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

Faculty of Informatics
Autumn 2024
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching
Teacher(s)
doc. RNDr. Petr Sojka, Ph.D. (lecturer)
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 26. 9. to Thu 19. 12. 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
there are 32 fields of study the course is directly associated with, display
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 each semester.
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, Spring 2024, Spring 2025.

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

Faculty of Informatics
Spring 2025
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching
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
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
there are 32 fields of study the course is directly associated with, display
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)
The course is taught annually.
The course is taught: every week.
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, Spring 2024, Autumn 2024.

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
there are 79 fields of study the course is directly associated with, display
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.

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

Faculty of Informatics
Autumn 2023
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)
Mgr. Marek Kadlčík (assistant)
Mgr. Vlastimil Martinek (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
there are 80 fields of study the course is directly associated with, display
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 each semester.
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, Spring 2024, Autumn 2024, Spring 2025.

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

Faculty of Informatics
Spring 2023
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 16. 2. to Thu 11. 5. 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
there are 79 fields of study the course is directly associated with, display
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, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

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

Faculty of Informatics
Autumn 2022
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)
Mgr. Martin Geletka (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
there are 80 fields of study the course is directly associated with, display
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 each semester.
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, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2021
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)
RNDr. Vít Starý Novotný, Ph.D. (assistant)
Mgr. Dávid Lupták (assistant)
Mgr. Michal Štefánik (assistant)
Mgr. Mikuláš Bankovič (assistant)
Mgr. Vlastimil Martinek (assistant)
Mgr. Marek Petrovič (assistant)
Mgr. Jakub Ryšavý (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 16. 9. to Thu 9. 12. 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
there are 79 fields of study the course is directly associated with, display
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 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2020
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)
RNDr. Vít Starý Novotný, Ph.D. (assistant)
Mgr. Dávid Lupták (assistant)
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
there are 79 fields of study the course is directly associated with, display
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 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2019
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)
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
there are 79 fields of study the course is directly associated with, display
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)
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 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2018
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)
Guaranteed by
doc. RNDr. Petr Matula, 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 8:00–9:50 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
there are 47 fields of study the course is directly associated with, display
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)
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 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2017
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)
Guaranteed by
doc. RNDr. Petr Matula, 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 13:00–14:50 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
there are 47 fields of study the course is directly associated with, display
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)
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 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2016
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)
Guaranteed by
doc. RNDr. Petr Matula, 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
Tue 10:00–11:50 A502, except Tue 6. 12. ; and Tue 6. 12. 10:00–11:50 C522
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
there are 47 fields of study the course is directly associated with, display
Course objectives
The aim of the seminar is a presentation of results of student research (both pregradual and gradual). At the end of the course students will have experience in presenting and discussion of their or other (from readings) research. Every student has to have her/his own presentation in the seminar.
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 either in Czech or, according to the preferences of the speaker, in English. The students can control the content of the seminar 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 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 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2015
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)
Guaranteed by
doc. RNDr. Petr Matula, 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 14:00–15:50 C522
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
there are 47 fields of study the course is directly associated with, display
Course objectives
The aim of the seminar is a presentation of results of student research (both pregradual and gradual). At the end of the course students will have experience in presenting and discussion of their or other (from readings) research. Every student has to have her/his own presentation in the seminar.
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 either in Czech or, according to the preferences of the speaker, in English. The students can control the content of the seminar 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 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 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2014
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)
Guaranteed by
doc. RNDr. Petr Matula, 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
Fri 10:00–11:50 C522
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
there are 46 fields of study the course is directly associated with, display
Course objectives
The aim of the seminar is a presentation of results of student research (both pregradual and gradual). At the end of the course students will have experience in presenting and discussion of their or other (from readings) research. Every student has to have her/his own presentation in the seminar.
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 either in Czech or, according to the preferences of the speaker, in English. The students can control the content of the seminar 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 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)
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 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2013
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)
Guaranteed by
doc. RNDr. Petr Matula, 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 of Seminar Groups
PV212/01: Thu 14:00–15:50 C522, P. Sojka
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
there are 46 fields of study the course is directly associated with, display
Course objectives
The aim of the seminar is a presentation of results of student research (both pregradual and gradual). At the end of the course students will have experience in presenting and discussion of their or other (from readings) research. Every student has to have her/his own presentation in the seminar.
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 either in Czech or, according to the preferences of the speaker, in English. The students can control the content of the seminar 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 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 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2012
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)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
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
Tue 8:00–9:50 C522
Prerequisites
SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.
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 46 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to read, understand, explain and evaluate [English] scientific papers, based on experience of practising these skills in this seminar.
Syllabus
  • Topics and projects for every year will be posted on the web page of the course. On seminars students will refer about topics studied and they will be discussed thoroughly.
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
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. 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
Teaching methods
Lectures intermixed with seminar style discussions and brainstormings to solve given topics-projects. Students will be given readings as a preparation for the contact teaching hours.
Assessment methods
Every student will either refer about some research topic or solve small research project and present its solution.
Language of instruction
English
Further comments (probably available only in Czech)
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 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2021, Autumn 2022, Spring 2023, Autumn 2023, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2011
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)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Prerequisites
SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.
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 46 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to read, understand, explain and evaluate [English] scientific papers, based on experience of practising these skills in this seminar.
Syllabus
  • Topics and projects for every year will be posted on the web page of the course. On seminars students will refer about topics studied and they will be discussed thoroughly.
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
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. 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
Teaching methods
Lectures intermixed with seminar style discussions and brainstormings to solve given topics-projects. Students will be given readings as a preparation for the contact teaching hours.
Assessment methods
Every student will either refer about some research topic or solve small research project and present its solution.
Language of instruction
English
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
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 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, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2010
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)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Timetable
Tue 15:00–16:50 C522
Prerequisites
SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.
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 44 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to read, understand, explain and evaluate [English] scientific papers, based on experience of practising these skills in this seminar.
Syllabus
  • Topics and projects for every year will be posted on the web page of the course. On seminars students will refer about topics studied and they will be discussed thoroughly.
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
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. 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
Teaching methods
Lectures intermixed with seminar style discussions and brainstormings to solve given topics-projects. Students will be given readings as a preparation for the contact teaching hours.
Assessment methods
Every student will either refer about some research topic or solve small research project and present its solution.
Language of instruction
English
Further comments (probably available only in Czech)
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 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, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2009
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)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Timetable
Tue 12:00–13:50 C522
Prerequisites
SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.
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 44 fields of study the course is directly associated with, display
Course objectives
At the end of the course students should be able to read, understand, explain and evaluate [English] scientific papers, based on experience of practising these skills in this seminar.
Syllabus
  • Topics and projects for every year will be posted on the web page of the course. On seminars students will refer about topics studied and they will be discussed thoroughly.
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
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. 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
Teaching methods
Lectures intermixed with seminar style discussions and brainstormings to solve given topics-projects. Students will be given readings as a preparation for the contact teaching hours.
Assessment methods
Every student will either refer about some research topic or solve small research project and present its solution.
Language of instruction
English
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 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, Spring 2024, Autumn 2024, Spring 2025.

PV212 Readings in Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning

Faculty of Informatics
Autumn 2008
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)
Guaranteed by
prof. Ing. Jiří Sochor, CSc.
Department of Visual Computing – Faculty of Informatics
Contact Person: doc. RNDr. Petr Sojka, Ph.D.
Timetable
Tue 11:00–12:50 C418
Prerequisites
SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.
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 37 fields of study the course is directly associated with, display
Course objectives
Students will be given readings and/or projects to read and/or solve. On seminars students will refer about topics studied and they will be discussed thoroughly.
Syllabus
  • Topics and projects for every year will be posted on the web page of the course.
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
  • KNUTH, Donald Ervin. Digital typography. Stanford: Center for the Study of Language and Information, 1999, xv, 685. ISBN 1575860112. 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
Assessment methods
Every student will either refer about some research topic or solve some project and present its solution.
Language of instruction
English
Further comments (probably available only in Czech)
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 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, Spring 2024, Autumn 2024, Spring 2025.