3089 English language
3rd Semester ECE
Συνδιδασκαλία: 1254
ECTS : 2
Study Load : theory 2, lab 0
Language : el, en
Learning Outcomes :
The English language course aims at familiarising students with language use in a variety of social contexts and communicative tasks (development of linguistic awareness). A range of practical activities in advanced syntactic structures are regularly provided along with activities designed to develop understanding and production of both spoken and written language. The level attained at the end of the semester is C1, as it is defined by the Common European Framework of Reference for Languages.
3090 French language
3rd Semester ECE
Συνδιδασκαλία: 1255
ECTS : 2
Study Load : theory 2, lab 0
Language : el
Learning Outcomes :
3386 Introductory Lab of Electronics and Telecommunications
3rd Semester ECE
ECTS : 5
Study Load : theory 2, lab 2
Language : el
3355 Foundations of Computer Science
3rd Semester ECE
ECTS : 5
Study Load : theory 4, lab 0
Language : el, en
Learning Outcomes : By the end of this course, students will be able to:
-Understand the concepts of alphabets, strings, languages, and grammars.
-Design and analyze finite automata (DFA and NFA) for language recognition.
-Convert between regular expressions and finite automata.
-Minimize finite automata.
-Prove whether a language is regular or non-regular using tools such as the pumping lemma and closure properties.
-Describe and construct context-free grammars (CFGs).
-Construct formal proofs related to automata, languages, and computation.
-Learn how to analyze algorithms and express their time complexity in big-oh notation.
-Learn how to argue about the correctness of algorithms.
-Become aware of basic algorithmic strategies and algorithm design techniques (divide and conquer, Greedy methods, Dynamic programming).
-Understand numerical algorithms for computing gcd, powers of integers and Karatsuba's multiplication algorithm.
-Become familiar with introductory concepts of artificial intelligence.
-To understand the operation of AI algorithms such as solution finding (depth-first, breadth-first, iterative deepening), heuristic solution finding algorithms (hill climbing, best first, branch and bound, A*.
The course aims at a comprehensive introduction of students to fundamental concepts and methods of Computer Science. It consists of the following sections.
**Theory.** Computability and complexity. Efficiency of algorithms. Modeling of computation: automata, Turing machines, random access machines (RAM). Formal languages and grammars. Logic for computer science. Algorithms: techniques and strategies (divide-and-conquer, greedy method, dynamic programming), numerical calculations (GCD, exponentiation, integer and matrix multiplication), graph and network algorithms (shortest paths, minimum spanning trees), string algorithms (encoding, compression, recognition).
**Data management.** Introduction to databases and data models. The entity-relationship model. Relational model and relational algebra. Introduction to SQL. Introduction to application design and development. Current trends: data flows, distributed databases, map-reduce computing model.
**Artificial intelligence.** Introduction to artificial intelligence: historical data, Turing test, modern artificial intelligence and applications, terminology and areas of artificial intelligence. Problem solving with artificial intelligence methods: problem representation in graphs, solution search, solution finding algorithms (depth-first, breadth-first, iterative deepening), heuristic solution finding algorithms (hill climbing, best first, branch and bound, A*), game algorithms (min-max, alpha-beta). Symbolic artificial intelligence: from logic to artificial intelligence, formal knowledge representation, knowledge graphs, automatic reasoning. Machine learning: data analysis, classification and prediction, perceptrons, linear regression, classification with clustering.
3282 Probability Theory and Statistics
3rd Semester ECE
Τομέας: Μαθηματικών Σχολής ΕΜΦΕ
ECTS : 6
Language : el
Sample spaces, events, Probability measures. Conditional Probability, Law of total Probability, Bayes’s formula. Independent events. Elementary Combinatorics. Random variables and their distribution. Special discrete and continuous distributions. Expectation, median, variance, moments. Markov, Chebyshev and Jensen inequalities. Multivariate distributions. Joint, marginal and conditional distributions. Conditional expectation. Independence, measures of correlation. Multidimensional normal distribution. Transformations of random variables and random vectors. Sums/extrema of independent random variables. Law of Large Numbers, Chernoff bounds, Central Limit Theorem. Poisson Processes. Descriptive Statistics. Parameter estimation, bias, Moment Estimator, Maximum Likelyhood Estimator. Confidence intervals.
3371 Organization and Management
3rd Semester ECE
ECTS : 3
Study Load : theory 3, lab 0
Language : el
Learning Outcomes : The course is a general-background course, providing the basic concepts of organization and management, both at the theoretical and practical level. The course material aims to introduce students to the concepts of management theory and to the analysis of case studies—problems / strategies of Greek and foreign enterprises. At the same time, the course aims to familiarize students and future engineers with topics related to the field of management and organization.
Upon successful completion of the course, students will be able to:
• Understand and become familiar with the basic principles of the scientific field of Organization and Management (definition of the enterprise, objectives and types of enterprises, the business environment—basic concepts of economic theory, functions and rules of the market, forms of organization and competition), which are approached systemically in order to meet the needs of new engineers.
• Understand the business environment (external, operational, and internal), while using methodologies such as PEST analysis, SWOT analysis, and Porter’s Five Forces model.
• Understand and become familiar with the basic functions of an enterprise, such as production management, marketing management, financial management, and human resource management.
• Understand the basic principles of financial operations and apply related tools in corresponding case studies.
• Understand the principles and functions of management (planning, organizing, controlling, leading).
• Use tools and techniques that support managerial decision-making for conducting forecasts, statistical distributions, hypothesis testing, decision trees and matrices, simulations, and applications of linear and dynamic programming.
• Understand and comprehend the basic principles and processes of Marketing, becoming proficient in concepts and topics of corporate strategy, marketing information, marketing strategy, and the marketing mix.
• Investigate and analyze business practices and strategies in the rapidly changing environment of the last decade, while understanding the contribution and influence of technology on business decision-making.
• Utilize organizational and managerial knowledge as well as the skills they have acquired, which constitute essential assets for the new scientist/engineer, who is expected to meet the demands of modern business and organizational management.
Definition of the enterprise, goals and types of enterprises, the business environment (basic principles of economics, operation and rules of the market, modes of organization and competition), systemic approach. Business environment: external, functional and internal environment, PEST and SWOT analysis, Porter’s five forces model. Basic functions of the enterprise: production management, marketing, financial management, human resources management. Principles and functions of management: planning, organization, control, leadership. Tools and techniques for management decision support. Forecasting, statistic distributions, hypothesis testing, decision matrices and trees, simulation, linear and dynamic programming. Project Management. Investigation and analysis of business practices and strategies in the rapidly changing environment of the last decade, contribution and influence of technology in business decision making. Case studies.