PV177 Laboratory of Advanced Network Technologies

Faculty of Informatics
Spring 2020
Extent and Intensity
0/2/0. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. RNDr. Eva Hladká, Ph.D. (lecturer)
Ing. Jana Hozzová, Ph.D. (lecturer)
Mgr. Aleš Křenek, Ph.D. (lecturer)
Mgr. Martin Macák (lecturer)
prof. RNDr. Václav Matyáš, M.Sc., Ph.D. (lecturer)
RNDr. Tomáš Rebok, Ph.D. (lecturer)
RNDr. Vít Rusňák, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Eva Hladká, Ph.D.
Department of Computer Systems and Communications - Faculty of Informatics
Contact Person: doc. RNDr. Eva Hladká, Ph.D.
Supplier department: Department of Computer Systems and Communications - Faculty of Informatics
Timetable of Seminar Groups
PV177/DataScience: Mon 17. 2. to Fri 15. 5. Thu 10:00–11:50 A505, J. Hozzová, M. Macák, T. Rebok
PV177/Experiments: No timetable has been entered into IS. J. Hozzová, A. Křenek, V. Rusňák
PV177/ComputerNetworks: No timetable has been entered into IS. E. Hladká
Prerequisites
SOUHLAS
PV177/DataScience (Big Data Analytics in Practice) -- none
PV177/Experiments (Modelling and Evaluation of Experiments) -- basic mathematics (MB101-MB103, resp. MB202-203) and programming (IB111, IB113)
PV177/ComputerNetworks (Computer Networks) -- completed PB156, preferably also PA159
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 76 fields of study the course is directly associated with, display
Course objectives
Familiarization with the area and practical (team) project aimed at adopting the principles in one of the areas, which the course is specialized on in the particular semester.
In current semester, the course is specialized in the following areas:
1. PV177/DataScience (Big Data analytics in practice) -- the aim of this course's specialization is to introduce students to methods and tools for analyzing large data volumes (so-called Big Data), which will then be examined in the form of practical projects presented at the end of the semester.
2. PV177/Experiments (Modelling and Evalution of Experiments) -- the aim of this course's specialization is to introduce students to methods and tools for modelling and quantitative and qualitative evaluation of experiments, which will then be examined in the form of three practical projects throughout the semester.
3. PV177/ComputerNetworks (Advanced Computer Networks) -- the aim of this specialization is to introduce students to the area of computer networks and related technologies, research methodology, own research and presentation of results. The work is separated into two semesters, begin in the fall semester with lower network levels (physical infrastructure, construction of computer halls, basic protocols from level two = STP, 802.1Q,...), in spring semester work follows with protocols on L3, mostly routing protocols (OSPF, BGP). Sprig semester follows the fall.
Learning outcomes
Getting new knowledge in the chosen area of interest and working on a practically-oriented (team) project.
Syllabus
  • 1. PV177/DataScience (Data Analytics in Practice):
    Team project in one of the areas, which the course is specialized on in the particular semester: Big Data analytics, computer networks, grids or multimedia. Students can choose or are assigned a practical project (team-based, i.e. an assignment will be solved by a group of students). When solving the project, students will master the advanced understanding of a subject, acquire basic research methodology, will optionally perform the research and will present achieved results. The work progress will be evaluated on regular weekly or two-weekly seminars, where students will receive the necessary feedback on their undertakings.
    The last seminar will be devoted to the overall evaluation and students will receive credits.
  • 2. PV177/Experiments (Modelling and Evaluation of Experiments):
    Seminar will be done in four blocks, in 2., 5., 8. and 11. week of the semester, each time for 6 hours, dates will be discussed with students and determined after the beginning of the semester.
    1) Quantitative evaluation: what is hypothesis and how to formulate it, what can happen (false negative, true negative, false positive, true positive), how to control the false positive error rate, how to ensure high statistical power, what means statistically significant result, p-value and its interpretation, what is pre-registration and how to use it.
    2) Modelling: introduction to mathematical and computational models, parametrization of models towrads experimental data, least squares method; goodnes of fit evaluation, application of quantitative evaluation (presented in the first block), under- and overfitting; advanced techniques (mixed models etc.).
    3) Qualitative evaluation and user testing: types of qualitative experiments (questionnaires, interviews and focus groups, …), formative and summative testing, standardized questionnaires, goals and phases of the analysis of qualitative data.
    Each part includes a project. Students are encouraged to bring their own (evaluation in their thesis or its part). If they do not have their own data, a project will be assigned.
  • 3. PV177/ComputerNetworks (Computer Networks):
    Team project specialized in an area of computer networks, grids or multimedia. Students can choose or are assigned a practical project (team-based, i.e. an assignment will be solved by a group of students). When solving the project, students will master the advanced understanding of a subject, acquire basic research methodology, will optionally perform the research and will present achieved results. The work progress will be evaluated on regular weekly or two-weekly seminars, where students will receive the necessary feedback on their undertakings.
    The last seminar will be devoted to the overall evaluation and students will receive credits.
Literature
  • Research Methods in Human-Computer Interaction; Harry Hochheiser, Jinjuan Heidi Feng, Jonathan Lazar; 2nd Ed. ISBN: 9780128093436, 2017.
  • https://www.coursera.org/learn/statistical-inferences/
  • STEVENS, W. Richard, Bill FENNER and Andrew M. RUDOFF. UNIX network programming. 3rd ed. Boston, Mass.: Addison-Wesley, 2004. xxiii, 991. ISBN 0-13-141155-1. info
  • KUROSE, James F. Computer networking :a top-down approach featuring the Internet. Boston: Addison-Wesley, 2003. xvii, 752. ISBN 0-321-17644-8. info
  • GOUDA, Mohamed G. Elements of network protocol design. New York: John Wiley & Sons, 1998. xviii, 506. ISBN 0471197440. info
Teaching methods
There are several projects and each student works on one of them often in cooperation with others. Students explore the given theme during the semester. During seminars, students will refer about their results in the project. Some course specializations may be supplemented with introductory lectures on the particular topic.
Assessment methods
Students are evaluated according to their activity on seminars, and the quality of achieved results and their presentations in front of their peers.
Language of instruction
English
Further Comments
Study Materials
The course is taught each semester.
The course is also listed under the following terms Autumn 2005, Spring 2006, Autumn 2006, Spring 2007, Autumn 2007, Spring 2008, Autumn 2008, Spring 2009, Autumn 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, Autumn 2018, Spring 2019, Autumn 2019, Autumn 2020, Spring 2021.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2020/PV177