Decision Making
- Team work
- Project design and management
- Promoting reasoning and self-improvement
- Promoting free, creative and deductive reasoningCourse Content
- Week 1: Introduction to the course: Definition, human model, usability.
- Week 2: User interface design requirements engineering.
- Week 3 & 4 (Laboratory): Interaction styles.
- Week 5 (Laboratory): Techniques and methods for the design of user interfaces.
- Week 6 (Laboratory): Dialog design – direct manipulation design guidelines.
- Week 7: Midterm Exam.
- Week 8 & 9 (Laboratory): Rapid prototyping techniques and methods.
- Week 10 & 11: Usability evaluation techniques.
- Week 12: (Laboratory): Multimodal interaction, natural user interface.
- Week 13: Design for mobile devices: basic principles.Learning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
e-class, powerpoint presentations, browsing and
demonstrations of Internet case studies, e-mail
communication with studentsCourse Organization
Assessment
- Group Project Assignment: web site design and implementation.
- Written exams: multiple-choice questions and short questions.Literature
- Nikolaos Avouris. Introduction to Human Computer Interaction. Diavlos publications. Athens 2000. (in Greek)
- Alan Dix, Janet Finlay, Gregory D. Abowd, Beale Russell, 2007, Human computer interaction. -
Information System Security
ΥΠ27 - Information System Security
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Information System Security
Course id: ΥΠ27
Type: Core Course
Semester: 7
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT203/
Activities
Lectures (Theory): 3,0
Lab lectures: 0,0
ECTS credits: 5,0
Learning Outcomes
At the end of the course the students must:
Be presented with the transdisciplinary (technological, economic, legal, social) delimitation of security, trust and privacy.
Become familiar with the security issues and the technologies involved in modern information systems.
Gain understanding on and be able to perform analysis of vulnerabilities, threats and impact of attacks to information systems.
Gain the required qualifications for fortifying the information system against threats and dangers.General Skills
Search, analysis and synthesis of data and information with the use of the assorted technologies
Adaptation in new conditions
Decision MakingCourse Content
Basic Concepts in Information and Communication Systems Security.
Risk Analysis, Evaluation and Management.
Access Control. Identification and Authentication.
Cryptography and Cryptanalysis principles. Symmetric and Asymmetric Cryptography.
Certificates. Digital Signatures.
Operating Systems, Databases and Network Security.
Malicious Software. Web Services and Security.
Trust Technologies.
Security in Ubiquitous Computing (Grid and Cloud Computing, Internet of Things).
Applications-Case Studies.
Personal Data. Privacy. Legal Framework (national and European level) and Ethical Issues.Learning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
Course Organization
Assessment
Literature
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Information Systems
ΥΠ28 - Information Systems
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Information Systems
Course id: ΥΠ28
Type: Core Course
Semester: 5
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page:
Activities
Lectures (Theory): 3,0
Lab lectures: 2,0
ECTS credits: 5,0
Learning Outcomes
General Skills
Course Content
Learning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
Course Organization
Assessment
Literature
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Simulation
ΥΠ24 - Simulation
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Simulation
Course id: ΥΠ24
Type: Core Course
Semester: 6
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page:
Activities
Lectures (Theory): 3,0
Lab lectures: 2,0
ECTS credits: 5,0
Learning Outcomes
The objective of the course is the introduction to basic principles of Simulation and the exploration of modeling and simulation areas and techniques. Also, introduction to the art of model design and performing simulation studies.
General Skills
-Retrieve, analyse and synthesise data and information, with the use of necessary technologies
-Adapt to new situations
-Make decisions
-Work autonomously
-Promoting free, creative and deductive reasoningCourse Content
Principles of simulation, discrete event simulation, simulation phases, types of simulation, discrete event simulation, object oriented simulation, input modeling and random number generation, model validation, model validation,, experimentation, output analysis, design and implementation simulation studies. Examples using Arena software
Learning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
Course Organization
Assessment
The course grade takes into account
- the final exam grade (60%), which comprises
- Multiple choice questions
- Problem solving
- Critical evaluation of theoretical knowledge
- one compulsory assignment (40%).Literature
- A.M. Law, W.D. Kelton, Simulation Modeling and Analysis, McGraw Hill
● D. Kelton, Simulation with Arena, McGraw Hill
● B. Zeigler, H. Praehofer, T. Kim, Theory of Modeling and Simulation, Academic Press
● P. Fishwick, Simulation Model Design and Execution, Prentice Hall
- A.M. Law, W.D. Kelton, Simulation Modeling and Analysis, McGraw Hill
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Telecommunication Systems
ΥΠ14 - Telecommunication Systems
General Information
School: Digital Technology
Department: Informatics and Telematics
Level: Undergraduate
Course Title: Telecommunication Systems
Course id: ΥΠ14
Type: Core Course
Semester: 5
Teaching and Examination Language: Greek
Is the course offered in Erasmus: Yes
Course web-page: https://eclass.hua.gr/courses/DIT149/
Activities
Lectures (Theory): 3,0
Lab lectures: 0,0
ECTS credits: 5,0
Learning Outcomes
The aim of the course is to understand the basic principles of modern communication systems both wired and wireless. The main learning outcomes of the course are the following:
• Basic concepts of telecommunication systems.
• Basic forms of configuration and transmission of information.
• Information theory and information entropy.
• Coding.
• Compression.
• Calculation of telecommunication systems performance
• Broadcast and Receive Spectrum.General Skills
Independent Work
Work in Teams
Promoting free, creative and deductive thoughtCourse Content
1. Fourier transforms
2. Random Signals
3. Information Theory I.
4. Information Theory II
5. Sampling theorem
6. Amplitude modulation
7. Phase and quadrature amplitude modulation
8. Intersymbol interference
9. Linear block codes I
10. Linear block codes II
11. Cyclic codes I.
12. Cyclic Codes IILearning and Teaching Methods - Evaluation
Teaching methods: face-to-face
Use of ICT:
eclass, youtube channel
Course Organization
Assessment
Written final exam (100%) that includes:
Theory
ExercisesLiterature
1. S. Haykin and M. Moher, Communication Systems 5th Edition, Wiley, 2009
2. J. Proakis and M. Salehi, Fundamentals of Communication Systems 2nd Edition, Pearson, 2013ΙΕΕΕ Transactions on Communications