Katalog przedmiotow ECTS

Transkrypt

Faculty of Electrical Engineering, Computer
Science and Telecommunications
University of Zielona Góra
INFORMATION BOOKLET
Subject Area: COMPUTER SCIENCE (INFORMATICS)
Second-cycle Level Studies
(Full-time, Part-time)
Academic Year 2011/2012
European Credit Transfer System ECTS
Part II.B
ECTS COURSE CATALOGUE
COMPUTER SCIENCE (INFORMATICS)
SECOND-CYCLE LEVEL STUDY (M.Sc.Degree)
T ABLE
OF CONTENTS
Numerical methods
3
Security Engineering (Information Security)
5
Operational research
7
Digital processing and data compression
9
Data Warehouses
11
Neural and neuro-fuzzy networks
13
Digital system design
15
Computer-aided design
17
Virtual Reality Systems
19
i
S P E C I AL I S T S U B J E C T S
ECTS Course Catalogue Computer Science – second-cycle level
N
NU
UM
ME
ER
RIIC
CA
ALL M
ME
ETTH
HO
OD
DS
S
Co ur s e c o de : 11.9-WE-I-MN-PK1_S2S
T yp e of c o ur s e: Compulsory
E ntr y r e q u ir em e nts : La n gu a ge of i ns tr uc t io n: Polish
Dir ec tor of s t ud i es : Prof. dr hab. inż. Krzysztof Gałkowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er :
Prof. dr hab. inż. Krzysztof Gałkowski, mgr
inż. Łukasz Hładowski
F o r m o f r e c e i vi n g a c r e d i t
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
I
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Mathematics basics. Basic notions and theorems used in numerical analysis. Taylor series.
Numbers and Errors. Decimal, binary and hexadecimal numbers, floating point representations. Error
definitions and most commonly seen error types. Ill-conditioning and numerical stability.
Rootfinding. Bisection, Newton and Secant methods. Errors estimation. Extrapolation. Ill-conditioning
and numerical stability of solutions.
Interpolation. Aims and characterization, Lagrange metod. Newton metod. Errors. Splinem. Hermie
interpolation.
Approximation. Sum-of-squares error minimization. Ortogonal polynomials. Min-max error
minimization. Chebyshev polynomials.
Numerical integration. Trapezoidal and Simpson method. Gauss metod. Terror estimation. Richardson
ekstrapolation.
Solving of the linear algebraic equations set. Gauss elimination method; LU factorization and Doolittle
method. Errors estimation and correction. Numerical stability of solutions and conditional number.
Iterative methods: Jacobi and Gauss-Seidel method.
Basics of solving differential equations. Euler and Runge-Kutta methods.
3
Specialist subjects
LEARNING OUTCOMES:
Experience in computer solving of basic computational problems in Engineering with regard limitations
of floating point arithmetic.
ASSESSMENT CRITERIA:
Lecture – obtaining a positive grade in written or oral exam.
Laboratory – the main condition to get a pass are sufficient marks for all exercises and tests conducted
during the semester.
.
RECOMMENDED READING:
[1] Lloyd N. Trefethen and David Bau, III: Numerical Linear Algebra, SIAM, 1997,
[2] H.M. Antia: Numerical Methods for Scientists and Engineers, Birkhauser, 2000,
[3] Richard L. Burden, J. Douglas Faires, Numerical analysis, Brooks /Cole Publishing Company, ITP An
International Thomson Publishing Company, sixth edition, 1997
[4] Kendall Atkinson, Elementary numerical anlysis, John Wiley & Sons, Inc., second edition, 1993
OPTIONAL READING:
[1] –
4
S
SE
EC
CU
UR
RIITTY
Y E
EN
NG
GIIN
NE
EE
ER
RIIN
NG
G ((IIN
NFFO
OR
RM
MA
ATTIIO
ON
N S
SE
EC
CU
UR
RIITTY
Y))
Co ur s e c o de : 11.9-WE-I-IB-PK3_S2S
Dir ec tor of s t ud i es :
Prof dr hab inż. Eugeniusz Kuriata, Dr inż.
Bartosz Sulikowski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Dr inż. Bartosz Sulikowski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Grade
Grade
5
Part-time studies
Lecture
30
2
I
Laboratory
30
2
Grade
Grade
COURSE CONTENTS:
Information security. Introduction. Definitions. Security infrastructure. Security models.
Legal status. The classified information protection act (in Polish). Secret chambers. Classifications.
System access. System access supervision. User access management. User responsibility.
Systems and telecommunication networks security. Types of attacks. Firewalls. Physical protection
methods.
Security policy. Information security administrator role and tasks.
Cryptography. Symmetric and asymmetric methods. DES, AES standards.
Public key cryptography. RSA algorithm. Application of hash functions in cryptography.
Digital signature. PKI servers.
LEARNING OUTCOMES:
Destructive actions protection of information and applications. Legal status, laws and regulations in the
field of data protection. Computer crimes survey and analysis. Active and passive defense against
threats. Ways to handle with risks and their effects minimization.
Lecture – the main condition to get a pass are sufficient marks in written or oral tests conducted at least
once per semester.
5
Specialist subjects
1.
2.
3.
4.
Denning D. E. R.: Cryptography and Data Security, Addison-Wesley, New York, 1982
Allen J. H.: The CERT Guide to System and Network Security Practices. Boston, MA: AddisonWesley, 2001
Mochnacki W.: Kody korekcyjne i kryptografia, Oficyna Wydawnicza Politechniki Wrocławskiej,
Wrocław, 1997 (in Polish)
Menezes A. J., van Oorschot P. C.: Handbook of Applied Cryptography, CRC Press, 1996
OPTIONAL READING:
[1] –
6
O
OP
PE
ER
RA
ATTIIO
ON
NA
ALL R
RE
ES
SE
EA
AR
RC
CH
H
Co ur s e c o de : 11.9-WE-I-BO-PK4_S2S
Dir ec tor of s t ud i es : Dr inż. Maciej Patan
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr hab. inż. Krzysztof Patan, Dr inż. Maciej
Patan
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
I
Laboratory
30
2
Exam
Grade
Part-time studies
Lecture
18
2
I
Laboratory
18
2
6
Exam
Grade
COURSE CONTENTS:
Linear programming tasks (LPT). Standard formulation of LPT. Method of elementary solutions and
simplex algorithm. Optimal choice for production assortment. Mixture problem. Technological process
choice. Rational programming. Transportation and assignment problems. Two-person zero sum games
and games with nature.
Network programming. Network models with determined logical structure. CPM and PERT methods.
Time-cost analysis. CPM_COST and PERT-COST methods.
Non-linear programming tasks (NPT) – optimality conditions. Convex sets and functions. Necessary and
sufficient conditions for the solution existence in the case without constraints. Lagrange multiplayers
method. Extrema of the function with equality and inequality constraints. Kuhn-Tucker conditions.
Constraints regularity. Conditions of an equilibrium point existence. Least squares method. Quadratic
programming.
Computational methods for solving NPT. Directional search methods: Fibonacci, golden search, Kiefer,
Powell and Davidon. Method of basic search: Hooke-Jeeves and Nelder-Mead. Continuous and discrete
gradient algorithm. Newton method. Gauss-Newton and Levenberg-Marquardt algorithms. Elementary
methods of feasible direction: Gauss-Seidel, steepest decent, conjugate gradient of Fletcher-Reeves,
variable metric of Davidon-Fletcher-Powell. Searching for minimum in the case of constraints: internal,
external and mixed penalty functions, projected gradient, sequential quadratic programming and
admissible directions method. Elements of dynamic programming.
Practical issues. Simplification and elimination of constraints. Discontinuity elimination.
Scaling. Numerical approximation of gradient. Usage of numerical packages. Presentation of
methods implemented in popular environments for symbolic and numerical processing.
7
Specialist subjects
LEARNING OUTCOMES:
Skills and competences in: formulating mathematical programming tasks; constructing models for optimization
problems; solving linear and non-linear programming tasks with constraints; application of optimality conditions; timecost analysis of logistic problems; algorithmic approach to determine optimal solutions; creative usage of existing
numerical packages.
Lecture – the main condition to get a pass are sufficient marks in written or oral tests conducted at least
once per semester.
Project – the main condition to get a pass are sufficient marks for all exercises and tests conducted
1.
2.
3.
Barkalov, M. Węgrzyn.: Design of Control Units with Programmable Logic, University of Zielona
Góra Press, Zielona Góra, 2006
M. Adamski, A Barkalov: Architectural and sequential synthesis of digital devices, University of
Zielona Góra Press, Zielona Góra, 2006
A. Barkalov, L. Titarenko.: Logic synthesis for compositional microprogram control units, Lectures
Notes Electrical Engineering, V.22, Springer, 2008.
OPTIONAL READING:
[1] –
8
D
DIIG
GIITTA
ALL P
PR
RO
OC
CE
ES
SS
SIIN
NG
G A
AN
ND
D D
DA
ATTA
A C
CO
OM
MP
PR
RE
ES
SS
SIIO
ON
N
Co ur s e c o de : 11.9-WE-I-CPKD-PSW_A6_S2S
Dir ec tor of s t ud i es : Dr inż. Andrzej Popławski
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Dr inż. Wojciech Zając
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
Part-time studies
Lecture
18
2
II
Laboratory
18
2
7
Exam
Grade
COURSE CONTENTS:
Conversion AC of signal. Image and video acquisition.
Filtration, convolution, Fourier transform.
Discrete cosine transform.
Discrete wavelet transform.
Algorithms of entropy coding.
Lossless and lossy data compression, significance of compression.
Image quality measurements.
Image coding standards.
Video coding standards.
LEARNING OUTCOMES:
Abilities and competence in programming of application to process the digital images and video
sequences.
9
Specialist subjects
1.
Lyons R.G.: Introduction to Digital Signal Processing. Warszawa, WKŁ, 2003 (in Polish)
2.
Zieliński T.P.: Digital Signal Processing. From theory to application, Warszawa, WKŁ, 2007 (in
Polish)
3.
Sayood K.: Introduction to Data Compression, READ ME, 2002 (in Polish)
4.
Domański M.: Advanced compression techniques of pictures and video sequences, Poznań, WPP,
1998 (in Polish)
Ohm J. R.: Multimedia Communication Technology, Springer, 2004
Skarbek W.: Multimedia. Algorithms and compression standards, PLJ, 1998 (in Polish)
5.
6.
OPTIONAL READING:
[1] –
10
D
DA
ATTA
A W
WA
AR
RE
EH
HO
OU
US
SE
ES
S
Co ur s e c o de : 11.3-WE-I-HD-PSW_A6_S2S
T yp e of c o ur s e: Optional
Dir ec tor of s t ud i es : Dr hab. inż. Wiesław Miczulski, prof. UZ
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr hab. inż. Wiesław Miczulski, prof. UZ , Dr
inż. Robert Szulim
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction. Decision support systems. Operational processing versus analytical processing.
Data warehouses. Definition of Data Warehouse. Features of Data Warehouse. Exemplary applications.
Architectures of Data Warehouses. Layered structure of the Warehouse: data sources, extraction layer,
cleaning, transformation and data loading, data access layer. Tools for designing, building, maintaining
and administering of the Data Warehouse.
Multidimensional data models. Models: MOLAP, ROLAP, HOLAP. Building of exemplary data cube.
Data Mining. Data preparation process. Selected Data Mining methods: classification,
grouping, regression, discovering association and sequences, time series. Knowledge
representation forms: logical rules, decision trees, neural nets, Exemplary Data Mining
applications.
LEARNING OUTCOMES:
Skills and competences in: designing and maintaining of data warehouses, designing and
conducting data analysis based on OLAP technology and selected data mining techniques.
11
Specialist subjects
1.
2.
3.
4.
5.
Hand D., Mannila H., Smyth P.: Principles of Data Mining. Massachusetts Institute of Technology,
2001.
Jarke M., Lenzerini M., Vassiliou Y., Vassiliadis P.: Fundamentals of Data Warehouses. SpringerVerlag, Berlin, 2002.
Larose D.T.: Discovering Knowledge in Data. An Introduction to Data Mining. John Wiley & Sonc,
Inc., 2005.
Larose D.T.: Data Mining Methods and Models. John Wiley & Sonc, Inc., 2006.
Poe V., Klauer P., Brobst S.: Building a Data Warehouse for Decision Support. Prentice-Hall, Inc., a
Simon & Schuster Company, 1999.
OPTIONAL READING:
[1] –
12
N
NE
EU
UR
RA
ALL A
AN
ND
D N
NE
EU
UR
RO
O--FFU
UZZZZY
Y N
NE
ETTW
WO
OR
RK
KS
S
Co ur s e c o de : 11.9-WE-I-SNSR-PSW_A6_S2S
Dir ec tor of s t ud i es : Prof. dr hab. inż. Józef Korbicz
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : Prof. dr hab. inż. Józef Korbicz
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
30
2
II
Laboratory
30
2
Exam
Grade
7
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Exam
Grade
COURSE CONTENTS:
Introduction to neural networks. History and development of neural networks. Structure of biological
neuron. Mathematical model of artificial neuron. Neuron activation functions. Perceptron. Learning
algorithm for perceptron. Adaline and Madaline structures. Supervised and unsupervised learning
methods. Classical XOR problem.
Feedforward neural networks. Fundamentals of multilayer neural networks. Backpropagation algorithm
for neural network learning. Issues and limitations of gradient descent learning algorithms. Adaptive
learning rate. Momentum. Example applications of neural networks. Review of advanced learning
algorithms. Evolutionary algorithms for neural network design and learning. GMDH type networks.
Recurrent neural networks. Dynamic-feedback neural networks. Learning algorithms for feedback neural
networks. Mathematical model of dynamic neuron. Locally recurrent globally feedforward neural
networks. Hopfield networks. Learning algorithms for Hopfield network.
Self-organizing neural networks. Kohonen self-organizing feature maps. Competitive learning. Neural
gas algorithm. Example applications of Kohonen network.
Neuro-fuzzy systems. Fuzzy sets and fuzzy logic. Fuzzy inference. Mamadani type neurofuzzy networks. Takagi-Sugeno neuro-fuzzy networks. Learning algorithms for neuro-fuzzy
networks.
LEARNING OUTCOMES:
Skills and competences in: using and implementing neural networks and neuro-fuzzy
networks, properties of different structures of neural and neuro-fuzzy networks,
understanding mathematical principles of learning algorithms, using and
implementing learning algorithms, knowledge about limitations of learning
13
Specialist subjects
algorithms, applying neural network and neuro-fuzzy networks to model nonlinear
systems or pattern recognition .
[1] Haykin S.: Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998
[2] Rutkowska D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag, 2002
[3] Bishop M.: Neural Networks for Pattern Recognition, Oxford University Press, 1996
[4] Rutkowski L.: Computational Intelligence, Springer-Verlag, 2008
[5] Nauck D., Kruse R., Klawonn F.: Foundations of Neuro-Fuzzy Systems, John Wiley & Sons, 1997.
OPTIONAL READING:
[1] –
14
D
DIIG
GIITTA
ALL S
SY
YS
STTE
EM
M D
DE
ES
SIIG
GN
N
Co ur s e c o de : 11.9-WE-I-PSSI-PSW_B7_S2S
Dir ec tor of s t ud i es : Prof. dr hab. inż. Alexander Barkalov
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Dr inż. Grzegorz Łabiak, dr inż. Remigiusz
Wiśniewski
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Basic principles for control units’organization. Methods for presentation and interpretation of control
algorithms; Methods of control units’ organization for programmable logic devices.
Systems-on-Programmable-Chip:analysis and characteristics. Evolution of programmable logic;
Foundations of System-on-Programmable-Chip; Analysis of control units as the parts of SoPC.
Design of Moore control unit. Design of Moore FSM with trivial state encoding; Design of Moore FSM
with optimal state encoding; Design of Moore FSM with transformation of the code sates; Design of
Moore FSM with multilevel structure.
Design of microprogram control units I. Basic principles for organization and design of microprogram
control units; Design of microprogram control units with natural addressing of microinstructions; Design
of microprogram control units with combined addressing of microinstructions.
Design of microprogram control units II. Design of compositional microprogram control units
with a base structure; Design of compositional microprogram control units with common
memory; Design of compositional microprogram control units with address transformation;
Design of compositional microprogram control units code sharing.
LEARNING OUTCOMES:
Skills in design of control units; synthesis and analysis of control units with different
types; choice of the proper model of control unit based on analysis of the particular
project requirements .
15
Specialist subjects
[6] Haykin S.: Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1998
[7] Rutkowska D.: Neuro-Fuzzy Architectures and Hybrid Learning, Physica-Verlag, 2002
[8] Bishop M.: Neural Networks for Pattern Recognition, Oxford University Press, 1996
[9] Rutkowski L.: Computational Intelligence, Springer-Verlag, 2008
[10]Nauck D., Kruse R., Klawonn F.: Foundations of Neuro-Fuzzy Systems, John Wiley & Sons, 1997.
OPTIONAL READING:
[2] –
16
C
CO
OM
MP
PU
UTTE
ER
R--A
AIID
DE
ED
D D
DE
ES
SIIG
GN
N
Co ur s e c o de : 11.9-WE-I-KWP-PSW_B7_S2S
Dir ec tor of s t ud i es : dr inż. Janusz Kaczmarek
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr inż. Janusz Kaczmarek
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Introduction to the computer-aided design of electronic circuits. Historical outline. Overview of Electronic
Design Automation systems. Basic notions and definitions. Imperial and metric system of units.
Methodology of designing an electronic circuit using EDA system. Basic concepts on capturing a circuit
as a schematic diagram: netlist, wires and buses. Component library structure: part, symbol, package
and padstack. Creating schematic diagrams with hierarchical and multipage techniques. Printed Circuit
Board designing using layout editor. Methods of placing components and routing traces. Designing one,
two and multilayer PCB. Automatic routing of PCB traces with an autorouter tool. Design rule check in
EDA systems.
Printed Circuit Board designing for EMC requirements. Basic knowledge of RF emissions and
susceptibility of electronic circuits. PCB EMC techniques: circuit zoning, suppressing interfaces between
circuit zones, ground system, power routing and decoupling, signal routing and line termination. Signal
integrity and transmission lines on PCB.
Computer simulation of electronic circuits. SPICE simulation fundamentals. Types of simulation analysis:
nonlinear dc, small signal ac, transient, sensitivity and distortion. Models of electronic devices.
Schematic-level simulation of embedded microprocessor systems. Analysis of simulation results.
Computer simulation of thermal and electromagnetic properties of printed circuit boards.
Producing design documentation and CAM files in EDA system.
17
Specialist subjects
LEARNING OUTCOMES:
Know-how and competences in the field of applying Electronic Design Automation software
supporting the process of designing electronic circuits with emphasis on embedded
microprocessor systems.
1.
2.
3.
4.
5.
Rymarski Z.: Materials technology and construction of electronic circuits. Designing and production
of electronic circuits, Wydawnictwo Politechniki Śląskiej, Gliwice, 2000 (in Polish)
Williams T.: The Circuit Designer's Companion, Newnes, 2005
Michalski J.: Technology and Assembly of Printed Circuit Boards, WNT, Warszawa, 1992 (in Polish)
Dobrowolski A.: Under the mask of SPICE, BTC, Warszawa, 2004 (in Polish)
Sidor T.: Computer analysis of electronic measurement circuits, Uczelniane Wydawnictwa
Naukowo-Dydaktyczne AGH, Kraków, 2006 (in Polish).
OPTIONAL READING:
[1] –
18
V
VIIR
RTTU
UA
ALL R
RE
EA
ALLIITTY
Y S
SY
YS
STTE
EM
MS
S
Co ur s e c o de : 11.3-WE-I-SWR-PSW_B7_S2S
Dir ec tor of s t ud i es : dr hab inż. Sławomir Nikiel
Semester
Number of
teaching hours
per week
Form of
instruction
Number of
teaching hours
per semester
Nam e of lec t ur er : dr hab inż. Sławomir Nikiel
for a course
Number of
ECTS
credits
allocated
Full-time studies
Lecture
15
1
Laboratory
30
2
Project
15
1
Grade
II
Grade
Grade
6
Part-time studies
Lecture
18
2
II
Laboratory
18
2
Grade
Grade
COURSE CONTENTS:
Human factors. Human perception, definition of human senses. Content creation process: authoring,
distribution and viewing. Interaction modalities, sense of ‘presence’ in virtual environments.
Introduction to virtual reality-related technologies: Introduction to Virtual Environments (VE), historical
background, classification, technological demands, enabling technologies, VE applications. 3D game
programming environments. Application case studies in education, entertainment, architecture, industry
and healthcare.
Input/Output interfaces.VE hardware and software: visual, audio, multimodal, haptic and olfactory
interfacing. Brain-Computer Interfaces (BCI).
3D computer graphics. Geometric modeling, transformations in 3D space, navigation and scene
viewing. Virtual reality as a real-time computer graphics. World construction, scene graphs, building
elements for interactive environments. Object representation and transformations/deformations, terrain
models. Shading and shadows. Texture models and materials.
Animation and interactions in VR. Animation of position, orientation and scaling. Key-frame animations,
physical-based simulations, morphing and warping. Concepts of sensors and triggers. Collision
detection. Interaction with user.
Web-based VR. Introduction to Virtual Reality Modeling Language (VRML) and eXtensible 3D (X3D).
Modeling distributed VR environments (background, objects, actions)
VR modeling tools. Efficiency of geometrical modeling. 3D sound. Level of detail, normal
mapping and progressive meshes. Scripting and PROTO-typing. XNA and shaders.
19
Specialist subjects
LEARNING OUTCOMES:
Analysis and design of real-time computer graphics systems, design of virtual reality systems
based on X3D and XNA technologies; Preparation of media components for virtual reality
applications and 3D games.
[1] Vince J.: Virtual Reality Systems, Addison Wesley, Cambridge, 1995
[2] Ames A. et al: VRML Sourcebook, Wiley, 1997
[3] Arnaud R., Barnes M.C.: Collada, sailing the gulf of 3D digital content creation, A.K. Peters, 2006
[4] Sarris N., Strintzis M.G.: 3D Modeling and Animation Synthesis and Analysis Techniques for the
Human Body, IRM Press, 2005
[5] Vince J.: Interacting with virtual environments, Wiley, 1994
[6] Materials of PEACHbit consortium.
OPTIONAL READING:
[1] –
20
21

Katalog przedmiotow ECTS

Transkrypt

Podobne dokumenty

Semester Course of Polish Language

Data wydruku: 03.11.2016 20:28 Strona 1 z 2 Subject name Subject

Prezentacja

Elective courses for Civil Engineering program, spring semester

24.01.2017 18:50 Strona 1 z 3 Subject name Applied Mathematics

Prof. dr hab. inż. Jerzy Buzek „Scientific debut 2016 – Sustainable

Grading Scale.RTF

Zajęcia w językach obcych - i