Drukuj sylabusApproximation Approaches - from Fourier Analysis to Deep Learning
Informacje ogólne
| Kod przedmiotu: | 231791-D |
| Kod Erasmus / ISCED: |
04.0
|
| Nazwa przedmiotu: | Approximation Approaches - from Fourier Analysis to Deep Learning |
| Jednostka: | Szkoła Główna Handlowa w Warszawie |
| Grupy: |
Elective courses for AAB - masters Przedmioty kierunkowe do wyboru SMMD-ADA |
| Punkty ECTS i inne: |
4.50 (zmienne w czasie)
|
| Język prowadzenia: | angielski |
| Efekty uczenia się: |
Wiedza: Key concepts and results of approximation theory. Basic intutions and definitions and techniques of deep learning Main processing data tools in approximation problems Umiejętności: The skill of application concepts of approximation theory in constructing analytical models. The skill of use of approximation approaches in real problems. The skill of selecting appropriate computer-supported applications in analyses of aproximation problems. The skill of appropriate evaluation of errors and of interpreting of model results. Kompetencje społeczne: Reliablity in data analysis. Ability to operate with models involving complex social interactions. |
Zajęcia w cyklu "Preferencje - Semestr letni 2024/25" (jeszcze nie rozpoczęty)
| Okres: | 2025-02-15 - 2025-09-30 |
 
PN WT ŚR CZ PT |
| Typ zajęć: |
Zajęcia prowadzącego
|
|
| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | Bartosz Pankratz | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Ocena
Zajęcia prowadzącego - Ocena |
Zajęcia w cyklu "Semestr letni 2024/25" (jeszcze nie rozpoczęty)
| Okres: | 2025-02-15 - 2025-09-30 |
PN WT ŚR CZ PT |
| Typ zajęć: |
Ćwiczenia, 16 godzin
Wykład, 14 godzin
|
|
| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | (brak danych) | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
| Skrócony opis: |
The first part of the course combines traditional lecturing and exercises with students' presentations. Next, concepts and results of approximation theory which underly applications in the area of economics and management are presented. The second part of the course is devoted to methods of analyses in real problems. This part of the course focuses on methods used in analyses of big data sets which are supported by dedicated computer applications. |
|
| Pełny opis: |
The course aims at providing students with knowledge on mathematical foundations of approximation methods used in contemporary data analysis, including ill-structured data. Other goals are shaping skills of application of these methods, the ability to expand acquired knowledge with advanced texts and techniques and enforce teamwork practice. |
|
| Literatura: |
Literatura podstawowa: Analysis: Part One: Elements (Pt. 1) 1976th Edition, Springer; Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 Literatura uzupełniająca: Scientific articles provided by lecturer. |
|
| Uwagi: |
Kryteria oceniania: referaty/eseje: 50.00% ocena z ćwiczeń: 50.00% |
|
Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)
| Okres: | 2024-10-01 - 2025-02-14 |
PN WT ŚR CZ PT |
| Typ zajęć: |
Ćwiczenia, 16 godzin
Wykład, 14 godzin
|
|
| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | (brak danych) | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
| Skrócony opis: |
The first part of the course combines traditional lecturing and exercises with students' presentations. Next, concepts and results of approximation theory which underly applications in the area of economics and management are presented. The second part of the course is devoted to methods of analyses in real problems. This part of the course focuses on methods used in analyses of big data sets which are supported by dedicated computer applications. |
|
| Pełny opis: |
The course aims at providing students with knowledge on mathematical foundations of approximation methods used in contemporary data analysis, including ill-structured data. Other goals are shaping skills of application of these methods, the ability to expand acquired knowledge with advanced texts and techniques and enforce teamwork practice. |
|
| Literatura: |
Literatura podstawowa: Analysis: Part One: Elements (Pt. 1) 1976th Edition, Springer; Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 Literatura uzupełniająca: Scientific articles provided by lecturer. |
|
| Uwagi: |
Kryteria oceniania: referaty/eseje: 50.00% ocena z ćwiczeń: 50.00% |
|
Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)
| Okres: | 2024-02-24 - 2024-09-30 |
PN WT ŚR CZ PT |
| Typ zajęć: |
Ćwiczenia, 16 godzin
Wykład, 14 godzin
|
|
| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | (brak danych) | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
| Skrócony opis: |
The first part of the course combines traditional lecturing and exercises with students' presentations. Next, concepts and results of approximation theory which underly applications in the area of economics and management are presented. The second part of the course is devoted to methods of analyses in real problems. This part of the course focuses on methods used in analyses of big data sets which are supported by dedicated computer applications. |
|
| Pełny opis: |
The course aims at providing students with knowledge on mathematical foundations of approximation methods used in contemporary data analysis, including ill-structured data. Other goals are shaping skills of application of these methods, the ability to expand acquired knowledge with advanced texts and techniques and enforce teamwork practice. |
|
| Literatura: |
Literatura podstawowa: Analysis: Part One: Elements (Pt. 1) 1976th Edition, Springer; Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 Literatura uzupełniająca: Scientific articles provided by lecturer. |
|
| Uwagi: |
Kryteria oceniania: referaty/eseje: 50.00% ocena z ćwiczeń: 50.00% |
|
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
| Okres: | 2023-10-01 - 2024-02-23 |
PN WT ŚR CZ PT |
| Typ zajęć: |
Ćwiczenia, 16 godzin
Wykład, 14 godzin
|
|
| Koordynatorzy: | (brak danych) | |
| Prowadzący grup: | (brak danych) | |
| Lista studentów: | (nie masz dostępu) | |
| Zaliczenie: |
Przedmiot -
Ocena
Wykład - Ocena |
|
| Skrócony opis: |
The first part of the course combines traditional lecturing and exercises with students' presentations. Next, concepts and results of approximation theory which underly applications in the area of economics and management are presented. The second part of the course is devoted to methods of analyses in real problems. This part of the course focuses on methods used in analyses of big data sets which are supported by dedicated computer applications. |
|
| Pełny opis: |
The course aims at providing students with knowledge on mathematical foundations of approximation methods used in contemporary data analysis, including ill-structured data. Other goals are shaping skills of application of these methods, the ability to expand acquired knowledge with advanced texts and techniques and enforce teamwork practice. |
|
| Literatura: |
Literatura podstawowa: Analysis: Part One: Elements (Pt. 1) 1976th Edition, Springer; Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 Literatura uzupełniająca: Scientific articles provided by lecturer. |
|
| Uwagi: |
Kryteria oceniania: referaty/eseje: 50.00% ocena z ćwiczeń: 50.00% |
|
Właścicielem praw autorskich jest Szkoła Główna Handlowa w Warszawie.
