AGH University of Krakow, Poland (Previous name: AGH University of Science and Technology)
Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering
Department of Biocybernetics and Biomedical Engineering
Field of Biocybernetics, Cognitivistics and Artificial Intelligence
This course includes 28 lectures, 14 laboratory classes, and 14 project classes.
Due to the coronavirus threat and the temporal suspension of classes at the university, we will meet on MS Teams.
To join the KBCIDMBM Team, please enrol with the following code: axz8c3b
All lectures, laboratory and project classes will run remotely on MS Teams. Your attendance will be checked each time at the beginning of the laboratory and project classes with a short knowledge quiz with a few questions relating to the last lecture, so please be on time to fill in this test and electronically sign up for the classes. You will also get some points for the correct answers to these questions which will be added during the whole semester. They will be added to the points earned for the accomplishment of the other laboratory and project tasks, and altogether will produce your final grade of this course.
In this year, we will work with brand new Jupyter notebooks prepared for you for the lectures and the laboratory classes to dive faster, deeper, and easier to the essential topics of machine learning, deep learning, and computational intelligence. Thanks to these notebooks, you will have the opportunity to experiment with the code and accomplish more exciting tasks. I hope you will enjoy it!
What is this course about?
This course is intended to give students a broad overview and deep knowledge about popular models, techniques, and solutions of computational intelligence and data mining that can be applied to various biomedicine tasks. We will learn how to construct and train intelligent learning systems and automatically mine data for frequent patterns in order to use them in biomedical issues. During the course, we will deal with the famous and most efficient models and methods of neural networks, evolutionary approaches, fuzzy systems, and associatively-cognitive knowledge-based systems that enable us to find specific highly generalizing models solving difficult tasks. We will also tackle with various CI and KE problems and work with multiple data and try to model their structures efficiently to reduce resource consumption and make our solutions widely available in medicine. Students will be encouraged to undertake small research topics that can be further broadened in Master's or Ph.D. theses and scientific papers.
Maybe you would like to take part in IT competitions (e.g. Kaggle) and earn a lot of money in various competitions?!
Today, computational intelligence (CI), machine learning (ML), data mining (DM), and artificial intelligence (AI) achieved already a lot! We have many interesting methods, models and computational tools which let us adapt CI models to many practical tasks. The CI professionals can earn even a six-figure salary for their work, but it is only a tip of the iceberg! We can still achieve a great development in artificial intelligence can be still far more intelligent than we meet today. Today, we rather develop intelligently adapting computational tools than real artificial intelligence! Artificial Intelligence (AI) can do much more and without so much effort from the people's point of view. It must be embodied and equipped with some smartly motivational mechanisms and associative properties (known from our brains) which let it achieve an adequate level.
Lectures will be supplemented by laboratory and project classes during which you will train and adapt the models learned during the lectures on various data using self-developed applications and existing modern programistic tools, environment and libraries like TensorFlow, Keras, Jupiter Notebook, and Google Colaboration. Your hard work and practice will enable you not only to obtain expert knowledge and skills but also to develop your own intelligent learning system implementing a few of the most popular and efficient CI methods.
Expected results of taking a part in this course:
Broad knowledge of neural networks, associative systems, and evolutional approaches as well as other intelligent learning systems.
Novel experience and broaden skills in construction, adaptation and training of neural networks and fuzzy systems.
Ability to construct intelligent learning systems of various kinds, especially deep learning solutions using modern Python 3 and libraries like TensorFlow, Keras and Jupiter Notebook.
Good and modern practices in modeling, construction, learning and generalization using developed and other tools like RapidMiner, TensorFlow, Keras and Jupyter Notebook.
Own intelligent learning system to use in your life or work.
Satisfaction of enrollment to this course.
When does the course begin and what is the schedule of activities?
Students are invited to broaden their excellent projects from the project classes to their Master's theses and even scientific papers with help and contribution of the lecturer.
However lectures are generally not obligatory every student who enrolled in this course is invited to take a part in possibly all lectures which will be very intensive and full of news! The presented topics during lectures will be practically accomplished in the laboratory and project classes, so be conscious that the absence in the lectures will cause serious difficulties in the implementation of methods and accomplishing of tasks during the laboratory and project classes that will be evaluated after achieved results, implemented methods, and their quality. This course will be as much interesting, innovative, and valuable for you as the frequency and numerous presence of the participating students. Thus, all participating students are asked to organize their weekly schedule in such a way to be able to take a part in all course activities including lectures! Moreover, your ideas, whishes, and proposals can be also included and used in this course. We can do this course fascinating only together!
During the laboratory classes, you will use existing CI and DM tools as well as implement the selected and the topmost practically promising tasks in the programming language you like. You can use computers and programming languages installed in the laboratory (Python, C#, C++, Java) or can use your computers together with your compilers and programming languages you prefer. During the project classes, you will implement the selected project that will substantially expand the chosen laboratory class task or will be a new one after your selection from the list of the proposed projects. However, we can also consider your proposals that will be ambitious enough.
Detailed information will be presented at the beginning of this course, i.e. during the first lecture, as well as during the first laboratory classes and the first project classes.
The presentations of students' final projects and laboratory tasks will be carried during the last classes of the semester. All students are invited to come to all your final presentations together in order to learn from your fascinating works, results and presentation! Please, do not forget to take with all components (the solved tasks, the final project, applications, source codes, data, databases, sources, the final presentation etc.) which proof your self-execution of all tasks and the project as well as the hopefully positive grades that you will get!
Nikola K. Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, In Springer Series on Bio- and Neurosystems, Vol 7., Springer, 2019.
A. Horzyk and J.A. Starzyk, Fast Neural Network Adaptation with Associative Pulsing Neurons, IEEE Xplore, In: 2017 IEEE Symposium Series on Computational Intelligence, pp. 339-346, 2017. - presentation, movie Iris-4, movie Iris-12
A. Horzyk, Human-Like Knowledge Engineering, Generalization and Creativity in Artificial Neural Associative Systems, Springer Verlag, AISC 11156, ISSN 2194-5357, ISBN 978-3-319-19089-1, ISBN 978-3-319-19090-7 (eBook), DOI 10.1007/978-3-319-19090-7, Springer, Switzerland, 2016, pp. 39-51.
A. Horzyk, Innovative types and abilities of neural networks based on associative mechanisms and a new associative model of neurons – the invited talk and paper at the International Conference ICAISC 2015, Springer Verlag, LNAI 9119, 2015, pp. 26-38, DOI 10.1007/978-3-319-19324-3_3.
