Cotalogue_ECTS_DoctoralStudies_20015

Transcription

Doctoral studies – ECTS
Automatics and Robotics, Electrical Engineering, Computer
Science
Full-time third-cycle level studies
realization: IIE, IIiE, IME, ISSI
Speciality: Third-cycle level studies
Lp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Course name
ECTS
Modelling and
simulation of
systems and
processes
Statistical methods
In techniques
Experimental design
and technique
Decision systems
Advanced
optimisation and
adaptation
techniques
English language I
English language II
Doctoral seminar I
Doctoral seminar II
Doctoral seminar III
Doctoral seminar IV
Doctoral seminar V
Doctoral seminar VI
Doctoral seminar
VII
Doctoral seminar
VIII
16 Facultative lecture A
Facultative lecture
17
B
Facultative lecture
18
C
Facultative lecture
19
D
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)
2
2
2
2
2
1
2
1
2
1
2
2
1
2
2
2
2
2
2
2
1
1
1
1
1
1
2
1
2
2
2
1
Profesional activities (facultative)
1
1
2
1
2
1
Didactic activities (facultative)
Facultative lecture 20
univeristy-wide A
Facultative lecture 21
univeristy-wide B
22
Didactic classes
Total number of
hours / ECTS
3
3
2
45
1
1
Apprenticeship (facultative)
1
1
1
1
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:
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
polish/english
prof. dr hab. inż. Dariusz Uciński
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
30
2
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
1
exam
2
full-time
compulsory
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
Verification method
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
polish/english
dr hab. inż. Wiesław Miczulski, prof. UZ
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
2
exam
2
full-time
compulsory
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
Verification method
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
polish/english
prof. dr hab. inż. Marian Adamski
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
2
exam
2
full-time
compulsory
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.
2.
3.
4.
5.
6.
7.
Representation of declarative knowledge. Specification of decision systems: symbolic rule systems, decision
tables, decision graphs. Equilibrium of various specification forms of decision systems.
Rule-based systems and their formal representation in the language of mathematical logic.
Decision graphs: general form of decision graphs, binary decision graphs.
Analysis and size reduction of decision tables.
Classical computer methods of size reduction
Computer reasoning and its role in analysis of decision tables
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
Student workload
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
polish/english
dr hab. inż. Andrzej Obuchowicz, prof. UZ
Name of lecturer:
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
15
1
2
exam
2
full-time
compulsory
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
Verification method
Student workload
Recommended Reading
1)
2)
3)
Rutkowski L., Metody i techniki sztucznej inteligencji, PWN, Warszawa, 2005
Arabas J.: Wykłady z algorytmów ewolucyjnych, WNT Warszawa 2001
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
English
mgr Jolanta Bąk, mgr Wojciech Ciesinski
Name of lecturer:
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
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
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
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
English
Name of lecturer:
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
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
Verification method
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
Polish/English
dr hab. inż. Marcin Witczak, prof. UZ
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
seminar
15
1
semester
form of
reciving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
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
Course code:
polish/english
dr hab. inż. Zbigniew Fedyczak, prof. UZ
Name of lecturer:
dr hab. inż. Zbigniew Fedyczak, prof. UZ
form of
instruction
number of
hours per
semester
number of
hours per
week
semester
form of
receiving a
credit for a
number
of ECTS
mode
of
study
type of
course
course
seminar
15
1
2
grade
2
full-time
compulsory
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
Verification method
- oral presentation
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
Course code:
polish/english
prof. dr hab. inż. Józef Korbicz
Name of lecturer:
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
seminar
15
1
3
grade
2
full-time
compulsory
Aim of the course
- preparation of students to give scientific papers in English language
Course kontent
1.
2.
3.
Presentation of short papers in English language
Preparation of scientific publications for conferences
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
Verification method
- oral presentation
Student workload
Recommended reading
[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
Course code:
polish/english
Name of lecturer:
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
seminar
15
1
4
grade
2
full-time
compulsory
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
Verification method
- oral presentation
Student workload
Recommended reading
Course name:
Doctoral seminar V
Course code:
Polish/English
Name of lecturer:
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
seminar
15
1
5
grade
2
full-time
compulsory
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
Verification method
- oral presentation
Student workload
Recommended reading
Course name:
Doctoral seminar VI
Course code:
polish/english
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
seminar
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
6
grade
2
full-time
compulsory
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
Verification method
- oral presentation
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
Course code:
polish/english
Name of lecturer:
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
seminar
15
1
7
grade
2
full-time
compulsory
Aim of the course
Knowledge in the possibilities of gaining external financial support oriented towards research
Course kontent
1.
2.
3.
4.
5.
6.
Rules of applying for research project from NCN and NCBiR
Possibilities of funding research projects with the EU sources - FP7 and FP8 grants
Operational programme: Human resources and innovative economy
Erasmus program
International cooperation outsider EU
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
Verification method
- oral presentation
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
Course code:
Polish/English
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
seminar
15
1
Semestr
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
8
grade
2
full-time
compulsory
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.
2.
3.
4.
Preparation of the doctoral thesis in the Essentials and editorial way: structure of the dissertation
Preparation of the abstract and presentation for the doctoral defense as well as various applications
Presentation of the procedure of awarding a PhD degree
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
Verification method
- oral presentation
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
Software specification and modelling
Course name:
Polish/English
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
Semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
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
Verification method
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
Polish/english
dr hab. inż. Grzegorz Benysek, prof. UZ
Name of lecturer:
dr hab. inż. Grzegorz Benysek, prof. UZ
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
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
Verification method
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.
Modern control techniques
Course name:
Polish/english
prof. dr hab. inż. Krzysztof Gałkowski
Name of lecturer:
prof. dr hab. inż. Krzysztof Gałkowski
form of
instruction
number of
hours per
semester
number of
hours per
week
semester
form of reciving
a credit for a
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 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
Verification method
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
Polish/english
prof. dr hab. inż. Roman Gielerak
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
3,4
exam
2
full-time
optional
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
Verification method
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
Data warehouses
Course name:
Polish/english
dr hab. inż. Wiesław Miczulski, Prof. UZ
Name of lecturer:
dr hab. inż. Wiesław Miczulski, Prof. UZ
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
3,4
exam
2
full-time
optional
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
Verification method
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.
Intelligent computation
Course name:
Polish/English
dr hab. inż. Marcin Witczak
Name of lecturer:
dr hab. inż. Marcin Witczak
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
15
1
3,4
exam
2
full-time
optional
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
Verification method
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.
Process diagnostics
Course name:
Polish/english
dr hab. inż. Andrzej Pieczyński, prof. UZ
Name of lecturer:
dr hab. inż. Andrzej Pieczyński, prof. UZ
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
semester
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
3,4
exam
2
full-time
optional
Aim of the course
1.
2.
3.
4.
Introduction to structure and functions of advanced fault diagnosis systems
Introduction to fault detection, isolation and identification for industrial processes
Shaping skills of designing hybrid fault diagnosis and fault-tolerant systems
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
Verification method
Student workload
Recommended reading
1.
2.
3.
4.
5.
Diagnostyka procesów. Modele, metody sztucznej inteligencji, zastosowania. Red: Korbicz J., Kościelny J.M.,
Kowalczuk Z., Cholewa W. -Wydawnictwo Naukowo-Techniczne, Warszawa, 2002.
Kościelny J.M.: Diagnostyka zautomatyzowanych procesów przemysłowych. - Akademicka Oficyna Wydawnicza
EXIT, Warszawa, 2001.
Łęski J.: Systemy neuronowo-rozmyte, - WNT, Warszawa. 2008,
Nowicki R.K.: Rozmyte systemy decyzyjne w zadaniach z ograniczona˛ wiedza˛. - Akademicka Oficyna
Wydawnicza EXIT, Warszawa, 2009.
Pieczyński A.: Komputerowe systemy diagnostyczne procesów przemysłowych. - Wydawnictwo Politechniki
Zielonogórskiej, Zielona Góra, 1999.
Fault-tolerant control
Course name:
Polish/english
Name of lecturer:
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
15
1
3,4
exam
2
full-time
optional
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
Verification method
Student workload
Recommended reading
1.
2.
3.
4.
Witczak M.: Modelling and estimation strategies for fault diagnosis of non-linear systems. – Berlin: Springer, 2007
Witczak M.: Fault diagnosis and fault-tolerant control strategies for non-linear systems. – Berlin: Springer, 2013
Intelligent diagnostics systems
Course name:
Polish/English
dr hab. inż. Krzysztof Patan, prof. UZ
Name of lecturer:
dr hab. inż. Krzysztof Patan, prof. UZ
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
15
1
3,4
exam
2
full-time
optional
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
Verification method
Student workload
Recommended reading
1.
2.
3.
4.
Witczak M.: Modelling and estimation strategies for fault diagnosis of non-linear systems. – Berlin: Springer, 2007
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
Polish/English
dr inż. Elżbieta Kołodziejska
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
Semestr
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
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
Verification method
- lecture: test
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
Group work methodology
Course name:
Polish/English
dr Ewa Szumigraj
Name of lecturer:
dr Ewa Szumigraj
form of
instruction
number of
hours per
semester
number of
hours per
week
Semestr
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
lecture
15
1
3
Grade
3
full-time
other
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
Verification method
- lecture: test
Student workload
Recommended Reading
1.
2.
3.
Egan, G. (2002), Kompetentne pomaganie. Poznań: Zysk i S-ka.
Enright, J. (1993), Poradnictwo i terapia bez oporu. (red) Santorski, J. ABC pomocy psychologicznej, Warszawa
Tokarczuk, O. red. Grupa bawi się i pracuje cz. 1 i 2.
Interpersonal Communications
Course name:
Polish/English
polski/angielski
Dr inż. Anna Pławiak Mowna, dr inż. Jacek Bieganowski
Name of lecturer:
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
Semestr
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
4
Grade
3
full-time
other
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
Assessment criteria
Verification method
- lecture: test
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
Managing human teams
Course name:
Polish/English
Daria Zielińska-Pękał
Name of lecturer:
Daria Zielińska-Pękał
form of
instruction
number of
hours per
semester
number of
hours per
week
lecture
15
1
Semestr
form of
receiving a
credit for a
course
number
of ECTS
mode
of
study
type of
course
3
Grade
3
full-time
other
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
Critically assess the application of modern educational tools, their advantages and drawbacks with respect to the education
efficiency: D11
Assessment criteria
Verification method
- lecture: test
Student workload
Recommended Reading
1.
2.
3.
4.
Wyrzykowska, B. Karbowiak, K. Kierowanie zasobami ludzkimi w organizacji
Piotrkowski, K., Światkowski, M.: Kierowanie zespołami ludzi
Fullan, M.:Odpowiedzialne i skuteczne kierowanie szkołą
Clifford, E.: Dyscyplina i kierowanie klasą
Didactic classes
Course name:
06.0-WE-AiRIE-WFOA-D1_9_S3S
Polish/English
Name of lecturer:
UZ staff
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
15
1
2,4,6,8
Test
2 (in 8
sem.)
Fulltime
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

Cotalogue_ECTS_DoctoralStudies_20015

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