automatics and robotics, electrical engineering, computer science › attachments › article ›...

Doctoral studies – ECTS

Automatics and Robotics, Electrical Engineering, Computer Science

Speciality: Third-cycle level studies

Full-time third-cycle level studies realization: IIE, IIiE, IME, ISSI

Lp Course name ECTS

Course schedule in sem. (week hours)

sem. 1 sem. 2 sem. 3 sem. 4 sem. 5 sem. 6 sem. 7 sem. 8

w c L p w c l p w c L p w c l p w c l p w c l p w c l p w c l p

Field/research-oriented (compulsory)

1

Modelling and simulation of systems and processes

2 2

2 Statistical methods

In techniques 2 2

3 Experimental design

and technique 2 1

4 Decision systems 2 1

5

Advanced optimisation and

adaptation techniques

2 1

6 English language I 2 2

7 English language II 2 2

8 Doctoral seminar I 1 1

9 Doctoral seminar II 2 1

10 Doctoral seminar III 2 1

11 Doctoral seminar IV 2 1

12 Doctoral seminar V 2 1

13 Doctoral seminar VI 2 1

14 Doctoral seminar

VII 2 1

15 Doctoral seminar

VIII 2 1

Profesional activities (facultative)

16 Facultative lecture A 2 1

17 Facultative lecture

B 2 1


C 2 1


D 2 1

Didactic activities (facultative)

20 Facultative lecture -

univeristy-wide A 3 1

21 Facultative lecture -

univeristy-wide B 3 1

Apprenticeship (facultative)

22 Didactic classes 2

1

1

1

1

Total number of hours / ECTS

45 5 0 0 0 4 1 0 0 4 0 0 0 4 1 0 0 3 0 0 0 3 1 0 0 1 0 0 0 1 1 0 0

5h / 5p 4h / 8p 4h / 9p 5h / 9p 3h / 4p 4h / 4p 1h / 2p 2h / 4p

Third level studies

Course name: Modelling and simulation of systems and processes

Course code: 06.0-WE-AiRIE-MP-KO_1_S3S

Language of instructions: Polish/English

Director of studies: prof. dr hab. Roman Gielerak

Name of lecturer: prof. dr hab. Roman Gielerak

form of instruction

number of hours per semester

number of hours per

week semester

form of receiving a credit for a

course

number of ECTS

mode of

study

type of course

lecture 30 2 1 exam 2 full-time compulsory

Aim of the course

- introduction to general rules of designing mathematical models - introduction to the methods of numerical analysis in the paralel version. - introduction to computer systems supporting modelling and simulation - introduction the the multi-processor processing

Entry requirements

- numerical methods - essentials of computer-based modeling and simulation

Course kontent

1. Introduction: mathemtical model ling as an iterativa process. :PROBLEM--->Simplificatuion->Working model ->representation-- > Mathematical model ->translation ->Computational model -->simulation -->Results/Conclusions -->Interpretation -->PROBLEM. Elementary examples: Simple mechanical systems, Simple electrical systems, biological models. 2. Nonlinear bahviour and its modelling: complxity of nonlinear systems. Randomness v.s. deterministic chaos. Examples: 1D logistic image. Fractal structures – generation and role in dynamics non-linear systems – application in computer graphics. Lorenz equations and their visualisations. 3. Computer methods: sequential procedures – nonlinear sets of equations, IVP and BVP problems for ODE. Finite element methods for BVP for PDE (simple examples). 4. Computer methods: parallelisation of linear algebra. Sort and search algorithms. 5. Review of computer tools: computer clasters and MPI language, introduction to graphical card-based computations.

Teaching methods

lecture: conventional lecture

Learning outcomes

Skills in designing and testing simple mathematical models: D01, D03, D05 Skills in realizing computer simulations of simple models: D05 Evaluation of computational complexity of the investigated models: D03, D05

Assessment criteria

Verification method - lecture: written exam Final mark components = lecture: 100%

Student workload

Full-time studies (90 h.) Contact hours = 30 h. Preparing for classes= 10 h. Getting familiar with the specified literature = 10 h. Preparation of report = 10 h. Execution of the tasks assigned by the lecturer = 10 h. Classes carried out at the distance = 10 h. Preparing for the exam = 10 h.

Recommended Reading

1.G. Dahlquist , A. Bjorck , Numerical Methods in Scientific Computing, vol 1 + vol .2 , SIAM , Philadelphy , 2008. 2.L.R. Scott, T. Clark , B. Bagheri , Scientific Parallel Computing , Princeton University Press, 2005. 3.N. Bellomo . E. de Angelis , M.Delitala , Lecture Notes in Mathematical Modelling in Applied Science , 2007 4.K.T. Alligood , T. D. Spencer, J.A. Yorke, Chaos: An Introduction to Dynamical Systems, Springer Verlag, New York , 1996. 5.White, R.E. , Compuational Mathematics:models, methods and analysis with Matlab and MPI, Chapman and Hall , 2004.

Course name: Statistical methods in techniques

Course code: 06.0-WE-AiRIE-MSwT-KO_2_S3S

Language of instructions: polish/english

Director of studies: prof. dr hab. inż. Dariusz Uciński

Name of lecturer: prof. dr hab. inż. Dariusz Uciński

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- introduction to elementary qualitative and quantitative of data analysis - forming the critical view on confidence in engineering statistical analysis - forming the skills of estimating uncertainty in practical engineering experiments

Entry requirements

Mathemtical analysis, mathematical essentials of techniques, numerical methods

Course kontent

Measurement uncertainty. Propagation of uncertainty. Random and deterministic errors. Histograms. Measures of concentration, variability, asymmetry. Removing outliers. Probability. Probability definitions: classical, frequency-based and modern. Elementary features of probability. Conditional probability. Independence. Total probability. Bayes equation. Discrete and continuous random variables. Discrete random variables. Distributions: binomial, Bernoulli, Poisson and geometric. Functions of random variables. Expectation and ariance of random variables. Join distribution of two random variables. Independence of random variables. Continuous random variables. Uniform distribution. Exponential distributions. Cumulative distributions. Normal distribution. Essentials of statistical reasoning. Schemes of random selection. Simple trial. Distributions: chi-square, t-Student and Fisher-Snedecor. Point and interval estimation. Unibiasedness, consistency, efficiency. Parametric and nonparametric estimation. Confidence intervals for the expectation. Central limit theorem. Confidence interval for the expectation related to the population of unknown distribution and moments. Testing statistical hypotheses. Parametric significance tests for the expectation, variance and population measures. Nonparametric significance tests. Linear and polynomial regression. Correlation and regression. Least square method. Reasoning in correlation and regression analysis. Linear correlation coefficient.

Teaching methods


Learning outcomes

Has consciousness of the meaning of data analysis in engineering practice D01, D03 Can calculate and interpreter confidence intervals: D01, D05 Knows and understands assumption underlying statistical tests: D01 Can critically assess credibility of statistical analysis: D01, D05

Assessment criteria


Student workload


Recommended Reading

J. Koronacki, J. Mielniczuk, Statystyka dla studentów kierunków technicznych i przyrodniczych, WNT, Warszawa, 2000 Sobczyk M.: Statystyka, PWN, Warszawa, 2000 Ostasiewicz S., Rusnak Z., Siedlecka U.: Statystyka: elementy teorii i zadania, Akademia Ekonomiczna, Wrocław, 1999.

Course name: Experimental design and technique

Course code: 06.0-WE-AiRIE-PiTE-KO_3_S3S


Director of studies: dr hab. inż. Wiesław Miczulski, prof. UZ

Name of lecturer: dr hab. inż. Wiesław Miczulski, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- Introduction to modern methods of experimental design, - Shaping the skills in experimental design and measurement analysis

Entry requirements

Essential knowledge in mathematics, physics, methods and techniques of measurement as well as computer science.

Course kontent

Introduction. The role of experiment in knowledge development, experiment as a source of information, steps of experiment, features of systems, general modeling scheme. Experimental design. The goal of experiment, essential groups of experiments, examples of deterministic design. Concept of the modern theory of experimental design. Applied theory of experimental design, poliselective methods. Computer-supported experimental design. Intelligent systems of experimental design. Concept of intelligent design, rules of creating experimental design, algorithm of generating intelligent design, expert system for experimental design. Technique of experiment. Measurement method, structure of the measurement system, selection of equipment, realisation of measurements. Empirical data analysis. Introduction to analysis, approximation, optimization and measurement uncertainty. Selected examples of experimental design.

Teaching methods

lecture: problem lecture

Learning outcomes

Understands the need for experimental design: D01, D03 Can characterize the concept o f modern theory of experimental design with respect to the classical ones: D01 Can characterize particular steps of experimental design: D01, D06 Can characterize methods of empirical data analysis: D01, D05

Assessment criteria


Student workload


Recommended Reading

1. Brandt S.: Analiza danych. PWN, Warszawa 1998. 2. Górecka R.: Teoria i technika eksperymentu. Wyd. Politechniki Krakowskiej. Kraków 1995. 3. Polański Z.: Badania empiryczne – metodyka i wspomaganie komputerowe. WSPÓŁCZESNA METROLOGIA - Zagadnienia wybrane. Praca zbiorowa pod red. J. Barzykowskiego. WNT, Warszawa 2004. 4. Sydenham P. H.: Podręcznik metrologii, tom 1 i 2. WKiŁ, Warszawa 1988 (t.1) 1990 (t.2). 5. Turzeniecka D.: Ocena niepewności wyniku pomiarów. Wydawnictwo Politechniki Poznańskiej, Poznań 1997.1. Guide to the expression of uncertainty in measurement, 1993-95 ISO. Wyrażanie niepewności pomiaru. Przewodnik - tłumaczenie i

komentarz J. Jaworskiego. Główny Urząd Miar, Warszawa 1999.

Optional Reading

1. Guide to the expression of uncertainty in measurement, 1993-95 ISO. Wyrażanie niepewności pomiaru. Przewodnik - tłumaczenie i komentarz J. Jaworskiego. Główny Urząd Miar, Warszawa 1999. 2. Guide to the expression of uncertainty in measurement. Supplement 1. Numerical methods for the propagation of distributions-propagation of distributions using Monte Carlo method. Dokument opracowany przez Joint Committee for Guides in Metrology (Working Group 1), 2006. 3. Mańczak K.: Technika planowania eksperymentu. WNT, Warszawa 1976. 4. Tumański S.: Technika pomiarowa. WNT, Warszawa 2007.

Course name: Decision systems

Course code: 06.0-WE-AiRIE-SD-KO_4_S3S


Director of studies: prof. dr hab. inż. Marian Adamski

Name of lecturer: prof. dr hab. inż. Marian Adamski

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- introduction to decision systems and the related mathematical tools - Shaping the skills in specification and analysis of decision systems

Entry requirements

Knowledge in graphs theory and formal logic

Course kontent

1. Representation of declarative knowledge. Specification of decision systems: symbolic rule systems, decision tables, decision graphs. Equilibrium of various specification forms of decision systems.

2. Rule-based systems and their formal representation in the language of mathematical logic. 3. Decision graphs: general form of decision graphs, binary decision graphs. 4. Analysis and size reduction of decision tables. 5. Classical computer methods of size reduction 6. Computer reasoning and its role in analysis of decision tables 7. Practical examples of specification and analysis of simple decision systems

Teaching methods

lecture: problem lecture, conventional lecture

Learning outcomes

Has a skill of specifying scientific problems with decision systems as well as efficient search of solutions through the analysis of such systems: D03 Has a skill of appropriate selection and employment of various kinds of decision systems for declarative knowledge representation: D05 Has a skill of gathering information necessary to appropriate representation of scientific problems with rule-based systems: D03 Understands the need of using appropriate mathematic tools for knowledge representation: D01

Assessment criteria

Verification method - lecture: test, written exam Final mark components = lecture: 100%

Student workload

Full-time studies (60 h.) Contact hours = 15 h. Preparing for classes= 8 h. Getting familiar with the specified literature = 8 h. Preparation of report = 8 h. Execution of the tasks assigned by the lecturer = 7 h.

Classes carried out at the distance = 7 h. Preparing for the exam = 7 h.

Recommended Reading

Leszek Rutkowski: Metody i techniki sztucznej inteligencji. Wydawnictwo Naukowe PWN, Warszawa 2005 Mordechai Ben-Ari: Logika matematyczna w informatyce. Wydawnictwa naukowo-Techniczne, Warszawa,2005 Antoni Ligęza: Logical Foundations for Rule –Based Systems. Uczelniane Wydawnictwa Naukowo-Dydaktyczne Akademii Górniczo-Hutniczej w Krakowie, Kraków 2006 Zbigniew Pawlak : Rough Sets – Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht 1991. Wiesław Traczyk: Inżynieria Wiedzy. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2010

Course name: Advanced optimisation and adaptation techniques

Course code: 06.0-WE-AiRIE-ZTOiA-KO_5_S3S


Director of studies: dr hab. inż. Andrzej Obuchowicz, prof. UZ

Name of lecturer: dr hab. inż. Andrzej Obuchowicz, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- introduction to global optimisation - introduction to stochastic optimisation methods - introduction to evolutionary algorithms

Entry requirements

Mathematical analysis

Course kontent

Motivation for using global optimisation. Local and global optimisation. Stochastic, adaptive alogorithm v.s. deterministic optimization algorithms. Examples of stochastic algorithms: Adaptive random search. Examples of using global optimisation. Introduction to evolutionary algorithms. Examples of evolutionary algorithms: genetic algorithm, evolutionary programming, genetic programming and evolutionary search with soft selection. Examples of using evolutionary algorithms.

Teaching methods

lecture: problem lecture, conventional lecture

Learning outcomes

- understands the need for global optimisation in solving scientific problems: D01 - can formulate research problems in the form of global optimisation: D03 - Has knowledge in aplicability of stochastic, adaptive and evolutionary algorithms: D05

Assessment criteria


Student workload


Recommended Reading

1) Rutkowski L., Metody i techniki sztucznej inteligencji, PWN, Warszawa, 2005 2) Arabas J.: Wykłady z algorytmów ewolucyjnych, WNT Warszawa 2001 3) Obuchowicz A.: Evolutionary algorithms for global optimization and dynamic systems diagnosis, Wydawnictwo

UZ, Zielona Góra, 2003

Course name: English language I

Course code: 06.0-WE-AiRIE-JA1-KO_13_S3S

Language of instructions: English

Director of studies: mgr Jolanta Bąk, mgr Wojciech Ciesinski

Name of lecturer: mgr Jolanta Bąk, mgr Wojciech Ciesinski

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course

Lecture 30 2 5 Grade 2 full-time compulsory

Aim of the course

- development of students’ awareness related to the importance of linguistic correctness; - consolidating students’ knowledge of grammar; - shaping the ability to recognize and use appropriate language registers – both in written and spoken English;

Entry requirements

B1 according to the European Framework of Reference for Languages;

Course kontent

- grammar (e.g., grammar tenses, passive voice, reported speech, conditionals, definite and indefinite articles); - formal vs informal English; - academic English – ESP;

Teaching methods

- lecture: brainstorm, work with source document, discussion, consultations, work In groups, practical, classes

Teaching methods

Listening and speaking skills: a student can give detailed information and determine their needs in work environment, they are able to express their point of view effectively: D07 Reading skills: a student understands texts written both in ESP and general English , they can understand most of the scientific articles related to their specialization: D07; Writing skills: a student is able to make notes both for themselves and for the others, they can write an abstract of an article whereby most of the mistakes made by them do not distract the meaning of the text: D07

Assessment criteria

Verification method - lecture: test Final mark components = lecture: 100%

Student workload

Full-time studies (60 h.) Contact hours = 30 h. Preparing for classes= 6 h. Getting familiar with the specified literature = 6 h. Preparation of report = 6 h. Execution of the tasks assigned by the lecturer = 6 h. Classes carried out at the distance = 6 h.

Recommended Reading

1. Headway Academic Skills, Sarah Philpot, Lesley Curnick, Emma Pathare, Gary Pathare & Richard Harrison, OUP 2. FCE Use of English, Virginia Evans, Express Publishing 3. Cambridge Academic English, Craig Thaine , CUP

Optional Reading

English Grammar In Use, 4th edition, Raymond Murphy, OUP

Course name: English language II

Course code: 06.0-WE-AiRIE-JA2-KO_14_S3S

Language of instructions: English

Director of studies: mgr Jolanta Bąk, mgr Wojciech Ciesinski

Name of lecturer: mgr Jolanta Bąk, mgr Wojciech Ciesinski

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course

lecture 30 2 6 Exam 2 full-time compulsory

Aim of the course

- development of students’ awareness related to the importance of linguistic correctness; - know-how of preparing and running a multimedia scientific presentation; - learning the rules of formal correspondence ; - shaping the ability to recognize and use appropriate language registers – both in written and spoken English;

Entry requirements

- B1+ according to the European Framework of Reference for Languages;

Course kontent

- preparation and presentation of a multimedia scientific presentation; - formal vs informal English; - formal letters and e-mails; - academic English – ESP

Teaching methods

- lecture: brainstorm, work with source document, discussion, consultations, work In groups, practical

Learning outcomes

Listening and speaking skills: a student can give detailed information and determine their needs in work environment, they are able to express their point of view effectively: D07 Reading skills: a student understands texts written both in ESP and general English , they can understand most of the scientific articles related to their specialization: D07; Writing skills: a student is able to make notes both for themselves and for the others, they can write an academic paper and its abstract, a student is able to write formal e-mails and letters whereby most of the mistakes made by them do not distract the meaning of the text: D07

Assessment criteria


Student workload


Recommended Reading

1.Headway Academic Skills, Sarah Philpot, Lesley Curnick, Emma Pathare, Gary Pathare & Richard Harrison, OUP 2. FCE Use of English, Virginia Evans, Express Publishing 3. Cambridge Academic English, Craig Thaine , CUP 4. English for Presentations, Marion Grussendorf, OUP

Optional Reading

1. English Grammar In Use, 4th edition, Raymond Murphy, OUP

Course name: Doctoral seminar I

Course code: 06.0-WE-AiRIE-SD1-SO_15_S3S


Director of studies: dr hab. inż. Marcin Witczak, prof. UZ

Name of lecturer: dr hab. inż. Marcin Witczak, prof. UZ

form of instruction


number of hours per

week semester

form of reciving a credit for a

course

number of ECTS

mode of

study

type of course

seminar 15 1 1 grade 1 full-time compulsory

Aim of the course

- shaping skills in preparation and presentation of scientific presentations - shaping knowledge in preparation and publication of scientific works - shaping a habit of reviewing the papers related with particular branch of science

Course kontent

1. Rules of preparing a presentation of scientific works 2. Presentation of papers 3. Rules of preparing papers for conferences and journals 4. Finding source works for scientific research

Teaching methods

- lecture: disscucion, practical, classes, conventional lecture, problem lecture, seminar lecture

Learning outcomes

Can prepare a scientific presentation in a given subject: D01, D03, D06, D07 Can give a short scientific paper: D03, D06 Knows various mechanisms and procedures of publishing scientific works: D01, D05, D06 Can us database sources in order to direct and compare its own research: D03, D07, D12

Assessment criteria

Verification method - oral presentation Final mark components = seminar: 100%

Student workload


Recommended reading

[1] Carmine Gallo, Steve Jobs: Sztuka prezentacji. Jak świetnie wypaść przed każdą publicznością, wyd. Znak, 2012 [2] Janusz Biernat, Profesjonalne przygotowanie publikacji, PW, 2003 [3] Romuald Zabielski Michał M. Godlewski, Przewodnik prezentowania informacji naukowej, SGGW, 2011

Course name: Doctoral seminar II



Director of studies: dr hab. inż. Zbigniew Fedyczak, prof. UZ

Name of lecturer: dr hab. inż. Zbigniew Fedyczak, prof. UZ

form of instruction


number of hours per

week semester


number of ECTS

mode of

study

type of course

course


Aim of the course

- shaping skills in preparation and presentation of scientifically orientem works - knowledge in mechinsms of preparation and publication of scientific works - shaping a habit in reviewing the literature related to a given branch of science

Course kontent

- Continuation of gaining knowledge in formulation, analysis and verification of solutions of scientific problems - Continuation of shaping knowledge in preparation and presentation of scientific problems - Presentation of scientific achievements

Teaching methods

- lecture: disscucion, practical, classes, conventional lecture, problem lecture, seminar lecture

Learning outcomes

Understands the need for a wide description of the scientific area and problems (problems, which has to be solved): D04, D05, D06, D12 Can formulate, decribe and conduct presenation of the scientific area and problems: D01, D02, D03

Assessment criteria


Student workload


Recommended Reading

[. Ustawa z dnia 27 lipca 2005 r. "Prawo o szkolnictwie wyższym" (Dz.U. 2012, poz. 572 – tekst jednolity z dn. 23.05.2012 r.) 2. Klir G.: Ogólna teoria systemów. Tendencje rozwojowe. WNT, Warszawa, 1975 i wyd. późniejsze . 3. Ditrich J.: System i konstrukcja. WNT, Warszawa 1985 i wyd. późniejsze

Course name: Doctoral seminar III



Director of studies: prof. dr hab. inż. Józef Korbicz

Name of lecturer: prof. dr hab. inż. Józef Korbicz

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- preparation of students to give scientific papers in English language

Course kontent

1. Presentation of short papers in English language 2. Preparation of scientific publications for conferences 3. Preparation methods of scientific projects – financing perspectives

Teaching methods

- lecture: practical, conventional lecture, cases method

Learning outcomes

Can give a paper in English language: D01, D03, D06, D07 Can prepare a paper describing research results: D02, D04, D05, D06 Can formulate scientific problems for the purpose of preparing scientific projects: D05, D06 D07, D12

Assessment criteria


Student workload


Recommended reading

[1] Carmine Gallo, Steve Jobs: Sztuka prezentacji. Jak świetnie wypaść przed każdą publicznością, wyd. Znak, 2012 [2] Janusz Biernat, Profesjonalne przygotowanie publikacji, PW, 2003 [3] Romuald Zabielski Michał M. Godlewski, Przewodnik prezentowania informacji naukowej, SGGW, 2011 [4] Barbara Gastel, Writing Scientific Papers in English: Tips and Resources, Texas A&M University, 2010 [5] Writing scientific papers using LaTeX, BibTeX, JabRef, workshop materials

Course name: Doctoral seminar IV



Director of studies: dr hab. inż. Andrzej Obuchowicz, prof. UZ

Name of lecturer: dr hab. inż. Andrzej Obuchowicz, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- knowledge in mechanisms of group project realization with the fokus of comercialization of the achieved results

Course kontent

1. Project realization phases 2. Definition of the project member competences 3. Mechanisms of realizing a research project, resource management, transfer of documents, version control system 4. Cooperation with external enterprises (national and international): other research teams, enterprises 5. Reporting and documentation of work, patent law 6. R+D projects 7. Promotion and dissemination of project results

Teaching methods

- lecture: disscucion, practical, work in gropus, classes, conventional lecture, problem lecture, seminar lecture

Learning outcomes

Can work within group r5ealizing a research project: D01, D02, D03, D04, D05 Can document its own results and joint hem with the results of Rother team members: D04, D05, D06, D07 Knows project realization mechanisms: D12

Assessment criteria


Student workload


Recommended reading


Course name: Doctoral seminar V



Director of studies: dr hab. inż. Wiesław Miczulski, prof. UZ

Name of lecturer: dr hab. inż. Wiesław Miczulski, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- shaping skills in scientific activities in a given branch of science, i.e. control engineering and robotics, electrical engineering and computer science - shaping skills in preparing scientific publications and their appropriate presentation

Entry requirements

Knowledge related to the conducted research of the thesis

Course kontent

Presentation of the motivation of the selected subject of the thesis, objectives and scope of the work. Presentation of the selected research tasks planned for publication in a journal or in a conference proceedings Preparation of scientific projects related with the doctoral thesis

Teaching methods

- lecture: disscucion

Learning outcomes

Can specify and clarify a scientific problem: D01, D02, D03 , D05 Can appropriately specify priorities needed for the realization of a give goal: D04, D05, D07 Has a skill of oral presentation of particular scientific problems in control engineering and robotics, electrical engineering and computer science: D06, D12 Has a sill of writing scientific works: D06, D12

Assessment criteria


Student workload


Recommended reading


Course name: Doctoral seminar VI



Director of studies: prof. dr hab. inż. Dariusz Uciński

Name of lecturer: prof. dr hab. inż. Dariusz Uciński

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Improvement of skills in preparation of scientific papers in English and Polish languages Improvement of skills in presentation and discussion of research results in Polish and English languages

Course kontent

Presentation of draft papers related to the conducted research. Each presentation ends with discussion in which the whole group takes part. As a result, the student can formulate scientific problems, construct the plan of the paper, accurately select the arguments, appropriately select th research method, select appropriate literature use the scientific language. Presentation of the ways of collecting scientific sources (accessibility of sources, use of Internet databases I electronic library sources), work with text in a foreign language, citation, plagiat recognition, graphical shape of the paper.

Teaching methods

- lecture: disscucion

Learning outcomes

Can interpret the collected research material: D01, D02, D03, D04, D05 Can use the source literature and literature of the subject: D1, D2, D3, D4, D5 Can develop and present the research results in a form of scientific paper written in a foreign language: D07 Can appropriately prepare a work with respect to the formal way as well as with the language correctness: D07, D12

Assessment criteria


Student workload


Recommended reading

1. Barbara Osuchowska. Poradnik redaktora i autora. Nauki ścisłe i technika. Wydawnictwo Polskiego Towarzystwa Wydawców Książek, Warszawa, 1988. 2. Literatura przedmiotu wynika z tematyki realizowanej pracy dyplomowej.

Course name: Doctoral seminar VII





form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Knowledge in the possibilities of gaining external financial support oriented towards research

Course kontent

1. Rules of applying for research project from NCN and NCBiR 2. Possibilities of funding research projects with the EU sources - FP7 and FP8 grants 3. Operational programme: Human resources and innovative economy 4. Erasmus program 5. International cooperation outsider EU 6. Presentation of publications and scientific achievements

Teaching methods

- lecture: conventional lecture

Learning outcomes

Knows the external funding sources for scientific research: D12 Can find an appropriate call for project for a given research area: D12 Knows mechanisms of preparing Project proposals and application, constructing budgets of scientific projects: D12 Can present a research problem and the associated effects: D01, D02, D03, D04, D05, D06, D07

Assessment criteria


Student workload


Recommended Reading

[1] Ustawa z dnia 30 kwietnia 2010 r. o zasadach finansowania nauki [2] Ustawa z dnia 14 czerwca 1960 r. – Kodeks Postepowania Administracyjnego [3] Przewodnik po 7. Programie Ramowym [4] Praktyczny przewodnik po funduszach UE na badania i innowacje

Course name: Doctoral seminar VIII



Director of studies: prof. dr hab. inż. Józef Korbicz

Name of lecturer: prof. dr hab. inż. Józef Korbicz

form of instruction


number of hours per

week Semestr


course

number of ECTS

mode of

study

type of course


Aim of the course

- introduction to the preparation of the doctoral thesis as well as presentation during the doctoral defense - introduction to the detailed procedure of awarding a PhD degree

Course kontent

1. Preparation of the doctoral thesis in the Essentials and editorial way: structure of the dissertation 2. Preparation of the abstract and presentation for the doctoral defense as well as various applications 3. Presentation of the procedure of awarding a PhD degree 4. Presentation of achievement related to the doctoral thesis

Teaching methods

- lecture: conventional lecture, seminar lecture

Learning outcomes

Knows appropriate structure of the dissertation: D02, D03, D06 Can prepare the abstract and final presentation for the defense: D01, D05, D07, D12 Knows procedure of awarding a PhD degree: D07

Assessment criteria


Student workload


Recommended Reading

USTAWA z dnia 14 marca 2003 r. o stopniach naukowych i tytule naukowym oraz o stopniach i tytule w zakresie sztuki

Facultative courses – professional activities A, B, C i D

Course name: Software specification and modelling




form of instruction


number of hours per

week Semester


course

number of ECTS

mode of

study

type of course

lecture 15 1 3,4 Exam 2 full-time optional

Aim of the course

Introduction to selected elements of software engineering and examples of software modelling

Entry requirements

Programming languages

Course content

Selected elements of software engineering: concept modelling. Role of specification and model ling in software design. Historical overview of modern model ling techniques. Object-oriented programming and UML notation. Unified Modelling Language UML. Genesis of rise. Elementary elements of notation. The role of diagrams in structural and behavioural modelling. Behavioural modelling (system behaviour): case diagrams of use, action diagrams, state diagrams. Examples of software modelling for embedded systems, application in control engineering, electrical engineering and mechatronics.

Teaching methods

lecture: conventional lecture, problem lecture

Learning outcomes

Has knowledge in application of UML: D01, D03 Knows the role of diagrams in structural and behavioural modelling: D03 Can indicated possible applications of software modelling: D05

Assessment criteria


Student workload


Recommended Reading

Booch G., Rumbaugh J., Jacobson I.: UML: przewodnik użytkownika, WNT, Warszawa 2001 Cheesman J, Daniels J.: Komponenty w UML, WNT, Warszawa, 2004 Muller R.L.: Bazy danyc. Język UML w modelowaniu danych, MIKOM, Warszawa 2000

Course name: Alternative sources of energy and electrical vehicles

Language of instructions: Polish/english

Director of studies: dr hab. inż. Grzegorz Benysek, prof. UZ

Name of lecturer: dr hab. inż. Grzegorz Benysek, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course

lecture 15 1 3,4 exam 2 full-time optional

Aim of the course

- introduction to alternative techniques of electrical energy generation - introduction to the problem of electrical transport - Shaping the understanding of problems related with ecological technologies

Entry requirements

Knowledge in physics and electrical engineering

Course content

Wind energy. Wind conditions in Poland and Europe. Ecological, landscape and environmental causes of using wind installations. Design rules and structures of wind installations. Sun energy. Insolation in Poland. Kinds and structure of solar collectors. Solar heating installation – heating rooms and water. Solar tanks of hot water. Control systems. Pump ensembles. Rules of selecting elements of the system. Examples of installations with solar collectors. Application of electrolysis and hydrogen. Methods of hydrogen generation. Advantages and drawbacks of using hydrogen. Water energy. Influence of water power stations to environmental changes. Ways of designing small water power stations. Geothermal energy. Ways and examples of using geothermal energy. Sources of geothermal energy in Poland. Biogas and biomass. Biomass – sources and production for energy. Advantages and drawbacks. Wood as an ecological and renewable souse of energy. Growing energetic plants in Poland. Liquid biomass. Ecological vehicles. Exemplary solutions. Methods and techniques of storing electrical energy: banks of accumulators, supercapasitors, etc. Selection of energy sources for electrical vehicles. Economy of transport of ecological energy.

Teaching methods

lecture: problem lecture, discussion

Learning outcomes

Has knowledge in alternative sources of producing and exploting electrical energy: D01, D03 Understands the need for developing ecological technologies: D01,D05 Has knowledge in economical aspects of investing in ecological technologies: D01, D03

Assessment criteria


Student workload


Recommended reading

[1] Klugmann E., Klugmann-Radziemska E.: Alternatywne źródła energii. Energetyka fotowoltaiczna, Wydawnictwo Ekonomia i Środowisko, Białystok, 1999. [2] Heier S., Waddington R.: Grid Integration of Wind Energy Conversion Systems, John Wiley & Sons, 2006. [3] Luque A.: Handbook of Photovoltaic Science and Engineering, John Wiley & Sons, 2003. [4] O'Hayre R.: Fuel Cell Fundamentals, John Wiley & Sons, 2006. [5] Jastrzębska G.: Odnawialne źródła energii i pojazdy proekologiczne, WNT Warszawa, 2007/2009.

Course name: Modern control techniques


Director of studies: prof. dr hab. inż. Krzysztof Gałkowski

Name of lecturer: prof. dr hab. inż. Krzysztof Gałkowski

form of instruction


number of hours per

week semester

form of reciving a credit for a

course

number of ECTS

mode of

study

type of course


Aim of the course

- Introduction to selected modern control techniques - Review of solutions - Shaping knowledge in the role of modern control techniques and understanding of their purpose - Shaping the skills of analysis and selection of appropriate control techniques

Entry requirements

Elementary mathematical calculus and linear algebra

Course content

Repetitive processes, model ling and control.Procesy powtarzalne, modelowanie i sterowanie. Essentials of interative learning control. Repetitive control. Predictive control.

Teaching methods


Learning outcomes

Knowledge of selected modern control techniqes: D01 Ability of selecting appropriate control techniques among the known variety of strategies: D01, D03, D05. Skills of designing simple control schemes: D03, D05

Assessment criteria


Student workload


Recommended reading

Kaczorek T., Dzieliński A., Dąbrowski W., Łopatka R.: Podstawy teorii sterowania, WNT, Warszawa, 2006 Greblicki W.: Podstawy Automatyki, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, 2006 Dutton K., Thompson S., Barraclough B.: The Art of Control Engineering, Addison-Wersley, Harlow, Essex, 1997.

Course name: Invitation to quantum algorithms and computation


Director of studies: prof. dr hab. Roman Gielerak

Name of lecturer: prof. dr hab. inż. Roman Gielerak

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Introduction to intelligent computation and quantum algorithms

Entry requirements

Course content

Introduction. History and perspectives of quantum computer sciences. Schor algorithm: threat for RSA cryptography. Quantum theory: early days history. Duality of microworld. Light particles: photons and their properties, polarisation. General structure of quantum mechanics. Representation of location: wave function, Schrodinger equation. Eigenvalues. Spectral distributions. Unitary operations. Quantum mechanics measurements. Heisenberg rules. Complex systems: tensor product description. Q-bits. Elementary quantum gates. Universality of Gates. Discretisation. Quantum circuits. Jotzs-Deutch problem as an example of quantum computer. Quantum search algorithms: Grover quantum machine. Shora algorithm. RSA cryptography. Theoretical analysis of quantum algorithms. Perspectives: hardware side of the project. Physical representation of quantum gates. Quantum cryptography.

Teaching methods


Learning outcomes

- Elementary knowledge in mathematical and physical base of quantum computations: D01 - Elementary knowledge in quantum attack on RSA with the quantum Schor algorithm: D01, D05 - Elementary knowledge in quantum protection of information and quantum transfer of information: D03

Assessment criteria


Student workload


Recommended Reading

1.M.A. Nielsen , I.L.Chuang , Quantum Computation and Quantum Information , Cambridge University Press, 2000 2. I. Bengtsson , K.Życz\kowski ,Geometry of Quantum States. An Introduction to Quantum Entanglement. Cambridge University Press, 2006. 3. K. Giaro ,Elementy Kwantowego Modelu Obliczen i Algorytmiki Kwantowej , Wydawnictwo Naukowe OWSLiZ , Olsztyn 2013 4. Nicolas Gisin, Gre´ goire Ribordy, Wolfgang Tittel, and Hugo Zbinden, Quantum cryptography, REVIEWS OF MODERN PHYSICS, VOLUME 74, JANUARY 2002

Course name: Data warehouses


Director of studies: dr hab. inż. Wiesław Miczulski, Prof. UZ

Name of lecturer: dr hab. inż. Wiesław Miczulski, Prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

- intruduction to architectures and structures of data warehouses - introduction to multidimensional data models - introduction to selected methods of exploration in the process of gathering the data from warehouses

Entry requirements

Elementary knowledge in statistical data analysis, data bases and decision support systems

Course content

Introduction. Terminology related to data exploration. Methodology of data gathering. Problem analysis. Collection and clearing of the data. Design and validation of a model. Queries with respect to the data contained in the model. Maintenance of the model validity. Architecture and infrastructure data warehouses. General characteristic of data warehouses. Live cycle of decision supporting. Multidimensional data models. OLAP system. ROLAP and MOLAP models. Logical models of multidimensional information. Multi-level dimension. Selected exploration methods. Association Discovery. Clustering. Sequence discovery. Similarity Discovery in time series. Examples of exploration.

Teaching methods


Learning outcomes

Understands the Reed for designing data warehouses: D01, D03 Recognizes architectures and structures of data warehouses: D01 Can characterize selected methods of knowledge representation: D01

Can characterize selected data exploration methods: D05

Assessment criteria


Student workload


Recommended reading

1. Poe Vidette, Klauer Patricia, Brobst Stephen: Tworzenie hurtowni danych. WNT, Warszawa 2000. 2. Jarke Matthias, Lenzerini Maurizio, Vassiliou Yannis, Vassiliadis Panos: Hurtownie danych. Podstawy organizacji i

funkcjonowania. WSiP, Warszawa 2003. 3. Ch. Todman, Projektowanie hurtowni danych, WNT, Warszawa, 2003. 4. M. Nycz, Pozyskiwanie wiedzy i zarządzanie wiedzą, Wydawnictwo Akademii Ekonomicznej im. Oskara Langego,

Wrocław, 2003. 5. Barbara Smok, Eksploracja danych w procesie pozyskiwania wiedzy z hurtowni danych, Prace naukowe Akademii

Ekonomicznej we Wrocławiu, 2003.

Course name: Intelligent computation


Director of studies: dr hab. inż. Marcin Witczak

Name of lecturer: dr hab. inż. Marcin Witczak

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Introduction to soft computing. Introduction to advantages and applications of artificial neural networks, evolutionary algorithms and fuzzy systems

Entry requirements

Course content

Introduction. Artificial intelligence and its methodology, efficiency of classical computation and its drawbacks. General properties and features of soft computing. Neuaral networks: Neural structures and their training, multi-layer perceptron. Supervised and unsupervised learning. Applications. Evolutionary algorithms: drawbacks of classical optimization techniques, biological inspirations of evolutionary algorithms, general scheme of an evolutionary algorithm, standard evolutionary algorithms, advanced techniques Artificial immune systems: biological inspiration, immune clustering, optimisation, compression, protection against computer viruses. Fuzzy systems: elementary concepts of fuzzy sets and elementary operations. Fuzzy models: structure, main elements and operations. Application of fuzzy logic: fuzzy controllers, fault diagnosis, fuzzy reasoning. Fuzzy neural networks: design rules and applications

Teaching methods


Learning outcomes

Knows elementary features of soft computing: D01 Can indicate main drawbacks and advantages of soft computing: D05 Can indicate possible applications of soft computing D03

Assessment criteria


Student workload


Recommended Reading

Piegat A.: Modelowanie i sterowanie rozmyte. - Akademicka Oficyna Wydawnicza EXIT, Warszawa, 1999.

Diagnostyka procesów. Modele, metody sztucznej inteligencji, zastosowania. Red: Korbicz J., Kościelny

J.M., Kowalczuk Z., Cholewa W. -Wydawnictwo Naukowo-Techniczne, Warszawa, 2002.

Witczak: Modelling and estimation strategies for fault diagnosis of non-linear systems.

Course name: Process diagnostics


Director of studies: dr hab. inż. Andrzej Pieczyński, prof. UZ

Name of lecturer: dr hab. inż. Andrzej Pieczyński, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

1. Introduction to structure and functions of advanced fault diagnosis systems 2. Introduction to fault detection, isolation and identification for industrial processes 3. Shaping skills of designing hybrid fault diagnosis and fault-tolerant systems 4. Skills in applications of soft computing in fault diagnosis

Entry requirements

Analiza matematyczna, statystyka, algebra, logika rozmyta

Course content

Introduction. Elementary concepts, objectives and tasks of fault diagnosis, diagnosis specification for industrial systems. Concept of process diagnosis. Diagnostics and safety in control schemes. Fault-tolerant structures. Alarm signalling systems. Realization of fault diagnosis. Methods of fault detection. Control limits and data analysis. Relations between signals. Analitical fault detection methods. Kalman filter in fault detection and isolation. Identification-based residual generation. Residual generation with neural networks. Dynamic neural models with non-linear characteristics. GMDH neural models. Application of fuzzy sets and neuro-fuzzy in fault diagnosis. Application of evolutionary algorithms in fault detection. Integration of various techniques. Fault isolation methods. Classification of fault isolation methods. Analytical methods in fault isolation. Bank of observers and parity relations. Pattern recognition for fault isolation. Diagnostic matrix. Bayes theory. Fuzzy logic in fault isolation. Quality indicators for fault diagnosis. DTS, F-DTS, T-DTS monitoring methods. Decentralised structures of fault diagnosis.

Teaching methods

lecture: problem lecture, project method

Learning outcomes

Has knowledge in the functionality of hybrid fault diagnosis: D01 Can prepare a description of diagnostics methods joining elements of soft computing and design a hybryd fault diagnosis:D03 Can design fault diagnosis w neural Network and fuzzy logic and assess its functionality:D05 Is creative in selection of environment for building a complex fault diagnosis system: D01, D03 Can prepare a documentation of the implemented system and takes care about its completness: D05

Assessment criteria


Student workload


Recommended reading

1. Diagnostyka procesów. Modele, metody sztucznej inteligencji, zastosowania. Red: Korbicz J., Kościelny J.M., Kowalczuk Z., Cholewa W. -Wydawnictwo Naukowo-Techniczne, Warszawa, 2002.

2. Kościelny J.M.: Diagnostyka zautomatyzowanych procesów przemysłowych. - Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2001.

3. Łęski J.: Systemy neuronowo-rozmyte, - WNT, Warszawa. 2008, 4. Nowicki R.K.: Rozmyte systemy decyzyjne w zadaniach z ograniczona˛ wiedza˛. - Akademicka Oficyna

Wydawnicza EXIT, Warszawa, 2009.

5. Pieczyński A.: Komputerowe systemy diagnostyczne procesów przemysłowych. - Wydawnictwo Politechniki Zielonogórskiej, Zielona Góra, 1999.

Course name: Fault-tolerant control


Director of studies: dr hab. inż. Marcin Witczak, prof. UZ

Name of lecturer: dr hab. inż. Marcin Witczak, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Introduction to modern control systems with the disturbance and possibile faults

Entry requirements

Elementary mathematical calculus and linear algebra

Course content

Elementary concepts of fault diagnosis: hardware and analitical redundancy, detection, isolation and identification of faults. Classical schemes for fault detection: parameter estimation, parity relation, observers. Control: state feedback control, output feedback control. Control with predefined quality. Fault-tolerant control = fusion of control and fault diagnosis: passive and active schemes. Elementary solutions for fault-tolerant control. Exemplary applications.

Teaching methods


Learning outcomes

Knowledge about elementary features of fault diagnosis: D01, D03 Knowledge about elementary control systems: D01, D03 Knowledge about possibilities of joining control and diagnosis systems: D01, D03 Ability of indicating advantages of fault-tolerant systems: D05

Assessment criteria


Student workload


Recommended reading



3. Witczak M.: Modelling and estimation strategies for fault diagnosis of non-linear systems. – Berlin: Springer, 2007 4. Witczak M.: Fault diagnosis and fault-tolerant control strategies for non-linear systems. – Berlin: Springer, 2013

Course name: Intelligent diagnostics systems


Director of studies: dr hab. inż. Krzysztof Patan, prof. UZ

Name of lecturer: dr hab. inż. Krzysztof Patan, prof. UZ

form of instruction


number of hours per

week semester


course

number of ECTS

mode of

study

type of course


Aim of the course

Introduction to the concept of fault diagnosis with neural networks

Entry requirements

Elementary knowledge in soft computing

Course content

Structure of neural networks. Neural model ling and the associated optimisation processes. Modelling dynamic systems with neural networks. Uncertainty problem in neural modelling. Design of fault detection and isolation, robust methods. Sensor and actuator fault diagnosis. Applications of neural networks in fault diagnosis.

Teaching methods


Learning outcomes

Knows abilities, advantages and drawbacks of artificial neural networks: D01 Knows how to design fault diagnosis with artificial neural networks: D03 Has knowledge in achieving robustness of fault diagnosis: D05

Assessment criteria


Student workload


Recommended reading



3. Witczak M.: Modelling and estimation strategies for fault diagnosis of non-linear systems. – Berlin: Springer, 2007 4. Patan K.: Artificial neural networks for the modelling and fault diagnosis of technical processes. – Berlin: Springer,

2008

Facultative lectures – didactic activities A, B

Course name: Methodology of didactic activities

Course code: 06.0-WE-AiRIE-MZD-KO_6_S3S


Director of studies: dr inż. Elżbieta Kołodziejska

Name of lecturer:

form of instruction


number of hours per

week Semestr


course

number of ECTS

mode of

study

type of course

lecture 15 1 3 Grade 3 full-time other

Aim of the course

The objective of the course is to prepare the students to the role of an academic teacher. In particular, the goal is to train how to manage the tasks related with planning, organization and realization of the education of students. The detailed objectives are: introduction to selected problems of didactics, elementary practical skills necessary for leading the activities with students.

Course kontent

Introduction: elementary concepts of didactics, education objective in higher education, teacher development models. Styles and strategies of teaching adults. Teaching legal basis in higher educations. Academic education as a communication process. Education methods: classification, required methods in higher education: discussion, activation methods, stand-alone work of students. Selection of education methods with respect to objectives and effects of education. New media in academic education. Assessment and verification methods of the student achievements. Design and documentation of courses (exercises, labs, e-learning courses).

Reflective academic teacher – evaluation of activities.

Teaching methods

lecture: classes

Learning outcomes

Explains elementary concepts and problems related with didactics in higher education: D11 Distinguishes the education methods and selects them for the conducted activities: D11 Can plan the didactic activities and prepare a documentation for them: D11 Critically assess the application of modern educational tools, their advantages and drawbacks with respect to the education efficiency: D11 Can assess its own pedagogical preparation, tie it with the evaluation of activities and the need for self-improvement: D11

Assessment criteria


Student workload


Recommended Reading

1. Bereźnicki F., Zagadnienia dydaktyki szkoły wyższej, Szczecin 2009, Rozdział 2. Metody i formy kształcenia s.61-112, Rozdział 3. Przygotowanie pedagogiczne nauczycieli akademickich, s.121-128 2. Edukacja dorosłych, red M.S. Knowles i inni, PWN, Warszawa 2009, Rozdział 10. Poza andragogikę, s. 186-208 3. Media w edukacji - poglądy, zastosowania, społeczne spostrzeganie, red. B. Siemieniecki, T. Lewowicki, Toruń 2010 4. Okraj Zofia, Wspólne nauczanie – uczenie się studenta i nauczyciela akademickiego w dyskusji, [w:] Proces kształcenia akademickiego studenta, red. D. Ciechanowska, Szczecin 2009, s.129-142 5. Pedagogika 2, red. Z. Kwieciński, B. Śliwerski, PWN, Warszawa 2003

Optional Reading

1. Arends R., Uczymy się nauczać, WSiP, Warszawa 1994, 2. Pausch R., Zaslow J., Ostatni wykład, tłum. Jan Kabat, Warszawa 2008, 3. Marciszewski W., Sztuka dyskutowania, Warszawa 1996

Course name: Group work methodology


Director of studies: dr Ewa Szumigraj

Name of lecturer: dr Ewa Szumigraj

form of instruction


number of hours per

week Semestr


course

number of ECTS

mode of

study

type of course


Aim of the course

Student can: lead group educational and general-development activities with active methods, recognize and name fundamental mechanisms ruling group dynamics, roles of group members, using sources about group leading methods and techniques, work actively towards new tools supporting group activities.

Initial requirements

Course content

Building confidence in a group. Contract in helping relation. Group integrating tasks. Contact and communication in

helping relation. Motivation for helping. Terapeutic listening. Psychological border and territory. Elements of assertiveness training. Work on stress. Relaxation methods. Addiction therapy. Relation and bonding in a group. Closing group process.

Teaching methods

lecture: classes

Learning outcomes


Assessment criteria


Student workload


Recommended Reading

1. Egan, G. (2002), Kompetentne pomaganie. Poznań: Zysk i S-ka. 2. Enright, J. (1993), Poradnictwo i terapia bez oporu. (red) Santorski, J. ABC pomocy psychologicznej, Warszawa 3. Tokarczuk, O. red. Grupa bawi się i pracuje cz. 1 i 2.

Course name: Interpersonal Communications


Director of studies: polski/angielski

Name of lecturer: Dr inż. Anna Pławiak Mowna, dr inż. Jacek Bieganowski

form of instruction


number of hours per

week Semestr


course

number of ECTS

mode of

study

type of course


Aim of the course

Development of skills and competences with respect to interpersonal communication in the work group.

Initial requirements

Course kontent

Communication. Verbal, non-verbal and written communication, communication barriers. Social communication conditions. Mistakes in communication with a client. Autopresentation in the work place. Assertiveness and its practical applications. Team. Teams in a work place. Roles in a team. Team development steps. Communication in a team. Team problems. Effective and ineffective behaviour templates. Heuristic techniques for solving team problems. Sources and kinds of conflicts. Role of conflicts. Negotiation.

Teaching methods

lecture: classes

Learning outcomes

Explains elementary concepts and problems related with didactics in higher education: D11 Distinguishes the education methods and selects them for the conducted activities: D11 Can plan the didactic activities and prepare a documentation for them: D11 Critically assess the application of modern educational tools, their advantages and drawbacks with respect to the education efficiency: D11

Can assess its own pedagogical preparation, tie it with the evaluation of activities and the need for self-improvement: D11

Assessment criteria


Student workload


Recommended Reading

1. Balbin R. M.: Twoja rola w zespole, GWP, Gdańsk, 2003. 2. Edelman R. J.: Konflikty w pracy, GWP, Gdańsk, 2005. 3. Fisher R., Ury W.: Dochodząc do tak. Negocjowanie bez poddawania się, PWE, Warszawa, 1992. 4. Gerrig R. J., Zimbardo P.: Psychologia i życie, Wydawnictwo PWN, Warszawa, 2006. 5. Kamiński J.: Negocjowanie. Techniki rozwiązywania konfliktów, POLTEXT, Warszawa, 2003. 6. Leary M.: Wywieranie wrażenia na innych. O sztuce autoprezentacji, GWP, Gdańsk, 2003. 7. Nęcki Z.: Komunikacja międzyludzka, Antykwa, Kraków, 2000

Course name: Managing human teams


Director of studies: Daria Zielińska-Pękał

Name of lecturer: Daria Zielińska-Pękał

form of instruction


number of hours per

week Semestr


course

number of ECTS

mode of

study

type of course


Aim of the course

Developing knowledge about social links and the associated governing rules. Animation towards self-development and development of members of educational processes. Rules and recipes in work group. Essential skills in leading a group.

Wymagania wstępne

Umiejętności interpersonalne, superwizja, znajomość zasad pracy grupy

Course kontent

Managing and leading a group (managing styles). Decision ma king process (decision ma king models, kinds of decisions, decision supporting techniques). Resolving conflicts. Motivation (motivation models, motivation theory, motivation tools). Managing theory (modification of Skinner behaviour, Lee Canter assertive discipline, Jones model)

Teaching methods

lecture: classes

Learning outcomes


Assessment criteria

Verification method - lecture: test

Final mark components = lecture: 100%

Student workload


Recommended Reading

1. Wyrzykowska, B. Karbowiak, K. Kierowanie zasobami ludzkimi w organizacji 2. Piotrkowski, K., Światkowski, M.: Kierowanie zespołami ludzi 3. Fullan, M.:Odpowiedzialne i skuteczne kierowanie szkołą 4. Clifford, E.: Dyscyplina i kierowanie klasą

Course name: Didactic classes

Language of instructions: 06.0-WE-AiRIE-WFOA-D1_9_S3S

Director of studies: Polish/English

Name of lecturer: UZ staff

form of instruction


number of hours per

week Semester


course

number of ECTS

mode of

study

type of course

lecture 15 1 2,4,6,8 Test 2 (in 8 sem.)

Full-time

Other

Aim of the course

Development of skills, knowledge and competences in knowledge dissemination and organization of didactic activities.

Entry requirements

Course content

Subject Dependent

Teaching methods

Excercises, Labs

Learning outcomes

Is able to lead didactic activities with students: D11

Assessment criteria

15 hours of Excercises or Labs

Student workload


Literature

Subject dependent

automatics and robotics, electrical engineering, computer science › attachments › article ›...

Documents