PV202 Laboratoř servisních systémů
Fakulta informatikypodzim 2016
- Rozsah
- 0/0/2. 2 kr. Doporučované ukončení: k. Jiná možná ukončení: z.
- Vyučující
- Ing. Leonard Walletzký, Ph.D. (přednášející)
Mgr. Jitka Kitner (cvičící) - Garance
- doc. RNDr. Eva Hladká, Ph.D.
Katedra počítačových systémů a komunikací – Fakulta informatiky
Dodavatelské pracoviště: Katedra počítačových systémů a komunikací – Fakulta informatiky - Rozvrh
- St 12. 10. 12:00–15:50 B517, 18:00–19:50 A218, Čt 13. 10. 12:00–15:50 B517, 18:00–19:50 A217, Pá 14. 10. 10:00–13:50 A217, 14:00–15:50 A217
- Předpoklady
- PB114 Datové modelovaní I &&SOUHLAS
Preconditions for this course: (1) English; (2) In the seminar, the students are expected to develop their own recommender system project. It will involve some web development, algorithm implementation and system design. - Omezení zápisu do předmětu
- Předmět je nabízen i studentům mimo mateřské obory.
Předmět si smí zapsat nejvýše 20 stud.
Momentální stav registrace a zápisu: zapsáno: 0/20, pouze zareg.: 0/20, pouze zareg. s předností (mateřské obory): 0/20 - Mateřské obory/plány
- předmět má 37 mateřských oborů, zobrazit
- Cíle předmětu
- Course objective: The course of Recommender System for Service Science is to present the knowledge of recommender systems in the context of Service Science. The students will learn algorithms, mathematical underpinnings and up-to-date research results in recommender systems and information retrieval. The course will also provide several real-world recommender system applications such as AMAZON recommendation and booking.com recommendation to students. The students will learn and discuss the applications according to the case studies in the context of Service Science. In the seminar, the students are expected to design and try to develop a system prototype and present their work in recommender systems.
- Osnova
- The lecturer Mouzhi Ge will explain further topics from Recommender System for Service Science, e.g. Construct a recommender for Cloud IT service:
- Introduction to Recommender Systems
- Collaborative filtering, Content-based and Knowledge-based recommendations
- Explanation in recommender systems
- Recommender System and Service Science
- Group Recommendations
- Evaluating recommender systems
- Case study – personalized recommendations on the Internet
- Recommender systems and the next-generation Web
- Recommendations in ubiquitous environments
- Context-aware recommender system
- Recommender system and HCI
- Výukové metody
- lectures, making a quick RecSys prototype
- Metody hodnocení
- In the seminar, the students are expected to develop their own recommender system project. It will involve some web development, algorithm implementation and system design. The students can choose the domain they like, the domain should be related to Service Science. As the block course is short, the students should at least design the system, if some students have ever done the web development before (e.g. they don’t need time to learn how to set up a server, install an IDE).
- Informace učitele
- https://www.unibz.it/en/public/university/default.html
Lecturer Mouzhi Ge from University of Bozen (Bolzano, Italy): Mouzhi.Ge@unibz.it - Další komentáře
- Studijní materiály
Předmět je vyučován každý semestr.
- Statistika zápisu (podzim 2016, nejnovější)
- Permalink: https://is.muni.cz/predmet/fi/podzim2016/PV202