PV177 Laboratory of Advanced Network Technologies

Faculty of Informatics
Autumn 2021
Extent and Intensity
0/2/0. 2 credit(s). Type of Completion: z (credit).
Teacher(s)
doc. RNDr. Eva Hladká, Ph.D. (lecturer)
RNDr. Tomáš Rebok, Ph.D. (lecturer)
Ing. Jana Hozzová, Ph.D. (seminar tutor)
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
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
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
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.
3. PV177/ComputerNetworks (Advanced Computer Networks) -- during the sessions of this specialization students will be introduced to software-defined networks (SDN) in the context of the newest IT trends (software-defined infrastructure, cloud, content delivery networks). In addition, the sessions will include discussions about the most common problems traditional computer networks face today and about solutions to those problems using software-defined technologies (streaming telemetry, traffic engineering and automation).
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):
    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.

  • 3. PV177/ComputerNetworks (Computer Networks):
    1) Introduction to SDN: discussion about the most common problems traditional computer networks face today and elaboration on the evolution of SDN
    2) SDN usage: in data centers, CDN, WAN, wireless,…​
    3) SDN architecture
    4) Semestral project: create a system study for the implementation of SDN in a given case
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
The course is taught each semester.
The course is taught: every week.
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, Spring 2020, Autumn 2020, Spring 2021, Spring 2022, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.
  • Enrolment Statistics (Autumn 2021, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2021/PV177