Informace o předmětu

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
Mgr. Michal Štefánik (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 79 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
Mgr. Michal Štefánik (pomocník)
Bc. Marek Kadlčík (pomocník)
Mgr. Vlastimil Martinek (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 80 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každý semestr.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
Mgr. Michal Štefánik (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 16. 2. až Čt 11. 5. Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 79 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
Mgr. Michal Štefánik (pomocník)
Mgr. Martin Geletka (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 80 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každý semestr.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno prezenčně.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
RNDr. Vít Starý Novotný, Ph.D. (pomocník)
Mgr. Dávid Lupták (pomocník)
Mgr. Michal Štefánik (pomocník)
Mgr. Mikuláš Bankovič (pomocník)
Mgr. Vlastimil Martinek (pomocník)
Mgr. Marek Petrovič (pomocník)
Mgr. Jakub Ryšavý (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 16. 9. až Čt 9. 12. Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 79 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.
Vyučováno online.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)
RNDr. Vít Starý Novotný, Ph.D. (pomocník)
Mgr. Dávid Lupták (pomocník)
Mgr. Michal Štefánik (pomocník)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 79 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Sojka, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 10:00–11:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 79 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 8:00–9:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 47 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 13:00–14:50 A502

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 47 mateřských oborů, zobrazit

Cíle předmětu

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.

Výstupy z učení

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PV173 Seminář zpracování přirozeného jazyka
• PV174 Laboratory of Electronic and Multimedia Applications

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
Course is especially useful to be enrolled by students preparing their research thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Út 10:00–11:50 A502, kromě Út 6. 12. ; a Út 6. 12. 10:00–11:50 C522

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 47 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PA178 Digital Typography and Visualization
• PV173 Seminář zpracování přirozeného jazyka

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm and discuss in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Čt 14:00–15:50 C522

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 47 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PA178 Digital Typography and Visualization
• PV173 Seminář zpracování přirozeného jazyka

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm and discuss in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Pá 10:00–11:50 C522

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 46 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PA178 Digital Typography and Visualization
• PV173 Seminář zpracování přirozeného jazyka

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm and discuss in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

doc. RNDr. Petr Matula, Ph.D.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh seminárních/paralelních skupin

PV212/01: Čt 14:00–15:50 C522, P. Sojka

Předpoklady

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.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 46 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

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.

Vyučovací jazyk

Angličtina

Navazující předměty

• DUVOD Úvod do PhD studia
• PA178 Digital Typography and Visualization
• PV173 Seminář zpracování přirozeného jazyka

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm and discuss in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered. "Education is not about the filling of a bucket but the lighting of a fire!" William Butler Yeats

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

prof. Ing. Jiří Sochor, CSc.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.
Dodavatelské pracoviště: Katedra vizuální informatiky – Fakulta informatiky

Rozvrh

Út 8:00–9:50 C522

Předpoklady

SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 46 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

Every student will either refer about some research topic or solve some project and present its solution.

Vyučovací jazyk

Angličtina

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered.

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

prof. Ing. Jiří Sochor, CSc.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.

Předpoklady

SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 46 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

Every student will either refer about some research topic or solve some project and present its solution.

Vyučovací jazyk

Angličtina

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered.

Další komentáře

Předmět je vyučován každoročně.
Výuka probíhá každý týden.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

prof. Ing. Jiří Sochor, CSc.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.

Rozvrh

Út 15:00–16:50 C522

Předpoklady

SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 44 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

Every student will either refer about some research topic or solve some project and present its solution.

Vyučovací jazyk

Angličtina

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered.

Další komentáře

Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

prof. Ing. Jiří Sochor, CSc.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.

Rozvrh

Út 12:00–13:50 C522

Předpoklady

SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 44 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• 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.

Literatura

• WITTEN, I. H. a 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

Výukové metody

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.

Metody hodnocení

Every student will either refer about some research topic or solve some project and present its solution.

Vyučovací jazyk

Angličtina

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered.

Další komentáře

Studijní materiály
Předmět je vyučován každoročně.

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

Rozsah

0/2/0. 2 kr. (plus ukončení). Ukončení: k.

Vyučující

doc. RNDr. Petr Sojka, Ph.D. (přednášející)

Garance

prof. Ing. Jiří Sochor, CSc.
Katedra vizuální informatiky – Fakulta informatiky
Kontaktní osoba: doc. RNDr. Petr Sojka, Ph.D.

Rozvrh

Út 11:00–12:50 C418

Předpoklady

SOUHLAS
Deep interest in areas of Digital Typography, Scientific Visualization, Information Retrieval and Machine Learning.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

předmět má 37 mateřských oborů, zobrazit

Cíle předmětu

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.

Osnova

• Topics and projects for every year will be posted on the web page of the course.

Literatura

• WITTEN, I. H. a 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

Metody hodnocení

Every student will either refer about some research topic or solve some project and present its solution.

Vyučovací jazyk

Angličtina

Informace učitele

http://www.fi.muni.cz/~sojka/PV212/
In the case all students enrolled will understand Czech, we may brainstorm in Czech. Course is especially useful to be enrolled by students preparing their thesis in the areas covered.

Další komentáře

Předmět je vyučován každoročně.

• Statistika zápisu (nejnovější)