PV212 Seminar on Machine Learning, Information Retrieval, and Scientific Visualization

Faculty of Informatics
Spring 2024
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Teacher(s)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
Mgr. Michal Štefánik (assistant)
Guaranteed by
doc. RNDr. Petr Sojka, Ph.D.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Supplier department: Department of Visual Computing – Faculty of Informatics
Timetable
Thu 10:00–11:50 A502
Prerequisites
SOUHLAS
Interest in research problems in areas of Machine Learning, Scientific Visualization, Information Retrieval and Digital Typography. Courage to learn how to move the human knowledge and understanding in these areas by CS research. Willingness to study particular topic of choice, and refer, discuss and brainstorm about it with others.
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
The aim of the seminar is to give floor to students (both pregradual and gradual) to read, practice and present scientific results (eitheir their or those ackquires from scientific papaers. Every student will have her/his own presentation in the seminar.
Learning outcomes
At the end of the course students will have experience in presenting and discussion of their or other (from readings) research. They also will be able to prepare scientific presentation of their work (slides, thesis), and communicate scientific results.
Syllabus
  • Referred topics/projects for every year will be posted on the web page of the course, and negotiated with registered students. The lectures consist mostly of students' presentations. The presentations and discussion are in English. The students will have an ample space in the discussions after each presentation.
Literature
  • WITTEN, I. H. and Eibe FRANK. Data mining : practical machine learning tools and techniques. 2nd ed. Amsterdam: Elsevier, 2005, xxxi, 525. ISBN 0120884070. info
  • MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
  • Information retrieval :data structures & algorithms. Edited by William B. Frakes - Ricardo Baeza-Yates. Upper Saddle River: Prentice Hall, 1992, viii, 504. ISBN 0-13-463837-9. info
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. info
Teaching methods
Lectures intermixed with seminar style discussions and brainstormings to solve given research problems. Students will be given readings as a preparation for the contact teaching hours, if they will not come with their own research problems.
Assessment methods
Every student will either refer about some research topic from readings or solve some project (typical from their thesis) and present its solution. Students must attend the seminar regularly and take active part in the seminar discussions.
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/~sojka/PV212/
The course is also listed under the following terms Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Autumn 2024, Spring 2025.
  • Enrolment Statistics (Spring 2024, recent)
  • Permalink: https://is.muni.cz/course/fi/spring2024/PV212