FI:PV177 Laboratory of Networks - Course Information

PV177 Laboratory of Advanced Network Technologies

Extent and Intensity

0/2/0. 2 credit(s). Type of Completion: z (credit).

Teacher(s)

doc. RNDr. Eva Hladká, Ph.D. (lecturer)
RNDr. Martin Macák, Ph.D. (lecturer)
prof. RNDr. Václav Matyáš, M.Sc., Ph.D. (lecturer)
RNDr. Tomáš Rebok, Ph.D. (lecturer)
Ing. Jana Hozzová, Ph.D. (seminar tutor)
Mgr. Aleš Křenek, Ph.D. (seminar tutor)
RNDr. Vít Rusňák, Ph.D. (seminar tutor)
Ing. Martin Tuleja (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

Timetable of Seminar Groups

PV177/ComputerNetworks: Thu 10:00–11:50 A505, T. Rebok, M. Tuleja
PV177/DataScience: No timetable has been entered into IS. T. Rebok
PV177/Experiments: Mon 12:00–13:50 A505, J. Hozzová, A. Křenek, V. Rusňák

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

there are 81 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.
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

Study Materials
The course is taught each semester.

• Enrolment Statistics (Autumn 2020, recent)
  
• Permalink: https://is.muni.cz/course/fi/autumn2020/PV177