AGH University of Science and Technology in Cracow, Poland
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 14 lectures 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 AIKE Team, please enrol with the following code: phqsa8z
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.
We will work with Jupyter notebooks prepared for you for the lectures and the project 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!
05.03., 12.03., 19.03., 26.03. - working with the prepared Jupyter notebooks and learning various issues of deep learning
16.04. - choosing topics of the final project by students (students' proposals are invited)
14.05. - mid-semester presentations of the progress of the project implementations and consultations
11.06. - final project presentations and evaluation
What is this course about?
This course is intended to give students a broad overview and deep knowledge about popular solutions and efficient neural network models as well as to learn how to construct and train intelligent learning systems in order to use them in everyday life and work. During the course we will deal with the popular and most efficient models and methods of neural networks, fuzzy systems and other learning systems that enable us to find specific highly generalizing models solving difficult tasks. We will also tackle with various CI and AI problems and work with various data and try to model their structures in such a way to optimize operations on them throughout making data available without necessity to search for them. This is a unique feature of associative structures and systems. These models and methods will allow us to form and represent knowledge in a modern and very efficient way which will enable us to mine it and automatically draw conclusions. You will be also able to understand solutions associated with various tasks of motivated learning and cognitive intelligence.
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 and fuzzy systems 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 Jupiter Notebook.
Own intelligent learning system to use in your life or work.
Satisfaction of enrollment to this course.
What do associated and associations mean? How to model knowledge and intelligence? How to create artificial intelligence?
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!
We start our classes with laboratory classes, followed by project classes. During the laboratory classes, you will 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 out on 19th June. 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!
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.