PV177 Laboratory of Advanced Network Technologies

Faculty of Informatics
Spring 2022
Extent and Intensity
0/2/0. 2 credit(s). Type of Completion: z (credit).
Taught in person.
Teacher(s)
doc. RNDr. Eva Hladká, Ph.D. (lecturer)
Bc. Hynek Cihlář (lecturer)
Ing. Jana Hozzová, Ph.D. (lecturer)
RNDr. Tomáš Rebok, 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/AppliedAI: Mon 14. 2. to Mon 9. 5. Mon 14:00–15:50 A217, T. Rebok
Prerequisites
SOUHLAS
none
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 area:
1. PV177/DataScience (Practical application of Artificial Intelligence techniques) -- within this course specialization, students will be introduced to the practical application of (big) data analysis techniques and methods of advanced machine learning and artificial intelligence. Students will not only gain basic theoretical knowledge in the field of artificial intelligence, but above all they will learn to apply these to practical data analysis problems using appropriate tools.
Learning outcomes
Getting new knowledge in the chosen area of interest and working on a practically-oriented (team) project.
Syllabus
  • 1. PV177/AppliedAI(Practical application of Artificial Intelligence techniques):
    Classes of this seminar group will be held every week, the course language is Czech (with English slides). The course has been prepared in cooperation with the Autocont a.s. company.
    During the course, students will be introduced to the freely available, general and open-source KNIME Analytics Platform, which they will use during the semester for various practical tasks in the field of data analytics and application of artificial intelligence techniques. They will learn to work with different data sources (SQL and NoSQL databases) to which they will apply various analytical techniques and algorithms in practical exercises. At the end of the course, they will participate in a (group) project where they will practice their knowledge.
    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.knime.com/knime-analytics-platform
  • 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, Spring 2020, Autumn 2020, Spring 2021, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024.
  • Enrolment Statistics (Spring 2022, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2022/PV177