Module also offered within study programmes:
General information:
Name:
Advances in Computer Science
Course of study:
2019/2020
Code:
ZSDA-3-0061-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 inż. Bubak Marian (bubak@agh.edu.pl)
Dyscypliny:
informatyka, informatyka techniczna i telekomunikacja
Module summary

The aim of this subject is to familiarize PhD students with current, valuable trends in computer science on the example of research conducted in the Department of Computer Science. The course includes 10 lectures, 3 hours each, conducted by selected employees of the Department of Computer Science and scientists visiting it (30 hours in total) and a seminar (30 hours). On the basis of these lectures, PhD students study, develop and present selected topics at the seminars..

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)
Social competence: is able to
M_K001 Ability to collaborate and to share knowledge and experience during the research process SDA3A_K01, SDA3A_K03 Activity during classes
Skills: he can
M_U001 Is able to present results of research in English and in a form used during scientific conferences SDA3A_U05 Presentation
Knowledge: he knows and understands
M_W001 Understanding of basic research methods in computers science SDA3A_W03 Presentation
M_W002 Getting knowledge of recent valuable trends in computer science 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
60 30 0 0 0 0 30 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
Social competence
M_K001 Ability to collaborate and to share knowledge and experience during the research process + - - - - + - - - - -
Skills
M_U001 Is able to present results of research in English and in a form used during scientific conferences - - - - - + - - - - -
Knowledge
M_W001 Understanding of basic research methods in computers science + - - - - + - - - - -
M_W002 Getting knowledge of recent valuable trends in computer science + - - - - + - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 135 h
Module ECTS credits 5 ECTS
Udział w zajęciach dydaktycznych/praktyka 60 h
Preparation for classes 30 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 30 h
Realization of independently performed tasks 10 h
Examination or Final test 2 h
Contact hours 3 h
Module content
Lectures (30h):
Topics of lectures

1. Advanced algorithms for computer simulations with B-splines and Non-Uniform 1. Rational B-splines (NURBS) in the frame of isogeometric analysis.
Maciej Paszyński
2. Methods and algorithms of high-dimensional data and complex networks embedding – the role of interactive data visualisation in data science.
Witold Dzwinel
3. Challenges of high performance software construction for the 5G systems and next
generation cloud applications
Krzysztof Zieliński
4. Nature-inspired computing: state-of-the-art solutions and current trends.
Aleksander Byrski, Marek Kisiel-Dorohinicki
5. Solving large scale applications scheduling problems with mathematical programming methods.
Maciej Malawski
6. High Performance Computing, environments and software tools for computational science and Big Data problems.
Jacek Kitowski
7. Selected problems of computational network science: graph models, social network analysis, community detection, identification of roles, graph matching, identification of frequent patterns and anomalies in dynamic graphs, prediction of graph evolution.
Jarosław Kożlak
8. Introduction to computer-aided decision making: how to enhance with computer science
methods an efficient elaboration of the final decision of a team of decision makers.
Grzegorz Dobrowolski
9. Stochastic algorithms of huge data inversion.
Robert Schaefer
10. Cognitive computing – systems that learn, reason and interact with humans naturally.
Bogdan Kwolek
11. Integrating cognitive science in developing emerging technologies: some case studies.
Bipin Indurkhya, Bartlomiej Sniezynski
12. Overview of recent developments in quantum computing and quantum informatics
Marian Bubak, Katarzyna Rycerz

Seminar classes (30h):
Topics of seminars

1. Short PhD students presentations of their research topics as the first step of exchange of ideas and knowledge.
2-9. Eight seminars devoted to presentations of a selected topics based on lectures, students experience, and overview of computer science journals.
10. Final seminar: presentations of students achievements in their PhD researches and assessment of knowledge exchange

Additional information
Teaching methods and techniques:
  • Lectures: lecture, discussion
  • Seminar classes: Presentations, discussions, overviews
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Student should be present at least at 5 seminars and give 2 presentations on her/his PhD, 3 on lecture topics, and 2 overviews of selected computer science journals.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: Yes
    – Participation rules in classes: PhD student should be present at least at 5 lectures
  • Seminar classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: PhD student should be present at least at 5 seminars
Method of calculating the final grade:

The final grade is the average grade of presentations and student’s activity.

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

By participation in the next seminar meeting.

Prerequisites and additional requirements:

None

Recommended literature and teaching resources:

Will be presented during each of lectures.

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

K. Rycerz, M. Bubak,  E. Ciepiela, D. Harezlak, T. Gubala, J. Meizner, M. Pawlik, B. Wilk: Composing, Execution and Sharing of Multiscale Applications, Future Generation Computer Systems, 53, 77-87, 2015
M. Kasztelnik, E. Coto, M. Bubak, M. Malawski, P. Nowakowski, J. Arenas, A. Saglimbeni, D. Testi, A. F. Frangi: Support for Taverna Workflows in the VPH-Share Cloud Platform, Computer Methods and Programs in Biomedicine, 146, 37-46, 2017
P. Nowakowski, M. Bubak, T. Bartyński, T. Gubała, D. Harężlak, M. Kasztelnik, M. Malawski, J. Meizner: Cloud computing infrastructure for the VPH community, Journal of Computational Science, 24, 169-179, 2018
as well as publications of the above listed professors – authors of 10 lectures.

Additional information:

PhD students will be asked to participate in conferences at the Department of Computer Science as well as in selected PhD defenses; their observations will be discussed at seminars.