Artificial Intelligence
Informacje ogólne
Kod przedmiotu: | 220621-D |
Kod Erasmus / ISCED: |
11.4
|
Nazwa przedmiotu: | Artificial Intelligence |
Jednostka: | Szkoła Główna Handlowa w Warszawie |
Grupy: |
Elective courses for AAB - masters Elective courses for QEM - masters Przedmioty kierunkowe do wyboru SMMD-ADA Przedmioty kierunkowe do wyboru SMMD-MIS |
Punkty ECTS i inne: |
3.00 (zmienne w czasie)
|
Język prowadzenia: | angielski |
Efekty uczenia się: |
Wiedza: Students should be able to describe the main areas of Artificial Intelligence, Students should be able to define and describe the methods of knowledge representation Students should be able to describe the uncertainty representation methods Students should be able to define the concept of heuristic, list and classify the search methods in the large problem spaces Students should be able to define and give examples of machine learning Umiejętności: Students should be able to properly classify the problem, Students should be able to estimate if it is possible and is it worth to solve the problem using AI methods Students should be able to choose appropriate method to solve the given problem Students should be able to apply chosen method to solve the problem Students should be able to compare the solution with the solutions obtained using other methods (e.g. econometric or statistical) Kompetencje społeczne: Ability to crative thinking and solving the real life complex problems, Ability to cooperate in the group. |
Zajęcia w cyklu "Semestr letni 2024/25" (jeszcze nie rozpoczęty)
Okres: | 2025-02-15 - 2025-09-30 |
Przejdź do planu
PN WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Michał Bernardelli | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Laboratorium - Ocena |
|
Skrócony opis: |
Presentation of the basic artificial intelligence methods with implementation (in R language) and examples of use. Among the commonly known methods, genetic algorithms, classification trees, random forests, and artificial neural networks are discussed in more detail. Several alternative modelling methods are the basis of student designs. |
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Pełny opis: |
The aim of the lectures is to present the fundamental knowledge of Artificial Intelligence (AI) emphasizing the economic aspects of AI. Additionally the aim of the classes is learning how to solve chosen economic problems using methods and tools of Artificial Intelligence. Artificial Intelligence (AI) is the field of computer science that seeks to understand and implement computer-based technology that can simulate: characteristics of human intelligence or processes observed in the nature. Lectures will present methods and techniques of AI e.g. : knowledge representation, uncertainty representation, machine learning, neural networks, genetic algorithms, data mining, web mining, and text mining. Students will have the opportunity to solve: optimization, classification and prediction issues using AI methods and tools. |
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Literatura: |
Literatura podstawowa: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning Michael Nielsen, Neural Networks and Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R Brad Boehmke, Brandon M. Greenwell, Hands-On Machine Learning with R Fred Nwanganga, Mike Chapple, Practical Machine Learning in R Dominic Lordy, Machine Learning with R: Step by Step Guide for Newbies Literatura uzupełniająca: E.Rich, K.Knight, Artificial Intelligence, McGrawHill, 1991 E.Turban, J.Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall 1998 |
|
Uwagi: |
Kryteria oceniania: egzamin tradycyjny-pisemny: 50.00% projekty: 50.00% |
Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)
Okres: | 2024-10-01 - 2025-02-14 |
Przejdź do planu
PN WT ŚR CZ LAB
LAB
PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Michał Bernardelli | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Laboratorium - Ocena |
|
Skrócony opis: |
Presentation of the basic artificial intelligence methods with implementation (in R language) and examples of use. Among the commonly known methods, genetic algorithms, classification trees, random forests, and artificial neural networks are discussed in more detail. Several alternative modelling methods are the basis of student designs. |
|
Pełny opis: |
The aim of the lectures is to present the fundamental knowledge of Artificial Intelligence (AI) emphasizing the economic aspects of AI. Additionally the aim of the classes is learning how to solve chosen economic problems using methods and tools of Artificial Intelligence. Artificial Intelligence (AI) is the field of computer science that seeks to understand and implement computer-based technology that can simulate: characteristics of human intelligence or processes observed in the nature. Lectures will present methods and techniques of AI e.g. : knowledge representation, uncertainty representation, machine learning, neural networks, genetic algorithms, data mining, web mining, and text mining. Students will have the opportunity to solve: optimization, classification and prediction issues using AI methods and tools. |
|
Literatura: |
Literatura podstawowa: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning Michael Nielsen, Neural Networks and Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R Brad Boehmke, Brandon M. Greenwell, Hands-On Machine Learning with R Fred Nwanganga, Mike Chapple, Practical Machine Learning in R Dominic Lordy, Machine Learning with R: Step by Step Guide for Newbies Literatura uzupełniająca: E.Rich, K.Knight, Artificial Intelligence, McGrawHill, 1991 E.Turban, J.Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall 1998 |
|
Uwagi: |
Kryteria oceniania: egzamin tradycyjny-pisemny: 50.00% projekty: 50.00% |
Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)
Okres: | 2024-02-24 - 2024-09-30 |
Przejdź do planu
PN LAB
LAB
WT ŚR CZ PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Michał Bernardelli | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Laboratorium - Ocena |
|
Skrócony opis: |
Presentation of the basic artificial intelligence methods with implementation (in R language) and examples of use. Among the commonly known methods, genetic algorithms, classification trees, random forests, and artificial neural networks are discussed in more detail. Several alternative modelling methods are the basis of student designs. |
|
Pełny opis: |
The aim of the lectures is to present the fundamental knowledge of Artificial Intelligence (AI) emphasizing the economic aspects of AI. Additionally the aim of the classes is learning how to solve chosen economic problems using methods and tools of Artificial Intelligence. Artificial Intelligence (AI) is the field of computer science that seeks to understand and implement computer-based technology that can simulate: characteristics of human intelligence or processes observed in the nature. Lectures will present methods and techniques of AI e.g. : knowledge representation, uncertainty representation, machine learning, neural networks, genetic algorithms, data mining, web mining, and text mining. Students will have the opportunity to solve: optimization, classification and prediction issues using AI methods and tools. |
|
Literatura: |
Literatura podstawowa: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning Michael Nielsen, Neural Networks and Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R Brad Boehmke, Brandon M. Greenwell, Hands-On Machine Learning with R Fred Nwanganga, Mike Chapple, Practical Machine Learning in R Dominic Lordy, Machine Learning with R: Step by Step Guide for Newbies Literatura uzupełniająca: E.Rich, K.Knight, Artificial Intelligence, McGrawHill, 1991 E.Turban, J.Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall 1998 |
|
Uwagi: |
Kryteria oceniania: egzamin tradycyjny-pisemny: 50.00% projekty: 50.00% |
Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)
Okres: | 2023-10-01 - 2024-02-23 |
Przejdź do planu
PN WT LAB
ŚR CZ PT |
Typ zajęć: |
Laboratorium, 30 godzin
|
|
Koordynatorzy: | (brak danych) | |
Prowadzący grup: | Michał Bernardelli | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Ocena
Laboratorium - Ocena |
|
Skrócony opis: |
Presentation of the basic artificial intelligence methods with implementation (in R language) and examples of use. Among the commonly known methods, genetic algorithms, classification trees, random forests, and artificial neural networks are discussed in more detail. Several alternative modelling methods are the basis of student designs. |
|
Pełny opis: |
The aim of the lectures is to present the fundamental knowledge of Artificial Intelligence (AI) emphasizing the economic aspects of AI. Additionally the aim of the classes is learning how to solve chosen economic problems using methods and tools of Artificial Intelligence. Artificial Intelligence (AI) is the field of computer science that seeks to understand and implement computer-based technology that can simulate: characteristics of human intelligence or processes observed in the nature. Lectures will present methods and techniques of AI e.g. : knowledge representation, uncertainty representation, machine learning, neural networks, genetic algorithms, data mining, web mining, and text mining. Students will have the opportunity to solve: optimization, classification and prediction issues using AI methods and tools. |
|
Literatura: |
Literatura podstawowa: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning Michael Nielsen, Neural Networks and Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning with Applications in R Brad Boehmke, Brandon M. Greenwell, Hands-On Machine Learning with R Fred Nwanganga, Mike Chapple, Practical Machine Learning in R Dominic Lordy, Machine Learning with R: Step by Step Guide for Newbies Literatura uzupełniająca: E.Rich, K.Knight, Artificial Intelligence, McGrawHill, 1991 E.Turban, J.Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall 1998 |
|
Uwagi: |
Kryteria oceniania: egzamin tradycyjny-pisemny: 50.00% projekty: 50.00% |
Właścicielem praw autorskich jest Szkoła Główna Handlowa w Warszawie.