A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
MI-SZ1 Knowledge Engineering Seminar Master I Extent of teaching: 2C Instructor: Completion: Z Department: 18105 Credits: 4 Semester: L,Z Annotation:
On this seminar you will present a research paper from a top institute / research group to your peers. You will learn what is being cooked in top research labs around the world. Additionally, you will learn how to properly present and read scientific papers. The work in the seminar will prepare you to attend (and profit from) top machine learning and AI conferences and summer schools, as well as FIT's own Summer Research Program (VyLet).
Lecture syllabus:
Seminar syllabus:
Bez osnovy - seminář.Literature:
Temporary course website: http://www.pablomaldonado.org/cvut-szi/ You can find a list of the proposed articles (suggestions welcome).Requirements:
- Strong interest in machine learning and AI.
Předmět je nahrazen ekvivalentním NI-SZ1 // Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-SZ1/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester MI-ZI.2016 Knowledge Engineering V Není MI-ZI.2018 Knowledge Engineering V Není MI-SP-TI.2016 System Programming V Není MI-SP-SP.2016 System Programming V Není MI-SPOL.2016 Unspecified Branch/Specialisation of Study V Není MI-WSI-WI.2016 Web and Software Engineering V Není MI-WSI-SI.2016 Web and Software Engineering V Není MI-WSI-ISM.2016 Web and Software Engineering V Není MI-NPVS.2016 Design and Programming of Embedded Systems V Není MI-PSS.2016 Computer Systems and Networks V Není MI-PB.2016 Computer Security V Není NI-TI.2018 Computer Science V Není
Page updated 18. 4. 2024, semester: L/2020-1, L/2023-4, L/2019-20, Z/2021-2, L/2022-3, Z/2023-4, Z/2019-20, Z/2022-3, L/2021-2, Z/2024-5, Z/2020-1, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška