Moduł oferowany także w ramach programów studiów:
Informacje ogólne:
Nazwa:
Artificial Intelligence
Tok studiów:
2017/2018
Kod:
IES-2-306-NA-s
Wydział:
Informatyki, Elektroniki i Telekomunikacji
Poziom studiów:
Studia II stopnia
Specjalność:
Networks and Services
Kierunek:
Electronics and Telecommunications
Semestr:
3
Profil kształcenia:
Ogólnoakademicki (A)
Język wykładowy:
Angielski
Forma i tryb studiów:
Stacjonarne
Osoba odpowiedzialna:
dr hab. inż. Chołda Piotr (cholda@agh.edu.pl)
Osoby prowadzące:
dr hab. inż. Chołda Piotr (cholda@agh.edu.pl)
Krótka charakterystyka modułu

The conversatory introduces fundamental concepts of artificial intelligence (AI). A practical project is aimed at implementation of an exemplary AI system related to a networking application.

Opis efektów kształcenia dla modułu zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Powiązania z EKK Sposób weryfikacji efektów kształcenia (forma zaliczeń)
Wiedza
M_W001 Student gets extended knowledge in the area of artificial intelligence (AI) in general. ES2A_W11 Prezentacja,
Udział w dyskusji
M_W002 Student gets extended and integrated knowledge (notions and methods) in the area of application of various concepts of AI in networking. ES2A_W11 Prezentacja,
Udział w dyskusji
Umiejętności
M_U001 Student is able to learn on her/his own and use the scientific literature, as well as retrieve information from databases on AI. Student is prepared to draw critical conclusions on the issues concerning system and network planning on this basis. ES2A_U02, ES2A_U01 Prezentacja,
Projekt
M_U002 Student can design a system or network using AI methods according to adopted assumptions or constraints. ES2A_U04, ES2A_U02, ES2A_U05, ES2A_U06 Projekt
M_U003 Student is able to prepare a detailed documentation on the results of a project based on assumed properties. Student can deliver a report summarizing these results in the form of a concise paper and/or public presentation. ES2A_U02, ES2A_U03 Projekt,
Przygotowanie i przeprowadzenie badań,
Sprawozdanie,
Wykonanie projektu,
Zaangażowanie w pracę zespołu
Kompetencje społeczne
M_K001 Student can critically and creatively approach a network or system design problem, pose it, and analyze it on her/his own. ES2A_K01, ES2A_K02 Praca wykonana w ramach praktyki ,
Projekt
Matryca efektów kształcenia w odniesieniu do form zajęć
Kod EKM Student, który zaliczył moduł zajęć wie/umie/potrafi Forma zajęć
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Inne
E-learning
Wiedza
M_W001 Student gets extended knowledge in the area of artificial intelligence (AI) in general. - - - - + - - - - - -
M_W002 Student gets extended and integrated knowledge (notions and methods) in the area of application of various concepts of AI in networking. - - - - + - - - - - -
Umiejętności
M_U001 Student is able to learn on her/his own and use the scientific literature, as well as retrieve information from databases on AI. Student is prepared to draw critical conclusions on the issues concerning system and network planning on this basis. - - - + + - - - - - -
M_U002 Student can design a system or network using AI methods according to adopted assumptions or constraints. - - - + - - - - - - -
M_U003 Student is able to prepare a detailed documentation on the results of a project based on assumed properties. Student can deliver a report summarizing these results in the form of a concise paper and/or public presentation. - - - + - - - - - - -
Kompetencje społeczne
M_K001 Student can critically and creatively approach a network or system design problem, pose it, and analyze it on her/his own. - - - + - - - - - - -
Treść modułu zajęć (program wykładów i pozostałych zajęć)
Ćwiczenia projektowe:
Project is performed in teams

Selection of application of the AI concept and development of an exemplary system based on it. Examples: chatbots, games with AI, etc. A short documentation should be provided and the results must be presented to the classmates.

Konwersatorium:
The course is lead in the seminar/conversatory manner

The participants are obliged to get to know some materials before the conversatory meeting, and during the meeting the problems are discussed in open. The discussion is led by one of the participants with the help of the prepared presentation.

The subset of the following topics is going to be covered at participants’ discretion: machine learning, heuristic search, Turing test, expert systems, agent systems, evolutionary algorithms for optimization, evolutionary game theory.

Nakład pracy studenta (bilans punktów ECTS)
Forma aktywności studenta Obciążenie studenta
Sumaryczne obciążenie pracą studenta 60 godz
Punkty ECTS za moduł 3 ECTS
Udział w konwersatoriach 30 godz
Wykonanie projektu 6 godz
Przygotowanie do zajęć 7 godz
Przygotowanie sprawozdania, pracy pisemnej, prezentacji, itp. 7 godz
Udział w ćwiczeniach projektowych 10 godz
Pozostałe informacje
Sposób obliczania oceny końcowej:

Conversatory
It is necessary to prepare a presentation (presentations) on a selected topic and lead a discussion on it. The number of presentations is related to the fair share of all the participants and the number of meetings. The grade is found as the maximum of m and n, where m is the grade proposed by the teacher and n is the median of the grades proposed by other participants of the course. Additionally:

  1. No more than 40% of absences at the conversatory meetings are acceptable.
  2. The teacher must be provided a presentation on a selected topic at least a week prior to the meeting.
  3. The presentation should be prepared according to the suggestions of the teacher.

If a student fails to conform to these rules, a revision test should be passed to obtain a positive grade.

Project
It is necessary to prepare the software implementing the assumed functionality, a short (up to 5 pages long) report, and present the project to the classmates.

Final grade
The both aspects should be graded positively, and then the final grade is calculated as the mean of the credit for the conversatory and for the project.

If any grade is determined based on achieved scores, the grading scale of §13, pt. 1 of the Study Regulations is applied. If any grade is determined on the basis of the weighted average of other grades, the thresholds defined in §27, pt. 4 of the Study Regulations are applied.

Wymagania wstępne i dodatkowe:

None.

Zalecana literatura i pomoce naukowe:
  1. S.Russell, P.Norvig, Artificial Intelligence – A Modern Approach, Prentice Hall 2010.
  2. W. Ertel, Introduction to Artificial Intelligence, Springer 2011.
  3. P. Hingston, Ed., Believable Bots. Can Computers Play Like People?, Springer 2012.
  4. J. McCormack, M. d’Inverno, Eds., Computers and Creativity, Springer 2012.
  5. S. Edelkamp, S. Schrödl, Heuristic Search, Morgan Kaufmann 2012.
  6. E. Sanchez, G. Squillero, A. Tonda, Industrial Applications of Evolutionary Algorithms, Springer 2012.
  7. J. Watt, R. Borhani, A. K. Katsaggelos, Machine Learning Refined, Cambridge University Press 2016.
  8. L. Torgo, Data Mining with R, CRC Press 2017.
Publikacje naukowe osób prowadzących zajęcia związane z tematyką modułu:
  1. Piotr Chołda, Piotr Guzik, Krzysztof Rusek, Risk Mitigation in Resilient Networks. Proc. 6th International Workshop on Reliable Networks Design and Modeling RNDM 2014, 17-19 November, 2014 Barcelona, Spain.
Informacje dodatkowe:

None.