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WYDZIAŁ INFORMATYKI I NAUKI O MATERIAŁACH
KIERUNEK INFORMATYKA
STUDIA II STOPNIA
STUDIA STACJONARNE (od roku akademickiego 20011/2012)
Specjalność: Modelling and Visualisation in Bioinformatics
List of Courses
OBJECT-ORIENTED PROGRAMMING
ALGORITHMS AND COMPLEXITY THEORY
BASICS OF MODELLING AND VISUALIZATION
DATA SECURITY
INTRODUCTION TO BIOINFORMATICS
PROJECT MANAGEMENT
WEB TECHNOLOGIES
ARTIFICIAL INTELLIGENCE
MATHEMATICAL METHODS IN BIOINFORMATICS
INTRODUCTION TO MOLECULAR BIOLOGY AND GENETICS
DATABASES AND DATA WAREHOUSES
MATHEMATICAL AND DIGITAL MODELLING
METHODS OF DATA ANALYSIS
MULTIRESOLUTION IMAGE ANALYSIS
1
Course title:
OBJECT-ORIENTED PROGRAMMING
Prerequisites:
Introduction to Programming
Programming Languages
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
30
45
semester year
1
I
ETCS
7
Instructor:
PhD Roman Simiński
Language of instruction:
English
2
Course Outline:
1. Class Design.
2. Concepts of Object-Oriented programming.
3. Classes, attributes and methods.
4. Introduction to UML.
5. Introduction to Java and Virtual Machine.
6. Introduction to Java applets and applications.
7. Java Application and API.
8. Java primitive data structures.
9. Statements.
10. Console I/O and Exception Handling.
11. Defining Class.
12. Inheritance and Class Hierarchy.
13. Polymorphism.
14. Collections.
15. Arrays.
16. ArrayList and Iterator.
17. Strings.
18. Interfaces.
19. Input and Output Programming.
20. Java Thread Programming.
21. Graphical User Interface.
Objectives of the course:
Upon successful completion of the course, the students should know:
1. How to design and program in the object-oriented paradigm making proper use
of encapsulation, inheritance, and polymorphism.
2. The Unified Modeling Language (UML) diagrams (selected), industry standard to
describe program designs.
3. The Java programming language to implement object-oriented program design.
4. The formulation and implementation of basic data structures, such as lists,
stacks, queues, trees, and tables.
5. Graphical User Interface (GUI) programming using Java GUI components.
6. The basics of component software construction and object-oriented frameworks.
Teaching methods:
Lectures and exercises in designing and programming different problems in the objectoriented paradigm using Java programming language.
Assessment methods:
Projects, tests and a final exam.
3
Textbooks and materials (recommended reading):
1. Ian Graham, Alan O’Callaghan, Alan Cameron Wills, Object-Oriented Methods.
Principles and Practice, Third Edition, Pearson Education Limited 2001.
2. Bruce Eckel, Thinking in Java, Third Edition, Prentice Hall PTR, 2002.
3. Java tutorials: http://java.sun.com/docs/books/tutorial/java/index.html
4
Course title:
ALGORITHMS AND COMPLEXITY THEORY
Prerequisites:
Basic course of Algorithms and Data Structures and Discrete Math.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
2
I
ETCS
5
Instructor:
PhD Barbara Marszał-Paszek
Language of instruction:
English
5
Course Outline:
1. Review of mathematical induction.
2. Asymptotic notation for characterizing algorithm complexity.
3. Basic techniques for complexity analysis of various algorithm patterns.
4. Recursive procedures, proofs by induction, recursion trees.
5. Complexity classes P and NP, NP-completeness, some NP-complete problems.
6. Algorithm design techniques: greedy algorithms, divide and conquer, dynamic
programming, randomized algorithm.
Approximation algorithms.
Objectives of the course:
1. Skills in understanding some fundamental concepts in algorithm design,
verification, and complexity analysis.
2. Identify several design approaches to algorithmic problems.
3. Express algorithms as recurrence relations.
4. Proficiency in using appropriate method for constructing data structures.
5. Choosing appropriate method for constructing effective algorithms.
6. Solving the NP-time problems.
Teaching methods:
Format will center on lecture (with slides), discussion, and directed or group problem
solving.
Assessment methods:
Exam, tests, reports.
Textbooks and materials (recommended reading):
1. Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, 2nd edition,
McGraw-Hill, 2001: entire book.
2. Arora and Barak, Complexity Theory: A Modern Approach, preliminary version
available at http://www.cs.princeton.edu/theory/complexity , 2006: Part I.
3. Lewis and Papadimitriou, Elements of the Theory of Computation, 2nd edition,
Prentice Hall, 1998.
4. Papadimitriou, Computational Complexity, Addison Wesley, 1995.
5. Garey and Johnson, Computers and Intractability A Guide to the Theory of NPcompleteness, Freeman, 1979.
6. Sipser, Introduction to the Theory of Computation, PWS Publishing Company,
1997.
7. Brassard and Bratley, Fundamentals of Algorithmic, Prentice-Hall, Englewood
Cliffs, 1996
8. Knuth, The Art of Computer Programming, Vol. 1-3, Addison-Wesley, 1977-1988.
9. Rogers, Theory of recursive functions and effective computability, Mc Graw Hill
Book Company, New York, 1967.
6
Course title:
BASICS OF MODELING AND VISUALIZATION
Prerequisites:
Mathematical analysis and algebra basis, Programming basis.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
2
I
ETCS
6
Instructor:
Lect: Prof. Wiesław Kotarski,
Labs: MSc Krzysztof Gdawiec, MSc Grzegorz Machnik
Language of instruction:
English
7
Course Outline:
1. Geometry 2D.
2. Geometry 3D.
3. Visualization of functions: curves and surfaces.
4. Modeling of curves and surfaces.
5. Subdivision techniques.
6. Fractals.
7. Constructive Solid Geometry.
8. Visual photorealism: lights, materials, textures .
9. Ray Tracing.
10. Radiosity.
11. Software: Java View, POV Ray, Mayavi, Deep View.
Objectives of the course:
Upon successful completion of the course, the students should know:
1. How to visualize different types of data.
2. How to model shapes and present them photorealistically.
3. What software they can use for the visualization and modeling purposes.
Teaching methods:
Lectures with the usage of audiovisual means. Open source programs such as: Java
View, POVRay, Mayavi, Deep View for creation of photorealistic 3D graphical objects
will be used. During classes students will prepare a short animation.
Assessment methods:
Projects, tests and a final exam.
Textbooks and materials (recommended reading):
1. Agoston M.K., Computer graphics and geometric modeling. Implementation
and algorithms, Springer 2005.
2. Chen C.-H., Hardle W., Unwin A., Handbook of Data Visualization, SpringerVerlag, Berlin, 2008.
3. Hansen C. D., Johnson C. R., The Visualization Handbook, Elsevier, 2005.
4. Joy K. I., On-line geometric modeling notes, Dept. of Computer Science,
University of California, Davis, 2000,
http://www.idav.ucdavis.edu/education/CAGDNotes/homepage.html
5. Marsh D., Applied geometry for computer graphics and CAD, Springer 2000.
6. Schreider P.J., Eberly D.H., Geometric tools for computer Graphics, MorganKaufmann Publishers, San Francisco 2003.
7. Wright H., Introduction to Scientific Visualization, Springer, 2007.
8. Zorin D., Schröder P, Levin A., Kobbelt L., Sweldens W., DeRose T.,
Subdivision for modeling and animations, Course Notes, SIGGGRAPH 2000,
http://mrl.nyu.edu/publications/subdiv-course2000
9. http://www.javaview.de/
10. http://mayavi.sourceforge.net/
11. http://www.subdivision.org
12. http://www.povray.org/
8
13. http://spdbv.vital-it.ch/
Course title:
DATA SECURITY
Prerequisites:
Database systems.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
Type
lectures
Labs
hours
15
45
semester year
1
I
ETCS
5
Instructor:
PhD Romuald Błaszczyk
Language of instruction:
English
9
Course Outline:
1. Data Security Issues and Considerations
2. Identify components of data security.
3. Distinguish between various data security methodologies.
4. Begin to apply data security methodologies in situation cases.
5. Web Security Issues and Concerns
6. Identify components of Web security.
7. Distinguish between various Web security methodologies.
8. Begin to apply data security methodologies in situation cases.
Objectives of the course:
This course introduces security principles and issues that IT professionals must
consider. The course surveys current and emerging security practices and processes as
they relate to; information systems, systems development, operating systems and
programming, database development and management, networking and
telecommunications, and the Internet.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation.
Assessment methods:
Exam, tests, quizzes, projects.
Textbooks and materials (recommended reading):
1. Salomon D., Data Privacy and Security. Springer-Verlag, New York 2006.
2. Anderson R., Security Engineering: A Guide to Building Dependable
Distributed Systems. Wiley Publishing, Indianapolis 2008.
10
Course title:
INTRODUCTION TO BIOINFORMATICS
Prerequisites:
Running knowledge on operating system, operating different computer
application interfaces, installing software, searching The Web. Basics from
biochemistry, molecular biology and genetics, algorithms complexity. Basic
programming and scripting skills.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
seminar
labs
hours
15
30
semester year
2
I
3
II
ETCS
5
Instructor:
PhD Magdalena Tkacz
Language of instruction:
English
11
Course Outline:
1. Bioinformatics – scope, interests, methods.
2. The structure and synthesis of nucleid acids.
3. DNA Sequencing, comparing sequences.
4. The genetic code and gene expression.
5. Gene: gene prediction, genome rearrangement, gene networks.
6. The human genome and the genomics. The Human Genome Project.
7. DNA mapping. Motifs.
8. Technologies in bioinformatics (southern, microarray, RT-PCR).
9. Bioinformatics databases, websites and their contents. Publication databases.
Retrieving information.
10. Evolutionary bioinformatics (molecular evolution): SNPs, alignments and
phylogenetic trees.
11. Protein structure. Structural bioinformatics - predicting proteins structures and
functions. Molecular modeling in drug discovery.
12. Bioinformatics - oriented programming languages and libraries.
13. Clinical implications.
14. Other bio-tech science: computational biology.
15. Systems biology: metabolic pathways, regulatory networks, protein interaction
networks.
In all described above problems appropriate algorithms and data structures are to
be discussed (tree algorithms, combinatorial pattern matching, SVM, ANN, HMM,
clustering and decision trees, exhaustive search, greedy algorithms, combinatorial
pattern matching etc.) with respect to specific bioinformatical context.
Objectives of the course:
1. Well-grounded knowledge about different bioinformatics research areas and
scopes of interest.
2. Working knowledge on different computer science methods and algorithms in
different biomedical fields.
3. General knowledge about bioinformatics resources (databases and publications)
in The Web. Skills in searching The Web.
4. Proficiency in using specialized software, and ability for complete specific
solution development (using available, yet developed applications as well as
creating solution of one’s own: programming, scripting).
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation, computer simulations and hands-on labs.
Assessment methods:
exam, tests, quizzes, projects, hands-on lab.
12
Textbooks and materials (recommended reading):
1. Lesk A.: Introdution to Bioinformatics, Oxford University Press, 2008.
2. Jones N. C., Pevzner P. A.: An Introduction to Bioinformatics Algorithms, Mit
2004.
3. Gibas C., Jambeck P: Developing Bioinformatics Computer Skills O’Reilly 2001.
4. Westhead D. R., Parish H J., R. M. Twyman: Instant Notes: Bioinformatics BIOS
Scientific Publishers Ltd.,Oxford 2002.
5. Shui Qing Ye: Bioinformatics: a practical approach, CRC Press, 2007.
6. Bal H., Hujol J.: Java for bioinformatics and biomedical applications, Springer,
2006.
7. Bujnicki J. M.: Practical bioinformatics, Springer, 2004.
8. Böckenhauer
Hans-Joachim,
Bongartz
Dirk:
Algorithmic
Aspects
of Bioinformatics, Springer, 2007.
9. Flaig Ruediger-Marcus: Bioinformatics Programming in Python: A Practical
Course for Beginners, Wiley-VCH, 2008.
10. Kinser J.: Python for Bioinformatics, Jones & Bartlett Publishers, 2008.
11. Higgs P. G., Attwood T. K.: Bioinformatics and Molecular Evolution, Blackwell,
2005. (Chapters: 5,6,7,8,9,10,13).
13
Course title:
PROJECT MANAGEMENT
Prerequisites:
Database systems, programming.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
labs
hours
45
semester year
4
II
ETCS
2
Instructor:
PhD Katarzyna Trynda
Language of instruction:
English
14
Course Outline:
1. Managing projects within an organizational context, including the processes
related to initiating, planning, executing, controlling, reporting, and closing
a project.
2. Project integration, scope, time, cost, quality control, and risk management.
3. Managing the changes in organizations resulting from introducing or revising
information systems.
4. Managing the project team.
5. IS project management tools.
Objectives of the course:
Students develop detailed project plans, schedules, and budgets; estimate project
resources; allocate/coordinate resources; and interface with management. They are
expected to learn tools and techniques of project planning and management.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation.
Assessment methods:
Exam, tests, quizzes, projects.
Textbooks and materials (recommended reading):
1. Software Engineering, Sommerville, Addison Wesley-Hill, 2001.
2. Software Engineering: A Practitioner’s Approach, Roger S. Pressman,
McGraw-Hill, 2005.
3. www.pmi.org
4. Philips J., IT Project Management. On Track from Start to Finish. OnePress
2004.
5. Microsoft Project Documentation.
15
Course title:
WEB TECHNOLOGIES
Prerequisites:
Basic skills in programming, data bases and work with Internet.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
1
I
ETCS
6
Instructor:
PhD Katarzyna Trynda
Language of instruction:
English
16
Course Outline:
1. Network protocols, Communication models CLIENT/SERVER.
2. Dynamic web content generation – HTML forms, Cookies, Data security and
protection.
3. .Net Framework and components.
4. ASP.NET web developing.
5. Data Access and LINQ.
6. AJAX Technique.
7. SOA – Service Oriented Architecture.
Objectives of the course:
The course offers rapid and profound view of the basic actual Internet Technologies and
their applications in Web Programming.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation and hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
Textbooks and materials (recommended reading):
1. ASP.NET
AJAX
Extender
Controls,
http://msdn.microsoft.com/enus/library/bb532368.aspx
2. Cascading Style Sheets, http://www.w3.org/Style/CSS
3. Open Source Web Design, http://www.oswd.org
4. Stefan Schackow, Professional ASP.NET 2.0 Security, Membership, and Role
Management, Wrox, 2006.
5. M. Ahmen, Ch. Garrett, (and others), ASP.NET web developers guide. Syngress
Publishing, Rockland 2002.
6. MSDN documentation.
17
Course title:
ARTIFICIAL INTELLIGENCE
Prerequisites:
Students should be familiar with uninformed search algorithms (depthfirst and breadth-first methods), discrete probability (random variables,
expectation, simple counting), propositional logic (boolean algebra), basic
algorithms and data structures, basic computational complexity, and basic
calculus. They should have a strong background in computer science,
including a programming course in data structures.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
30
semester year
1
I
ETCS
6
Instructor:
PhD Agnieszka Nowak
Language of instruction:
English
18
Course Outline:
1. Introduction to AI: definitions, approaches as expert systems, related areas,
history of Artificial Intelligence.
2. Logic: Notes on Logic (First Order Logic, Higher Order Logic), Deduction,
Soundness and Completeness, Conjunctive Normal Form, Clausal Form,
Resolution and Unification.
3. Generic Techniques Developed: forward/backward chaining, neural
networks, Bayesian methods, decision trees.
4. Representations/Languages Used: logic, fuzzy logic, production rules,
Bayesian nets, the Prolog Language (Prolog procedures, pattern matching,
data structures, graphs and searching in Prolog).
5. Searching: formulating a search problem, evaluating a search algorithm on
the basis of completeness, optimality, time complexity, and space complexity,
explaining and contrasting uniformed and informed searches, comparing,
contrasting, classifying and implementing various search algorithms including
breadth-first, uniform-cost, depth-first, depth-limited, iterative deepening,
bidirectional, best-first, greedy, A*, hill-climbing.
6. Machine learning: overview of machine learning (aims; representations),
decision tree learning (Decision trees; ID3 method; avoiding overfitting).
7. Practical point of view of AI products: MYCIN, Prolog (inductive logic
programming), C4.5 (decision tree learner), CART.
Objectives of the course:
1. Knowledge and understanding:
 understanding the principles and mechanisms underlying various kinds
of intelligent processes,
 understanding how to deal more effectively with natural intelligence using
AI tools and techniques,
 understanding how to represent and reason about knowledge in
a computer.
2. Intellectual skills:
 specifying and design intelligent and traditional computer-based systems,
using formal design procedures where appropriate,
 identifying problems requiring a combination of techniques from both
Artificial Intelligence and Software Engineering.
3. Practical skills:
 developing and implement intelligent and traditional computer-based
systems,
 use support tools from Artificial Intelligence and Software Engineering
during the development process.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation and hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
19
Textbooks and materials (recommended reading):
1. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach,
Prentice Hall, 2nd edition, 2003. [from website: http://aima.cs.berkeley.edu/]
2. Ivan Bratko. 2001 PROLOG Programming For Artificial Intelligence (3rd ed.
Printed 2001). England. Addison Wesley - ISBN 0-201-40375-7. [or slides
from website: http://www.ailab.si/ivan/] or
[http://www.booksites.net/bratko/]
3. Ulf Nilsson, Jan Małuszyński, Logic, Programming and Prolog, John Wiley
& Sons Ltd 1995 [ full book version at http://www.ida.liu.se/~ulfni/lpp/]
20
Course title:
MATHEMATICAL METHODS IN BIOINFORMATICS
Prerequisites:
Mathematics at the first year non-math faculty university course.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
30
semester year
1
I
ETCS
6
Instructor:
Prof. Ryszard Rudnicki
Language of instruction:
English
21
Course Outline:
1. Elements of the set theory. Set cardinality.
2. Functions and relations. Equivalence relation.
3. Mathematical induction.
4. Elements of the group theory: cyclic group, subgroups, cosets. Groups
of permutation.
5. Boolean and multi-valued logic.
6. Elements of the probability and the measure theory, Hidden Markov Models.
7. Principles of the graph theory. Selected graph algorithms. Graph
representation in data structures.
8. Trees. Tree-related algorithms (with special attention on searching
algorithms).
9. Elements of the group theory: cyclic group, subgroups. Groups
of permutation.
10. Automata, grammars, languages.
11. Differential equations: ordinary, partial.
12. Fractals, L-systems.
Objectives of the course:
Well-grounded knowledge about different areas of applied mathematics |(theory basis
and possible areas of application).
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation, computer simulations and hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
Textbooks and materials (recommended reading):
1. SuppesP: Introduction to logic. Courier Dover Publications, 1999.
2. Isaacs M. A. : Algebra: A Graduate Course. AMS Bookstore, 2009.
3. Bogopolskij O. V.: Introduction to group theory. European Mathematical
Society, 2008.
4. Blanchard P., Devaney L. R., Hall G. R.: Differential equations. Cengage
Learning, 2005.
5. Hopcroft J.: Introduction to Automata Theory Languages and Computation,
Addison-Wesley, 2006.
6. Reinhard Diestel R. :Graph Theory. Birkhäuser, 2006.
22
Course title:
INTRODUCTION TO MOLECULAR BIOLOGY AND GENETICS
Prerequisites:
Knowledge of general, inorganic and organic chemistry.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
2
I
ETCS
6
Instructor:
PhD Magdalena Tkacz
Language of instruction:
English
23
Course Outline:
1. Medicine and Biochemistry – aim, background for Molecular Biology and
Genetics.
2. The cell, water and pH.
3. Proteins, amino acids, peptides.
4. Protein structure.
5. Enzymes – kinetics, activities, regulation.
6. Biologic oxidation, ATP, bioenergetics, the respiratory chain and oxidative
phosphorylation.
7. Carbohydrates.
8. Lipids.
9. Glycolysis, glycogen, gluconeogenesis.
10. Oxidation of fatty acids.
11. Biosynthesis of fatty acids.
12. Metabolism of lipids. Transport and storage.
13. Cholesterol.
14. Catabolism of proteins and amino acids
15. Nucleotides, purines and pyrimidnes.
16. The structure and synthesis of nucleid acids.
17. The genetic code and gene expression.
18. Biological membranes.
19. Hormones and the Endocrine System.
20. The extracellular matrix.
21. Clinical and medical genetics.
22. The human genome and the genomics.
23. Molecular biology of the cell.
24. The human chromosomes.
25. Population genetics; a bridge from evolutionary history to genetic medicine.
26. Modern human origins and prehistoric demography of Europe - genetic
diversity.
27. Law and human genetics.
28. Cytogenetics and molecular diagnostics.
29. Pharmaco- and Immunogenetics.
30. Gene therapy and biotechnology.
Objectives of the course:
Since the cell is the structural unit of living systems – biochemistry, molecular biology
and genetics - will be defined as the science concerned with the chemical basis of life.
The cell is the structural unit of living systems and therefore biochemistry encompasses
enormous areas of cell biology, of molecular biology and of molecular genetics.
This is the reason why numerous molecules found in cells have to be described at the
molecular level, of all the chemical processes associated with living cells. In brief –
nucleid acids are corner stones of genetics, but Physiology and Immunology describes
the body function and Pharmacy or Pharmacology where many drugs are metabolized
by enzymes are closely related with Toxicology and Pathology.
Since life is dependent on biochemical processes and molecular reactions, scientific
workers in other fields of biology have to be strongly educated in problems of the above
cited fields including problems of bioinformatics and modern laboratory techniques.
24
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation and computer simulations during hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
Textbooks and materials (recommended reading):
1. Turner P. C., McLennan A., Bates A., White M.: Instant notes: Molecular
Biology. Edition: 3, Taylor & Francis, 2005.
2. Winter P. C., G. Ivor Hickey, H. L. Fletcher: Instant Notes in Genetics Edition: 2,
BIOS Scientific Publishers, 2002.
3. D. Hames, N. M. Hooper: Instant Notes In Biochemistry, Edition: 3, Taylor
& Francis, 2005.
4. Brown T. A.: Genomes 3, Garland Science Pub., 2006.
5. Hartl D. L., Jones E. W.: Genetics: Analysis of Genes and Genomes, 7 th Edition,
Jones & Bartlett Publishers, 2008.
6. Koolman J., Röhm Klaus-Heinrich, Robertson M. :Color atlas of biochemistry,
translated by Michael Robertson, Edition: 2, Thieme, 2005.
7. Passarge E.: Color atlas of genetics, Edition: 3, Thieme, 2007.
8. Bradley J., Johnson D., Pober B. : Lecture notes. Medical genetics, Edition: 3,
Wiley-Blackwell, 2006.
9. Higgs P. G., Attwood T. K.: Bioinformatics and Molecular Evolution, Blackwell,
2005. (Chapters: 1,2,3,4,11,12,13).
25
Course title:
DATABASES AND DATA WAREHOUSES
Prerequisites:
Basic concepts of databases and relational database. Working knowledge
on operating different computer applications (interfaces: console and GUI).
Installing software, searching The Web.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
2
I
ETCS
6
Instructor:
PhD Beata Zielosko
Language of instruction:
English
26
Course Outline:
1. Database, database types. Distributed, analytical DB. Data modeling- entities
and relationships. Transformation from logical to physical data model.
2. Relational database and data normalization. Elements of relational algebra.
3. Database management system (Oracle, MySql, Microsoft SQL Server, Sybase,
PostgreSQL).
4. Elements of database programming: SQL and triggers in popular database
management systems (Oracle, MySql, Microsoft SQL Server, Sybase,
PostgreSQL).
5. Foundations of database administration and security. Creating accounts,
assignment of roles and privileges.
6. Data distributed. Transactions, concurrency control. Database optimization.
Indexing.
7. Object oriented databases, ODMG. Object-relational databases – specific
features.
8. Advanced SQL language. Other query languages (e.g. XQuery language).
9. Accessing database – ODBC, JDBC, ADO, OLEDB etc.
10. Data warehouse. Differences between database and data warehouse.
11. Architecture and components of data warehouse.
12. Multi-dimensional data modeling (star, snowflake, fact constellations schema,
data cube). Metadata.
13. OLAP cube, Relational-OLAP, Multidimensional-OLAP, Hybrid-OLAP. OLAP vs
OLTP.
14. ETL process.
15. Application – software, case studies.
Objectives of the course:
1. Data modeling. Skills in creating physical database, warehouse.
2. Skills in operation on data using: SQL, OLAP tools.
3. Working knowledge of different database management systems, (Oracle,
MySql, Microsoft SQL Server, Sybase, PostgreSQL).
4. Foundations of database system administration.
5. Retrieving data from databases (for example bioinformatics’, biomedical).
6. Different data type management and integration.
7. Working knowledge of using different database and data warehouse software
implementation for data management and transformation.
Teaching methods:
Variety methods including: lectures, multimedia presentations, class discussions, handson labs, group work and project creation.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
27
Textbooks and materials (recommended reading):
1. Date C.J.: An Introduction to Database Systems.
2. Date C.J., Darwen H.: A Guide to SQL Standard.
3. Ullman J.D.: Principles of Data Base Systems.
4. Beyon-Davies P.: Relational Databases Design.
5. Elmasri R., Navathe S., Fundamentals of Database Systems.
6. Blaha M. R., Premerlani W.: Object-Oriented Modeling and Design
for Database Applications.
7. Kimball R., Ross M.: The Data Warehouse Toolkit: The Complete Guide
to Dimensional Modeling.
8. Inmon W.H.: Building the Data Warehouse.
9. Jarke M., Lenzerini M.,Vassiliou Y., Vassiliadis P.: Fundamentals of data
warehouses.
10. Clement T. Yu., Weiyi Meng: Principles of database query processing
for advanced applications.
11. Connolly T.M., Begg C.E.: Database Systems: A Practical Approach To Design,
Implementation And Management.
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Course title:
MATHEMATICAL AND DIGITAL MODELLING
Prerequisites:
Working knowledge on operating system, operating different computer
application (interfaces: console and GUI). Installing software, searching
The Web. Basics from mathematics, statistics. Basic programming and
scripting skills.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
Labs
hours
15
30
semester year
2
I
ETCS
4
Instructor:
PhD Magdalena Tkacz
Language of instruction:
English
29
Course Outline:
1. Real-world situation, model and modeling concepts.
2. Modeling as a process. Fitting models to real parameters (data). Simulation.
3. Continuous and discrete models. Methods of models description. Sensibility
analysis.
4. Overview of modeling methods and techniques.
5. Efficiency of the method and method comparison.
6. Dynamic and static systems and their models. Dynamic system stability.
7. Probabilistic modeling.
8. Model optimization.
9. Tools for digital modeling.
10. Automation of modeling – methods and techniques.
Objectives of the course:
1. Skills in creating appropriate model.
2. Skills in conducing modeling and simulation.
3. Proficiency in using specialized (or authored) software for model creation
and for simulation.
4. Skills in model utilization – interpretation of data from simulation.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation and hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
Textbooks and materials (recommended reading):
1. Bender E. A. An introduction to mathematical modeling: Published by Courier
Dover Publications, 2000.
2. Dym C.: Principles of mathematical modeling, Academic Press, 2004.
3. Meerschaert M.:Mathematical modeling 3d Ed, Elsevier/Academic Press,
2007.
4. Bellouquid A., Delitala M.: Mathematical Modeling of Complex Biological
Systems: A Kinetic Theory Approach. Springer, 2006.
5. Howison S.: Practical applied mathematics: modelling, analysis,
approximation. Cambridge University Press, 2005.
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Course title:
METHODS OF DATA ANALYSIS
Prerequisites:
Working knowledge on operating system, operating different computer
application (interfaces: console and GUI). Installing software, searching
The Web. Basics from statistics, charting. Basic programming and scripting
skills.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
30
semester year
3
II
ETCS
6
Instructor:
PhD Magdalena Tkacz
Language of instruction:
English
31
Course Outline:
1. Difference between: data, information and knowledge.
2. Qualitative vs quantitative data analysis.
3. Problems with data quality.
4. Problems with multidimensional data and dimensionality reduction methods.
5. Principles of ETL process.
6. Data preprocessing methods.
7. Statistical approach – review.
8. Role of descriptive, inferential and basic statistics in data analysis.
9. Common errors in data analysis, and how to avoid them.
10. Artificial intelligence and machine learning methods in data analysis.
11. Exploratory data analysis.
12. Data mining and Knowledge Discovery from data principles.
13. Human perception – appropriate charting and reporting.
14. Visual Data Mining.
15. Software for data analysis support.
Objectives of the course:
1. Skills in data quality assessment and data preparation for further analysis.
2. Choosing appropriate method of data analysis for a certain task.
3. Proficiency in using specialized software for data analysis.
4. Working knowledge concerning different statistical parameters and
coefficients with the ability of their interpretation with strong reference and
context of the analyzed data.
5. Skills in appropriate chart type choosing and familiarity with methods of data
visualization and reporting.
Teaching methods:
This course is taught with variety of methods, including: lectures, class discussions,
group work, project creation and hands-on labs.
Assessment methods:
Exam, tests, quizzes, projects, hands-on lab.
32
Textbooks and materials (recommended reading):
1. Pyle. D.: Data Preparation for Data Mining, Morgan-Kaufmann 1999.
2. The Data Analysis BriefBook: Web edtn:
http://physics.web.cern.ch/Physics/DataAnalysis/Briefbook/
3. Statsoft Electronic Textbook:
http://www.statsoft.com/textbook/stathome.html
4. John Hilary Maindonald, John Braun: Data analysis and graphics using R:
an example-based approach. Cambridge University Press, 2003.
5. John Spicer: Making sense of multivariate data analysis. SAGE, 2004.
6. Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, Lukasz A. Kurgan
Data Mining: A Knowledge Discovery Approach, Springer, 2007.
7. Glen Cowan: Statistical data analysis, Oxford University Press, 1998.
33
Course title:
MULTIRESOLUTION IMAGE ANALYSIS
Prerequisites:
Programming in C++ or Java.
Level of the course:
Master ‘s programme “Informatics” - the second degree studies
Specialization: “Modelling and Visualisation in Bioinformatics”
Form:
type
lectures
labs
hours
15
45
semester year
3
II
ETCS
6
Instructor:
PhD Agnieszka Lisowska
Language of instruction:
English
34
Course Outline:
1. Multiresolution image representation: quadtrees, quadtree partition,
wavelets, geometrical wavelets, fractals.
2. Image processing: edge and corner detection, segmentation, denoising,
objects detection.
3. Image compression: lossless compression, lossy compression, sample image
compression algorithms, fractal compression.
4. Image recognition: object recognition, face recognition, letters recognition.
Objectives of the course:
Upon successful completion of the course, the students should be able to implement the
advanced methods of digital image processing, coding and recognition used, for
example, in medical imaging.
Teaching methods:
Fully audiovisual lecture. Exercises in programming different problems of advanced
multiresolution image processing, coding and recognition. The C++ or Java
environments to choose.
Assessment methods:
Projects and a final exam.
Textbooks and materials (recommended reading):
1. Lisowska A., Multiresolution Image Representations and Their Applications,
textbook, 2009.
2. Mohlenkamp M., Pereyra M.C., Wavelets, Their Friends and What They Can Do
for You, European Mathematical Society, 2008.
3. Barnsley M.F., Superfractals, Cambridge University Press, Cambridge, 2006.
4. Sayood K., Introduction to Data Compression, Morgan Kaufmann, San
Francisco, 2000.
5. Lu N., Fractal Imaging, Academic Press, San Diego, 1997.
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