Module also offered within study programmes:
General information:
Name:
Advanced AI systems
Course of study:
2019/2020
Code:
ZSDA-3-0125-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr hab. Bielecki Andrzej (bielecki@agh.edu.pl)
Dyscypliny:
Moduł multidyscyplinarny
Module summary

The module covers selected issues of advanced artificial intelligence systems. The topics of advanced neural systems, advanced fuzzy inference systems and expert systems are discussed. Hybrid AI systems are considered as well. Applications in technology, industry, medicine and economics are discussed.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Skills: he can
M_U001 The student is able to assess whether a given problem can be solved with the help of AI. SDA3A_U01 Activity during classes
M_U002 The student is able to discuss the problems related to AI. SDA3A_U02 Activity during classes
Knowledge: he knows and understands
M_W001 The student knows the current trends in artificial intelligence systems, both theoretical foundations and applications. SDA3A_W01 Activity during classes
M_W002 The student knows the current methodology of scientific research of AI systems. SDA3A_W02 Activity during classes
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
15 5 0 0 0 0 10 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Skills
M_U001 The student is able to assess whether a given problem can be solved with the help of AI. - - - - - + - - - - -
M_U002 The student is able to discuss the problems related to AI. - - - - - + - - - - -
Knowledge
M_W001 The student knows the current trends in artificial intelligence systems, both theoretical foundations and applications. + - - - - - - - - - -
M_W002 The student knows the current methodology of scientific research of AI systems. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 20 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 15 h
Preparation for classes 1 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 2 h
Realization of independently performed tasks 2 h
Module content
Lectures (5h):
-
Seminar classes (10h):
-
Additional information
Teaching methods and techniques:
  • Lectures: Lectures.
  • Seminar classes: Discussions and presentations.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Class attendance, participation in discussions, preparing presentations.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Attendance at lectures.
  • Seminar classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Participation in discussions and presentations.
Method of calculating the final grade:

Attendance at all classes is the basis for obtaining a satisfactory grade. If, moreover, the student actively participated in the discussions, he gets a good grade. If, in addition, the student has prepared a presentation, he receives a very good grade.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Individual consultations.

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:

Recommended literature – scientific articles – will be provided by the lecturer at the beginning of the semester in order to take into account the most recent scientific results. The basic knowledge can be found in:
Flasiński M. (2017),
Introduction to Artificial Intelligence,
Springer.
(Polish edition of the handbook:
Flasiński M. (2011),
Wstęp do sztucznej inteligencji,
PWN,)

Scientific publications of module course instructors related to the topic of the module:

1. Brodowski S., Bielecki A., Filocha M. (2017),
A hybrid system for forecasting 24-hour power load profile for Polish electric grid,
Applied Soft Computing, vol.58, 527-539.

2. Bielecki A., Wójcik M. (2017),
Hybrid system of ART and RBF neural networks for online clustering.
Applied Soft Computing, vol.58, 1-10.

3. Bielecki A., Śmigielski P. (2017)
Graph representation for two-dimensional scene understanding by the cognitive vision module,
International Journal of Advanced Robotic Systems, vol.14(1), 1-14.

4. Bielecki A., Buratowski T., Śmigielski P. (2013),
Recognition of two-dimensional representation of urban environment for autonomous flying agents,
Expert Systems with Applications, vol.40, 3623-3633.

5. Bielecki A., Korkosz M., Zieliński B. (2008),
Hand radiographs preprocessing, image representation in the finger regions and joint space width
measurements for image interpretation,
Pattern Recognition, vol.41, 3786-3798.

6. Bielecki A., Podolak I.T., Wosiek J., Majkut E. (1996),
Phonematic translation of Polish texts by the neural network,
Acta Physica Polonica, Series B., vol.27, 2253-2264.

Additional information:

None