To master the fundamentals of the common language and basic technical terms of particular fields of study; with pre-intermediate students emphasis on reading technical texts and presentations of technical articles, application of French Internet websites.
Capelle Guy, Menand Robert: Le Nouveau taxi 1, Hachette FLE Paris, France 2009, ISBN 978-2-01-155548 - 9
test
French language is taught at two levels: beginner and intermediate. Students opt for one of them depending on whether they wish to obtain the fundamentals of the language or wish to maintain and/or improve previous knowledge of French.
100/0
To master the fundamentals of the common language and basic technical terms of particular fields of study; with pre-intermediate students emphasis on reading technical texts and presentations of technical articles, application of French Internet websites.
Capelle Guy, Menand Robert: Le Nouveau taxi 1, Hachette FLE Paris, France 2009, ISBN 978-2-01-155548 - 9
test
The subject continues the program of French language (1) and provides courses of essential and intermediate French language.
100/0
To master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
Appropriate study material is supplied by teacher based on the participants’level of German proficiency.
test
German language is taught at three levels: beginner, intermediate and advanced. Students opt for one of them depending on whether they need to learn the fundamentals or maintain and/or improve their previous knowledge.
This course’s focus is to master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
100/0
To master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
To master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
Appropriate study material is supplied by teacher based on the participants’level of German proficiency
test
German language is taught at two levels: beginner and intermediate. Students opt for one of them depending on whether they wish to obtain the fundamentals of the language or wish to maintain and/or improve previous knowledge of German.
This course’s focus is to to master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
100/0
To master the fundamentals of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency )
To master the fundamentals of general Russian. The language level is A1.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
The textbook: : Точка Ру A1 (Ольга Долматова, Екатерина Новачац), рracovné karty Падежи 1 (Л.С. Безкоровайная, В.Е. Штыленко).
test
To master the fundamentals of general Russian. The language level is A1.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
The subject provides a course in Russian language for beginners.
100/0
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
To master the fundamentals of general Russian.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
Textbook: Точка Ру A1 (Ольга Долматова, Екатерина Новачац), рracovné karty Падежи 1 (Л.С. Безкоровайная, В.Е. Штыленко).
test
To master the fundamentals of general Russian.
Learning the Cyrillic (Russian) alphabet, gaining basic language competence, building up skills and confidence in dealing with unfamiliar authentic and semi-authentic texts.
The subject continues the program of Russian language (1) and provides a course of Russian for beginners.
100/0
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
Krížom- Krážom Slovenčina 1, additional material to further support the covered topics.
tests
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
The sylabus is targeted at the comprehension of the basics of the Slovak language for the absolute beginners (A1).
100/0
This course is aimed for foreign students to learn the fundamentals of the Slovak language with the focus on basic communication as well as all other language skills- listening comprehension,reading and writing.
Krížom- Krážom Slovenčina 1, additional material to further support the covered topics
tests
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
The sylabus is targeted at the comprehension of the basics of the Slovak language for the absolute beginners (A1) and this course is a follow up course to the Slovak language course 1.
100/0
This course is aimed for foreign students to learn the fundamentals of the Slovak language with the focus on basic communication as well as all other language skills- listening comprehension,reading and writing.
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational English as well as English for specific purposes appropriate for university students. This course is a follow up of the previously taught ESP course.
Appropriate study material is supplied based on the participants’level of English by the lecturer.(Sources- The Guardian, The Herald Morning Sun. The Nine News, The West Australian, BBC News and podcasts, CNN podcasts).
tests, oral presentations, essays
tests, presentations, essays
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
This course’s focus is to broaden spoken/communicational English for students with B2/C1 level of English knowledge.
100/0
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational English as well as English for specific purposes appropriate for university students. This course is a follow up of the previously taught ESP course.
tests
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational/spoken English as well as English for specific purpose appropriate for university students. This course is a follow up of the Conversational English course 1.
Appropriate study material is supplied based on the participants’level of English by the lecturer.(Sources- The Guardian, The Herald Morning Sun. The Nine News, The West Australian, BBC News and podcasts, CNN podcasts).
tests, oral presentations, essays
tests, oral presentations, essays
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
This course’s focus is to broaden spoken/communicational English for students with B2/C1 level of English knowledge( Upper-Intermediate/Lower Advanced).
100/0
Continual improvement of all language skills focused on communication/speaking, listening comprehension and writing. The emphasis is on discourse, lexicology and morphology, word-bank broadening of communicational/spoken English as well as English for specific purpose appropriate for university students. This course is a follow up of the Conversational English course 1.
tests
Improvement of common language, deepening the knowledge of technical terms, reading technical texts, presentations of popular scientific articles.
Capelle Guy, Menand Robert: Le Nouveau taxi 1, Hachette FLE Paris, France 2009, ISBN 978-2-01-155548 - 9
test
The subject provides a course of intermediate French language, covering not only general, but also technical language.
100/0
Improvement of common language, deepening the knowledge of technical terms, reading technical texts, presentations of popular scientific articles.
Menand Robert: Le Nouveau taxi 2, Hachette FLE, Paris, France 2009, ISBN 978-2-01-155551 - 9
test
The subject provides a course of intermediate French covering not only general, but also technical French language.
100/0
Deepening the knowledge of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency).
Appropriate study material is supplied by teacher based on the participants’level of German proficiency.
test
The course is a follow-up to the German language (1,2). The subject provides a course of intermediate or advanced German language.
This course’s focus is to deepen the knowledge of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency).
100/0
Master the basics of general language and basic professional terminology of individual fields of study (depending on the advanced level of students)
Deepening the knowledge of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency).
Appropriate study material is supplied by teacher based on the participants’level of German proficiency.
test
The course is a follow-up to the German language (1-3). It provides a course of intermediate and advanced German language.
This course’s focus is to deepen the knowledge of the common language and basic technical terms of particular fields of study (depending on the student's level of German proficiency).
100/0
Master the basics of general language and basic professional terminology of individual fields of study (depending on the advanced level of students)
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
Точка Ру A2 (Ольга Долматова, Екатерина Новачац) a Short Stories in Russian (Olly Richards, Alex Rowlings)
test
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
The course "Russian for Intermediate Students" is a follow-up to "Russian for Beginners". The subject of the course is general Russian in the range appropriate to the given level.
100/0
Basic communication in Russian, developing other Russian language skills - listening comprehension, reading and writing.
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
Точка Ру A2 (Ольга Долматова, Екатерина Новачац) a Short Stories in Russian (Olly Richards, Alex Rowlings)
test
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
The course "Russian for Intermediate Students" is a follow-up to "Russian for Beginners". The subject of the course is general Russian in the range appropriate to the given level.
100/0
Learning the handwritten Russian (Russian Cursive Cyrillic), developing further language skills, gaining knowledge of Russian culture, history and way of life, pre-intermediate to intermediate grammar and vocabulary.
Krížom-Krážom Slovenčina 2, additional material to further support the covered topics.
tests
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
The sylabus is targeted at the comprehension of all the language skills of the Slovak language , and it is a follow up course to the Slovak language course 2.
100/0
This course is aimed for foreign students to better comprehend all the language skills important to enable correct usage of the Slovak language – listening comprehension, reading, writing and speaking.
Krížom-Krážom Slovenčina 2, additional material to further support the covered topics.
tests
Course prerequisites:
https://fmph.uniba.sk/microsites/kjp/katedra-jazykovej-pripravy/poziadavky-na-udelenie-priebezneho-hodnotenia-aj1aj2aj3-ostatne-kurzy/
The sylabus is targeted at the comprehension of all the language skills of the Slovak language , and it is a follow up course to the Slovak language course 3.
100/0
This course is aimed for foreign students to better comprehend all the language skills important to enable correct usage of the Slovak language – listening comprehension, reading, writing and speaking.
Web Style Guide, 4th ed. / P.J. Lynch, S. Horton. Yale University Press, 2016. Available online:
http://webstyleguide.com/
Mobile First. L. Wroblewski, A Book Apart, 2011
Selected current online publications
Semester: project 60pts, final test 15pts (minimum 50%)
Exam: oral exam 25pts (admittance requirement: 60pts from the semester)
Passing the course: 50pts altogether and 50% from the final test Scale: A 90%, B 80%, C 70%, D 60%, E 50%
- Overview of web technologies and web architecture
- Information architecture, types of websites, web applications, components and interfaces
- Client platforms (mobile, tablet, desktop) and implications for web design and development
- Methodologies of website and web application development (waterfall model, agile
methodologies)
- Interaction design methodologies (user research and modelling, iterative prototype-based design,
prototype testing)
- Principles and methodologies of web content curation
- Testing, optimization and maintenance of web applications and web content
- Website and web application quality measures
75/25
An overview of web technologies and their applications for various purposes. Principles and
methodologies of web applications, web user interfaces, and web content design.
Staab, S. and Studer, R. eds., 2010. Handbook on ontologies. Springer Science & Business Media.
Allemang, D. and Hendler, J., 2011. Semantic web for the working ontologist: effective modeling
in RDFS and OWL. Elsevier.
Selected relevant recent scientific papers.
Semester: project (60pts), ongoing work assessment (20pts)
Exam: written exam (20pts)
Min. passing requirements: 50% from the semester and 50% from the exam
Indicative assessment scale: A 90%, B 80%, C 70%, D 60%, E 50%
Slovak, English
- Ontologies and their applications
- Well-known ontologies
- Ontological representation languages (RDF, RDF Schema, OWL)
- Ontologies and databases
- SPARQL query language
- Linked Open Data network
- Ontology engineering
- Applications of ontologies in informatics
80/20
Students become acquainted with ontologies, with their role in data representation and sharing,
with ontological representation and query languages, and with ontology engineering
methodologies. They will also get acquainted with Semantic Web standards and with the principles
and possibilities of publishing data in the Linked Open Data network, as well as the use of such
data in knowledge-based applications.
Discrete and combinatorial mathematics: An applied introduction / Ralph P. Grimaldi. Rose-Hulman Institute of Technology: Pearson, 2004. Calculus / Gilbert Strang. Massachusetts Institute of Technology: Wellesley-Cambridge Press Fundamentals of Linear Algebra / James B. Carrell. Canada: University of British Colombia, 2005 Artificial Intelligence: A Modern Approach (3rd ed.) / Stuart Russell and Peter Norvig. The USA: Pearson, 2010
To be classified, the student has to achieve at least 50% of each activity: project (15%), weekly exams (40%), middle term exam (15%), final exam (30%).
English
1. Basics of mathematical analysis: functions, differential calculus. 3. Basics of linear algebra: matrices and vectors, operations. 3. Basics of statistics and probability theory.
http://dai.fmph.uniba.sk/w/Course:Mathematics/en
Grading: A > 90%, B > 80%, C> 70%, D > 60%, E > 52% points.
The lectures will provide students with basics of propositional and predicate logic, linear algebra, mathematical analysis, and probability that are important for the study of informatics and for (computational) cognitive science. At the same time, the students will learn about mathematical culture, notation, way of thinking and expressing oneself.
Introduction to Psychology (Kalat, 2008)
An Introduction to the History of Psychology (Hergenhahn & Henley, 2014)
Research in Psychology: Methods and Design (Goodwin, 2009)
Stevens' Handbook of Experimental Psychology: Methodology in Experimental Psychology (Pashler & Wixted, 2002)
None.
Seminar project 30% (theoretical assignment, oral presentation)
Seminar reading 10% (presenting a psychological study)
Midterm test 20%
Final test 40%
Active participation +/- 10%
English/Slovak
[1] Introductory lecture
[2] Origins and history
[3] What is mind
[4] Biological psychology
[5] Cognitive psychology
[6] Psychological methods
[7] Personality theories
[8] Clinical psychology
[9] Emotions
http://dai.fmph.uniba.sk/w/Course:Introduction_to_Psychology/en
Midterm: written exam (20%) a seminar projects (30%)m seminar reading (10%).
Examination period: final written exam (40%).
A: 91–100; B: 81–90; C: 73–80; D: 66–72; E: 60–65; Fx: 0–59.
Students will be familiarized with the subject of psychology as a scientific discipline, its main schools (e.g., behaviorism, gestalt psychology, psychoanalysis, cognitive psychology), approaches, and methods used to investigate mind and behavior (e.g., subjective, objective, projective, physiological). Lectures will provide introductory information on fundamental psychological concepts. Students will learn to work independently, read and understand scientific (behavioral) articles, search through scientific databases, review scientific sources, and present topics of their interest.
Cognitive Psychology (Sternberg, 2012)
Cognitive Science: An Introduction to the Study of Mind (Friedenberg & Silverman,)
Memory (Baddeley, Keysenck, Anderson, 2015 )
Stevens' Handbook of Experimental Psychology: Methodology in Experimental Psychology (Pashler & Wixted, 2002)
Seminar project 30% (includes a demonstration and/or practical exercise)
Seminar reading 10% (empirical paper from cognitive psychology)
Midterm test 20%
Final test 40%
Active participation +/- 10%
English / Slovak
[1] Sensation & Perception
[2] Attention
[3] Short-term memory
[4] Working memory and Executive functioning
[5] Long-term memory (Episodic, Semantic)
[6] Mental representation
[7] Language
[8] Cognition & Emotion
[9] Thinking & Reasoning
https://dai.fmph.uniba.sk/w?title=Cognitive_Psychology/en&setlang=en
During course: midterm and two seminar projects (30+10+20) = 60%
Examination period: final exam 40%.
In this course, students of cognitive science will be introduced to the field of cognitive psychology, including topics pertaining to perception, attention, memory and other higher-order cognitive processes. Neurobiological underpinnings of such cognitive phenomena and processes will be provided to facilitate the interdisciplinary understanding of human cognition. Students will learn to work independently (and in groups), assess scientific articles and search in scientific databases, critically read theoretical and empirical papers, review scientific sources, and present topics of their interest.
GÁLIKOVÁ, S.: An Introduction to the Philosophy of Mind. Trnava, FFTU, 2013.
MASLIN, K., T.: An Introduction to thePphilosophy of mind. Cambridge, Polity, 2007
BLOCK, N., FLANAGAN, O., GUZELDERE, G. (1996): The Nature of Consciousness: Philosophical and Scientific Debates. Cambridge, MA, MIT Press.
CHALMERS, D. (1996): The Conscious Mind. New York, Oxford University Press.
DENNETT, D. (1991): Consciousness explained. Little, Brown.
CHURCHLAND, P. M. (1995): The Engine of Reason, the Seat of the Soul. Cambridge, MA, MIT Press.
SEARLE, J. (1992): The Rediscovery of Mind. Cambridge, MA, MIT Press.
GÁLIKOVÁ, S.(2013): Philosophy of Consciousness. Towarzystwo Słowaków w Polsce.
Final paper- analytic-critical study (100%)
Grade A: 90% , B: 80% , C 70%, D 60%, and grade E at minimum 50%.
English
From the history in studying human mind and consciousness
Basic terminology, concepts: mind, consciousness, reason, mental states, subjectivity, mental causality, self etc.;
Taxonomy of approaches in the study on the nature of mind (dualism, identity theory, functionalism, naturalism etc.);
The concept and problem of consciousness,
Medicine and Philosophy (study of normal and pathological cases)
Impairments of Consciousness – Novel methods of investigating the states of consciousness.
Models and theories of conscious experience.
https://dai.fmph.uniba.sk/w/Course:Introduction_to_Philosophy_of_Mind/en
Students acquire knowledge of representative philosophical conceptions, theories and problems within contemporary philosophy of mind. They will be acquainted with main arguments and types of argumentation on the mind/body problem and they will improve their capacity for critical discussion and ability to defend their own attitudes.
Craenen B., Eiben A. (2003): Computational Intelligence. In: Encyclopedia of Life Support Sciences, EOLSS Publishers Co.
Engelbrecht A. (2007). Computational Intelligence: An Introduction (2nd ed.), John Willey & Sons. Available in faculty library.
Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in the faculty library.
Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
Woergoetter F., Porr B. (2008). Reinforcement learning, Scholarpedia, 3(3):1448.
Zadeh L. (2007). Fuzzy logic, Scholarpedia, 3(3):1766.
None.
Grading: 91-100% = A, 81-90% = B, 71-80% = C, 61-70% = D, 51-60% = E, else Fx.
https://dai.fmph.uniba.sk/w/Course:Introduction_to_computational_intelligence/en
Active participation during the semester (max. 14 points).
Written mid-term test (max. 12 points).
Final written-oral exam (max. 24 points).
The course objectives are to make the students familiar with basic principles of various computational methods of data processing that can commonly be called computational intelligence (CI). This includes mainly bottom-up approaches to solutions of (hard) problems based on various heuristics (soft computing), rather than exact approaches of traditional artificial intelligence based on logic (hard computing). Examples of CI are nature-inspired methods (artificial neural networks, evolutionary algorithms, fuzzy systems), as well as probabilistic methods and reinforcement learning. After the course the students will be able to conceptually understand the important terms and algorithms of CI, and choose appropriate method(s) for a given task. The theoretical introduction is combined with practical examples.
American Psychological Association (2010). Publication Manual of the American Psychological
Association. Washington: American Psychological Association.
Cooper, H. (2010). Reporting Research in Psychology. How to meet Journal Article Reporting
Standards. Washington: American Psychological Association.
Field, A. (2005). Discovering statistics using SPSS. London: Sage Publications.
Gould J. C. (2002). Concise Handbook of Experimental Methods for the Behavioral and Biological Sciences. London: CRC Press.
Harris, P. (2008). Designing and reporting experiments in psychology. Berkshire: McGraw-Hill.
Sani, F., & Todman, J. (2006). Experimental Design and Statistics for Psychology. A First Course. Oxford: Blackwell Publishing.
None.
At least 50% of the points within each of the 5 items of evaluation.
I. BEFORE EXPERIMENTING
1. Course introduction.
2. Variables and operationalization.
3. Deriving and testing research hypotheses.
4. Population and research sample. Generalization. Research ethics.
5. IMRaD. Sections of research report.
II. EXPERIMENTING
6. Research design. Introduction to experimental design.
7. Experimental and quasi-experimental design.
8. Data analysis: applied statistics I.
9. Significance testing.
10. Data analysis: applied statistics II.
III. AFTER EXPERIMENTING
11. How to write. How to present.
12. Final “open-book“ exam
http://dai.fmph.uniba.sk/w/Course:Methodology_of_Empirical_Research/en
1. Course assignments – 20%
2. Semestral project: preliminary text – 10%
3. Semestral project: full paper – 30%
4. Semestral project: presentation – 10%
5. Final “open-book“ exam – 30%
Students will acquire knowledge and skills in the field of quantitative, primarily experimental research: from design, through implementation, to data analysis, reporting, and interpretation of findings. After this course, students should be able to (critically) analyze research studies. Course will also provide practical experience with conducting own experiment, including basic statistical analysis.
Silverman G., Friedenberg J. (2011): Cognitive science. An introduction to the study of mind. SAGE.
Thagard, P. (2005): Mind: Introduction to Cognitive Science, 2nd Edition. MIT Press.
Stainton, J.R (2006): Contemporary Debates in Cognitive Science. Wiley.
Bermúdez, J. L.(2014): Cognitive science. An introduction to the science of the mind. Cambridge University Press.
Readings 25%
Short oral presentations 15%
Paper 30%
Paper peer review 10%
Group work & colloquium 10%
Integration reflection 10%
English
History, object and methods of cognitive science. Representational paradigms: functionalism, cognitivism, connectionism, embodiment, dynamical systems. Computational modeling. Neuroscience. Ethical aspects of research in cognitive science.
http://dai.fmph.uniba.sk/courses/ICS/
50/50
Content-wise, the students will learn about:
History, theories, methods and topics of cognitive science
Disciplines of cognitive science, their specific contributions
Representational paradigms
Cognitive modeling
Ethical aspects of cognitive science and technologies
Method-wise, the students will acquire the following skills:
Think in an interdisciplinary way, appreciate multiple perspectives.
Search and critically evaluate scientific knowledge sources.
Critically read papers of different disciplines/styles.
Orally present topics of interest.
Learn about academic honesty practices and plagiarism.
Write a scientific paper and cite literature properly.
Review their peer's paper.
Work independently, participate in discussions.
Koukolík,F.: Mozek a jeho duše. 3.vyd. Galén, Praha, 2005, 275 s.
Bear, M.F., Connors, B.W., Paradiso, M.A.: Neuroscience – exploring the brain. 2nd ed. Lippincott, Williams and Wilkins, Baltimore, 2001, 855 pp.
Gazzaniga,M.S., Ivry,R.B., Mangun,G.R.: Cognitive neuroscience – the biology of the mind. W.W.Norton, New York, 2002, 681 pp.
Kandel, E.R., Schwartz, J.H., Jessel, T.M.: Principles of Neural Science. 4th ed. McGraw-Hill Medical, 2000, 1414pp.
Neuro-immuno-endocrine regulation of human body.
Gross functional anatomy of the nervous system, the brain as the main regulatory organ.
The role of glial cells. Complex functions of the brain and the brain programmes.
Neuronal signalling. The membrane potential, receptor potential. Nerve impulses, action potential. Conduction of nerve impulses. Stimulation of nerve fibres, refractory periods.
Overview of neuronal communication, synaptic transmission, the role of myelin.
Developmental physiology of the brain. Neuronal plasticity. Functional organization of neuronal circuits.
Electrocorticogram, electroencephalogram, evoked potentials. Sleep and wakefulness.
Physiology of emotions, behaviour and motivation. Higher nervous functions including memory, learning and speech.
Functional specialization of brain hemispheres and gender dimorphism.
O'Reilly, R.C. et al. (2016). Computational Cognitive Neuroscience. Wiki Book. https://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Main
Each student gives 2 presentations - one during one of the lectures and one during one of the labs. The lecture presentation is based on a scientific paper related to the lecture topic. The lab presentation is related to the particular lab exercise. Students choose topics and dates of their presentations at the beginning of the course. There is also a computational project, usually due by the end of a semester. The final written exam is compulsory.
English
1. Introduction to computational cognitive neuroscience. Main concepts in modeling.
2. Spiking neurons models. Biology of individual neuron and its implementation in Emergent.
3. Structure of cortical networks, localist and distributed representations, excitation and inhibion of neurons.
4. Biological mechanism of memory and learning, long-term potentiation and depression of synaptic efficacy.
5. Self-organization, error-driven learning, combination of both.
6. Functional organization of the brain. Overview of brain areas.
7. Visual perception, attention, bottom-up and top-down mechanisms. Spatial neglect.
8. Motor control and reinforcement learning.
9. Memory, memory types, memory phenomena.
10. Language: neurobiology, syntax, semantics, modeling.
11. Executive functions, the role of prefrontal cortex.
12. Agency, theory of mind, self-awareness.
http://dai.fmph.uniba.sk/courses/CCN/
Each of the 2 presentation is worth max. 20%, a project is worth 20% max, total 60%. Final written exam 40% max. Evaluation scale: A 91 – 100 %, B 81 – 90 %, C 73 – 80 %, D 66 – 72 %, E 60 – 65 %, Fx < 60%
After passing this course, students will be familiar with the main theories and approaches of Computational cognitive neuroscience. They will gain an insight of how cognitive processes are affected and controlled by neural circuits in the brain. Students will become familiar with modeling of some basic mechanisms of cognitive functions using the Emergent simulator.
Biological Imaging and Sensing / T. Furukawa (Ed.). Berlin : Springer, 2004
Grading: 91-100% = A, 81-90% = B, 71-80% = C, 61-70% = D, 51-60% = E, else Fx.
Active participation during the semester: presentations (30%), quick quizzes (10%), EEG practice (10%)
Final exam: written test (50%)
English
An overview of examination and imaging methods – their temporal and spatial characteristics. Basics of electroencephalography (EEG). Physical and physiological principles of EEG. Basics of EEG signal analysis. Artifacts in EEG. EEG spectral analysis. Evoked and event-related potentials. Principles of computed tomography (CT), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), transcranial magnetic stimulation (TMS) and other modern methods used in the study of brain structure and function. The course will also include a brief introduction to other methods that do not directly measure brain activity, but rather its peripheral manifestations. These include electrooculography (EOG), electrodermal activity (EDA) or skin conductivity (SCL), electromyography (EMG), electrocardiography (ECG), respiratory rate and more.
http://dai.fmph.uniba.sk/courses/mmsm/
Pomer hodnotenia za semester voči skúške: 50/50
Students will become familiar with the basic imaging and other research methods used in modern cognitive neuroscience and understand the basic principles of use of these methods. They will get acquainted with the possibilities and suitability of their use, as well as with the advantages and disadvantages of each particular method. Theoretical knowledge will be supported by practical demonstrations of recording and analyzing brain activity (especially by using EEG).
Minsky, M.: Society of Mind, 1986
Brooks, R.: Cambrian Intelligence, 1999
Arkin, R.: Behavior-Based Robotics (Intelligent Robotics and Autonomous Agents), 2000
Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (Intelligent Robotics and Autonomous Agents series), 2004
Vernon, D.: A Roadmap for Cognitive Development in Humanoid Robots (Cognitive Systems Monographs), 2014
Davies, E.: Computer Vision 5th Edition. Principles, Algorithms, Applications, Learning, 2018
basic programming skills in Python
activity during the semester (project), final written test
English, Slovak
1. Action. Actuators. The robot gear control: via position, via speed. The robot arm control. Forward and inverse kinematics.
2. Perception. Sensors. The basic processing of sensor data: distance, camera image, depth map, tactile map.
3. Control. Decomposition of the control system by function and by activity. Significance of real time.
4. The regular objects recognition. Hough transform.
5. The irregular objects recognition: DOT/HOG, SIFT/SURF/ORB, phase correlation. Representation of objects in robot memory. Sensory-motor approach to perception.
6. Behavioral robotics. Emergence of control in modular control architecture.
7. Cognitive approach to robot control. GOFAI, planning. STRIPS. Sussman’s anomaly. Frame problem.
8. Post-cognitive approach to robot control. Dreyfus‘ criticism of GOFAI. Brooks‘ subsumption architecture. Situated robots. Embodiment. Interaction principle (robot ACE).
9. Minsky‘ society model of mind. Inspiration from Piaget‘s developmental psychology. Dennet‘s mind types. Robot COG.
10. Intelligence as a social phenomenon in group of robots.
11. Robots recognizing and emulating emotions. Robot KISMET.
12. Cloud technology for robots. Robot Pepper. IBM Watson. MicroSoft Azure.
13. Control emerging from interaction of robot with its environment. Delayed reinforcement learning, Genetic programing, Neural network training.
http://www.agentspace.org/kv/
50:50
After competing the course, students will be familiar with the basic concepts of robotics and will have acquired practical skills on how to control the robot in case of simple tasks, either in a simulated environment or with a physical robot. In addition, students will also get the basics of philosophical background of cognitive robotics (cognitivism versus post-cognitivism, empirism versus rationalism).
Carruthers, P., & Smith, P. K. (Eds.). (1996). Theories of theories of mind. Cambridge University Press.
Goldman, A. I. (2006). Simulating minds: The philosophy, psychology, and neuroscience of mindreading. Oxford University Press.
Dennett, D. C. (1989). The intentional stance. MIT press.
Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. MIT press.
Banaji, M. R., & Gelman, S. A. (Eds.). (2013). Navigating the social world: What infants, children, and other species can teach us. Oxford University Press.
Activity during the seminars
Presentation
Final essay
English
Social cognition in animals and humans.
Intentional stance (D. Dennett).
Shared intentionality. We-mode. Joint action.
Modularity of mind and social cognition.
Theory of mind – „classical“(theory theory, simulation theory) and new trends in the research of mindreading (4E cognition, predictive processing).
Mirror neurons system.
Theory of mind in the brain.
Mindreading and autism.
Social cognition and cultural transmission.
Social cognition and the origins of morality.
Activity during the seminars
Presentation
Final essay
Grading: 91-100% = A, 81-90% = B, 71-80% = C, 61-70% = D, 51-60% = E, else Fx
The course will provide an opportunity to acquire knowledge of some of the main topics and problems in the research of social cognition, especially theory of mind.
The focus of the course will be on the analysis of relevant theoretical and experimental papers.
The course will have an interdisciplinary character – we will discuss influential studies in comparative psychology, developmental psychology, philosophy and other related disciplines.
None.
Active participation in lectures (50%), writing two reflections (50%).
The seminar consists of a series of lectures by experts, on cognitive science related research topics. The students choose two topics for which they write a critical reflection.
http://cogsci.fmph.uniba.sk/seminar/
Grading: 91-100% = A, 81-90% = B, 71-80% = C, 61-70% = D, 51-60% = E, else Fx.
After the course, the students will have acquired knowledge about cognitive science related research topics in our area, which could help them decide for a topic for their semester project (2nd semester), and/or master thesis (4th semester).
Kováč L. (2015) Closing Human Evolution/Springer, https://goo.gl/Wo2ZRg
Kováč L. (2000) Fundamental principles of cognitive biology. Evolution and Cognition, 6, 51-69
Baluška F., Mansuso S. (2009) Deep evolutionary origins of neurobiology. Communicative & Integrative Biology, :1, 60-65
other research papers
Three assignments, each for max. 25 points (minimum 10 required), active involvement during course / discussion, max. 25 points (min. 20 required). Course total = 100 points (min. 51 required)
English
1. A Brief History of Cognitive Biology
2. The Underlying Principles of Cognitive Biology:
3. The Basal Level of Cognition: Molecular Mechanisms, Concept of Information; Biosemiotics
4. The Medial Level of Cognition: Cellular Communication, Neural Networks;
5. The Apical Level of Cognition: Organismal Behavior; “Rationality”; Goal-directedness; Emotions; Mechanisms for Learning & Memory; Brain Modularity;
6. The Ontic Level of Cognition: Developmental Processes I: Developmental Genetics; Gene Regulatory Networks; Epigenetic Landscape;
7. The Ontic Level of Cognition: Developmental Processes II: Generative Entrenchment & Ratchet Effects; Developmental Constraints; Hierarchical Processing
8. The Supra-Individual Level of Cognition: From Eliminative Reductionism to Organicism; Social Cognition; Evolution of Complex Systems;
9. Resumé & Outlook
https://dai.fmph.uniba.sk/w/Course:Cognitive_biology/en
75:25
This course provides a comprehensive overview of principles of cognition, seen as a natural biological phenomenon. The main objective is the formulation of substantiated interrelation of cognition and evolution. The interdisciplinarity of research in cognitive biology requires students to think multi-disciplinary and on multiple scales.
- S. Russell: Human compatible. Artificial intelligence and the problem of control. Viking, 2019.
- J. Havens: Heartificial intelligence. Embracing our humanity to maximize machines. Penguin, 2016.
- P. Boddington: Towards a code of ethics for artificial intelligence. Springer, 2017.
- M. Shanahan: The technological singularity. MIT Press, 2015.
- C. MacKellar, C.: Cyborg Mind: What Brain–Computer and Mind–Cyberspace Interfaces Mean for Cyberneuroethics. Berghahn Books, 2019.
- G. Bel, J. Gemmell: Total Recall, How the e-Memory Revolution will change everything. Dutton, 2009.
- S. Zuboff: The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs, 2019.
- C. O'Neil: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishers, 2016.
- M. Tegmark: Life 3.0. Allen Lane, 2017.
Semestral evaluation: active participation
Final evaluation: essay
Weight of the final evaluation: 60%
To achieve an A, 90% is needed, for B at least 80%, for C 70%, for D, 60% and for an E, at least 50% of overall assessment.
English
Big data: privacy, politics and power,
Internet of things, it usefulness and threats,
Assistant AI and its place in future society,
Job market and inequality,
Enhancements and human rights and the right to change self and others,
Initiatives for responsible research,
Artificial minds,
Hybridization between species and between AI and organic minds,
Future of minds and trans-humanism,
Artificial emotional intelligence,
An after human era.
http://dai.fmph.uniba.sk/courses/STH/
The students will gain awareness of the contemporary and potential future challenges posed by scientific and technological innovations and their impact on human behaviour, culture and society.
Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Work on exercises (50%)
Exam: test (50%)
Grade A: 90% , B: 80% , C 70%, D 60% and grade E at minimum 50%
Slovak, English
1. Introduction to reinforcement learning
2. Markov decision process
3. Dynamic programming
4. Monte Carlo methods
5. Temporal-Difference learning
6. Exploration and exploitation
7. Value function approximation
8. Policy gradient methods
9. Actor-critic methods
10. Deep reinforcement learning
The graduate will gain an overview of the methods and techniques used in reinforcement learning. Will gain practical experience with analysis, design and implementation of reinforcement learning problems.
Atran, S. (1993) Cognitive Foundations of Natural History: Towards an Anthropology of Science, Cambridge University Press, Cambridge.
D'Andrade, R. (1995) The Development of Cognitive Anthropology, Cambridge University Press, Cambridge.
Bloch, M. (2012) Anthropology and the Cognitive Challenge, London School of Economics, London
Jebelli, J. (2022) How the Mind Changed: A Human History of Our Evolving Brain, Little, Brown Spark
Dengah, F., Snodgrass, J., Polzer, E., Nixon, W., (2020) Systematic Methods for Analyzing Culture: A Practical Guide, Routledge.
N/A
Semester evaluation - weekly activities
Final evaluation - essay or project
english
Class syllabus:
1. Introduction to Anthropology Definition and scope of Anthropology, Brief history of Anthropology, Main branches of Anthropology, Introduction to Cognitive Anthropology
2. Evolution of Anthropological Methods Early anthropological methods, Participant observation and ethnography, Structured and unstructured Interviews.
3. Quantitative and Qualitative Methods in Anthropology Differences in the use of quantitative and qualitative methods, Statistical analysis in Anthropology, Case studies and narrative analysis.
4. Contemporary Anthropological Methods Use of technical tools in anthropological research, Digital ethnography and online research methods, Visual Anthropology.
5. Ethical Aspects in Anthropological Research Credibility and transparency in research, Ethical dilemmas in fieldwork, The role of the anthropologist: objectivity and engagement.
6. The Future of Anthropological Methods Interdisciplinary approaches in Anthropology, The role of big data in anthropological research.
7. Differences in Cognitive Anthropology Definition and scope of Cognitive Anthropology, Brief history and reasons for development, Key concepts in Cognitive Anthropology.
8. Culture and Cognition The concept of culture in Cognitive Anthropology, the influence of culture on cognition, Case studies of differences between cultures (innate and learned).
9. Language and Cognition The relationship between language and thought, Linguistic relativity and determinism, The role of language in shaping social cognition.
10. Cognitive Anthropology and Theories of Evolution The evolution of human cognition, Cognitive adaptations and human evolution, The role of culture in human evolution, Dual inheritance theory.
11. Cognitive Anthropology and Technology Culture and technology, Technology as an independent component of culture influencing human development.
12. The Future of Cognitive Approaches to the Study of Cultures Beyond relativism - new trends in Cognitive Anthropology, Cognitive Anthropology and artificial intelligence, Interdisciplinary approaches in Cognitive Anthropology.
https://dai.fmph.uniba.sk/w/Cognitive_Anthropology/en
30 % / 70 %
The course aims to provide students an introductory understanding of cognitive anthropology, its methods, and its relevance in fields of Cognitive science. Through a combination of theoretical knowledge and case studies, students will gain insights into the researched processes that shape human cultures and societies.
The course is designed to provide students with systematic knowledge in the field of cognitive anthropology. It focuses on the historical development of tools and methods, key theories, and methodological approaches relevant to the discipline. Students will engage with the interdisciplinary relationships of cognitive anthropology with other scientific fields such as psychology, linguistics, and neuroscience.
Papers will be provided on the course webpage.
During semester: reading assignments, discussions in the class, discussion moderation, peer activity (80%)
Final: written integration reflection (20%)
English
The goal of the course is to formulate questions and provide views from literature helping to understand how cognitive systems manipulate with meanings. Also, to present cognitive theories of representation bridging the gaps between brain, language and culture.
http://dai.fmph.uniba.sk/courses/CSCTR/
80%/20%
Students will gain knowledge of existing theories and conceptual apparatus for analysing meaning and understanding in animals, humans and artificial systems.
various journal papers related to individual topics
NA
weekly activity during the semester, paper presentation and discussion moderation, final paper presentation and writing
English
1. Introduction to language and concepts
2. Towards embodied cognition
3. Mirror neuron system and its role(s) in cognition
4. Common coding theory, motor simulation, mental simulation
5. Language as action
6. Conceptual and linguistic systems - two theories
7. Meaning as statistical covariation
8. Symbol grounding problem
9. Unification attempts
10. Role(s) of language in cognition and thought
11. Grounding abstract concepts.
12. Summary and reflection.
https://dai.fmph.uniba.sk/w/Course:Grounded_cognition
70/30
The course objective is to provide students with deeper insight into up-to-date research trends in cognitive science, from the perspective of various disciplines (psychological, neural and computational). The course focus is on grounded (embodied) cognition, and its relation to language. The course should also help students in their ability to interpret scientific papers, to formulate, present and defend ideas. The course is primarily taken by mobility students in cognitive science.
Arulkumaran K., Deisenroth M.P.,Brundage M., Bharath A.A. (2017) Brief Survey of Deep Reinforcement Learning. IEEE Signal Processing Magazine, https://arxiv.org/pdf/1708.05866.pdf
DeepLearning.AI (2023). A Complete Guide to Natural Language Processing. https://www.deeplearning.ai/resources/natural-language-processing/
Huang S. et al. (2023): Language Is Not All You Need: Aligning Perception with Language Models. https://arxiv.org/pdf/2302.14045.pdf
Korteling J.E. et al. (2021) Human- versus Artificial Intelligence. Frontiers in Artificial Intelligence, 4, https://www.frontiersin.org/articles/10.3389/frai.2021.622364
Lindsay G.W. (2020). Attention in Psychology, Neuroscience, and Machine Learning. Frontiers in Computational Neuroscience, 14, https://doi.org/10.3389/fncom.2020.00029
Lindsay G.W. (2021) Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future. Journal of Cognitive Neuroscience, 33(10): 2017–2031, https://doi.org/10.1162/jocn_a_01544
Garnelo, M. and Shanahan, M. (2019): Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Current Opinion in Behavioral Sciences, 29(17), 2352-1546, https://doi.org/10.1016/j.cobeha.2018.12.010
Perconti P., Plebe A. (2020) Deep learning and cognitive science. Cognition, 203, https://doi.org/10.1016/j.cognition.2020.104365
Silver, D., Huang, A., Maddison, C. et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484–489, https://doi.org/10.1038/nature16961
Sünderhauf N, Brock O, Scheirer W, et al. (2018) The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4-5), 405-420, https://doi.org/10.1177/0278364918770733
Zhu Y. et al. (2020): Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense. Engineering, 6(3), pp. 310-345, https://doi.org/10.1016/j.eng.2020.01.011.
weekly activity during the semester, paper presentation and discussion moderation, final group presentation
English
Introduction to human intelligence and artificial intelligence
Brief introduction to math needed for DL
Current hype in deep learning
Deep learning in computer vision
Deep learning in natural language processing
Deep learning for robotics
Deep reinforcement learning - how to act
Attention in psychology, neuroscience and machine learning
Multimodal models
Going beyond deep learning
Toward cognitive AI
Integrative summary
https://dai.fmph.uniba.sk/w/Course:Deep_learning_and_Cognition
80/20
The course introduces the field of a highly popular machine learning approach focused on deep learning in artificial neural networks. Aiming at master’s students with diverse backgrounds of bachelor’s studies (such as students of cognitive science), the course will guide them through different areas of DL, to highlight its successful applications. To better understand the mechanistic principles of DL models, the students will learn the basics about the underlying mathematical concepts of DL and will be shown a few simpler examples of functioning neural network models. Throughout the course, the discussions will also focus on virtues and vice of deep learning and its relation to human cognition.
Sumbitting the master's thesis concept, oral presentations in the seminar (shorter and longer),
peer activity during the semester, participation in peer reviews of extended abstracts (for the conference), active participation at MEi:CogSci conference (talk).
Introduction to the course, requirements and grading, plan for the semester.
Presentations of the student's mobility projects to 1st year students (joint meeting).
Student's short presentations (10 min.). Master Thesis Concept requirement.
Student's short presentations (10 min.).
Long presentations (several students), feedback
Long presentations (several students), feedback
Long presentations (several students), feedback
Interdisciplinarity - requirement for your master thesis.
Rehearsal of conference talks
Presentations (talks) at MEi:CogSci conference (during exam period)
https://dai.fmph.uniba.sk/w/Course:CogSci_Diploma_Thesis_Seminar/en
40% - quality of the master thesis concept
30% - oral presentations about the thesis in the seminar (shorter and longer)
20% - activity during the semester, peer feedback, participation in peer reviews of extended abstracts (for the conference)
10% - active participation at MEi:CogSci conference (talk)
After the course, you should be able to: (1) to formulate and follow a scientific question relevant to cognitive science, (2) to plan, conduct, document and present scientific work, (3) to write an extended scientific abstract, (4) to defend your research and constructively deal with critical commentary, (5) to constructively participate in a peer-review process, (6) to get involved in collaborative work in physical and virtual environments, (7) to engage in scientific discourse, (8) to communicate your expertise in order to contribute constructive criticism to the work of others.
Study materials from relevant courses of the program curriculum and various sources to individuals questions, will be provided at course website.
Perception
Attention
Memory
Language
Mental representations
Learning
Embodiment
Emotions
Social cognition
Consciousness
The course lists a number of cognitive phenomena, that can be looked from the perspective of psychology, computation and neuroscience.
https://dai.fmph.uniba.sk/w/Course:Cognitive_phenomena
The student will become familiar with connections between pieces of knowledge within individual disciplines in the context of selected cognitive phenomena.
Kortenkamp, Bonasso, Murphy, Artificial Intelligence and Mobile Robots, MIT Press, 1998
Dudek, Jenkin: Computational Principles of Mobile Robotics, Cambridge Univ. Press, 2000
Corrochano, Geometric Computing for Perception Action Systems, Springer, 2001
Arkin, Behavior-Based Robotics, MIT Press, 2000
Tomasi, Mathematical Methods for Robotics and Vision, Stanford University, 2000
Nehmzow, Scientific Methods in Mobile Robotics, Springer, 2006.
Additional papers upon need
Evaluation during semester: reports or projects (60%), active participation (40%)
Approximate evaluation scale: A 90%, B 80%, C 70%, D 60%, E 50%
Seminar topics will cover: robotic control architectures, probabilistic robotics, multi-robotic systems, evolutionary robotics, sensor systems and actuators, educational robotics, robotic competitions, entertainment robotics, servis robotics, embedded systems.
https://kempelen.dai.fmph.uniba.sk/rg/
100/0
A student will understand control and programming of robots, processing signals from sensors, he or she will experience preparation and realization of a individual or group project with robotic technologies in a lab with the support of the teacher.
1 – Sterratt D, Graham B, Gillies A and Willshaw D (2011) Principles of Computational Modelling in Neuroscience. Cambridge University Press, Cambridge, U.K. (http://www.biologia.buap.mx/ANTOLOGIA%20BIOFISICA%20I.pdf)
2 – SCHOLARPEDIA - the free online encyclopedia of computational neuroscience (http://www.scholarpedia.org/article/Encyclopedia_of_computational_neuroscience )
3 – Gillies A and Sterratt D (2012) NEURON Tutorial – available online (http://web.mit.edu/neuron_v7.4/nrntuthtml/index.html )
Work on home assignments and sitting the final written exam. Students are required to achieve at least half of marks from home assignments during the semester in order to be admitted to sit the final exam. The final exam is compulsory.
English, Slovak
minimal number of enrolled students = 4
- Principles of mathematical and computational modeling of dynamic systems
- Theory of action potentials in neurons and their implementation in NEURON
- Theory of signal processing in dendrites and implementation of dendrites in NEURON
- Theory of biological neural networks and connecting the neurons in NEURON
- Theory of synaptic transmission and implementation of synapses in NEURON
- Implementation of ion channels in NEURON
- Theories of coding of information in neural networks
http://dai.fmph.uniba.sk/courses/comp-neuro/
Home assignments 50%, final written exam 50%.
Grading: A 91-100%, B 81-90%, C 71-80%, D 61-70%, E 51-60%, Fx < 51%
The course will provide students with the basics of computational neuroscience including the basics of the programming metalanguage of the software NEURON, which is used e.g. in the Blue Brain project. The students will learn theoretical and practical principles of application of informatics to the study of processes in neurons and small neural networks. At the same time, they will become familiar with the way of critical thinking, reasoning and problem solving in this research area.
Speech and Language Processing, 2nd Edition / Daniel Jurafsky, James H Martin. Upper Saddle River : Prentice Hall, 2008
English
(1) Text Processing.
(2) Language Modeling (n-grams), Spelling Correction.
(3) Text Classification (Naive Bayes), Sentiment Analysis.
(4) Named Entity Recognition (HMM, MaxEnt), Relation Extraction.
(5) POS Tagging, Parsing.
(6) Information Retrieval.
(7) Meaning Extraction, Question Answering.
http://dai.fmph.uniba.sk/courses/nlp/
The students will acquire knowledge and practical experience in the field of natural language processing. They will know how to effectively apply the underlying theory from probability, statistics, computational linguistics, and machine learning, to perform tasks involving unstructured text, such as spelling correction, text generation, sentiment analysis, information extraction, and question answering.
http://cogsci.fmph.uniba.sk/seminar/
Basic practicing and improvement of locomotion skills in various selected sports: aerobics, basketball, volleyball, football, floorball, hockey, badminton, tennis, table tennis, swimming, body building, trampoline jumping, paddling. Development of a harmonic personality in the sense of “kalokagatia” – to cultivate the students´ mental and physical values.
Slovak, English
Practicing of the students´ game skills in collective sports: basketball, volleyball, football, floorball and hockey. Mastering of the basic technique of a particular sport discipline in other sports. In paddling, basic training on still and slightly flowing water. Development of coordination skills, improvement of articular mobility and cardiovascular system.
100/0
Grades: A 90%, B 80%, C 70%, D 60%, E 50%
The condition for the award of 1 or 2 credits is the completion of a multi-day course in its full scope, or the completion of one-day courses in the scope of 4 days. Candidates can apply to the leaders of individual courses. From the presented offer of courses, you can choose the one that suits your interests, abilities and deadlines.
Slovak
KTVŠ does not rent ski equipment.
The student can sign up for the outdoor sports courses offered by the department: skiing, snowboarding. The lessons in the courses are focused on the development of basic and special movement skills and mastering the techniques needed for the sports.
Acquisition and development of basic motor skills and abilities in selected sports: skiing and snowboarding. Mastering the correct technique of performing individual movements, which are necessary for skiing and snowboarding.
Grades: A 90%, B 80%, C 70%, D 60%, E 50%.
The condition for the award of 1 or 2 credits is the completion of a multi-day course in its full scope, or the completion of one-day courses in the scope of 4 days. Candidates can apply to the leaders of individual courses. From the presented offer of courses, you can choose the one that suits your interests, abilities and deadlines.
Slovak
KTVŠ will provide sports equipment.
The student can sign up for the outdoor sports courses offered by the department: water tourism - river rafting, windsurfing, beach volleyball, hiking and other hobby sports. The lessons in the courses are focused on the development of basic and special movement skills and, mastering the techniques needed for the sports.
Creating a positive and lasting relationship with physical activity. Acquisition and mastery of basic motor skills and abilities in outdoor sports: windsurfing, beach volleyball, water tourism - river rafting, hiking and other sports according to interest. Training and improving the technique needed for the sports.
Improvement training in a selected sport aimed at enhancing locomotion skills in the selected sport. To improve the students´ resistance against various vices, stresses and risk factors.
Slovak, English
Practicing of offensive and defensive game combinations and playing with modified rules in collective sports such as basketball, volleyball, football, floorball, hockey. Command of elements of higher difficulty in locomotion skills (swimming - crawl stroke, breast stroke, butterfly stroke, trampoline jumping and aerobics – practicing of areobics compositions, bodybuilding – development of the main muscle groups, paddling on running water. Testing of the level of physical fitness and coordination skills.
100/0
Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in faculty library.
Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
Passing the online course https://course.elementsofai.com/ (in Enlish or Slovak version).
Slovak or English
The course consists of 20 numerical and 5 text-based tasks. Numerical tasks are checked automatically, text-based tasks are evaluated anonymously by students.
1. What is artificial intelligence: related areas, AI philosophy.
2. Troubleshooting and UI: Browsing and troubleshooting, browsing and games
3. Probability and chance, Bayes' theorem, naive Bayesian classification.
4. Machine learning: nearest neighbor classifier, regression.
5. Neural networks: basics, creation, modern techniques.
6. Consequences: on predicting the future, the effects of AI on society, summary.
The student will get acquainted with selected basic concepts of artificial intelligence and their use in solving various practical tasks.
Russell S., Norwig P. (2010). Artificial Intelligence: A Modern Approach, (3rd ed.), Prentice Hall. Available in faculty library.
Marsland S. (2015). Machine Learning: An Algorithmic Perspective, (2nd ed.), CRC Press.
Passing the online course https://course.elementsofai.com/ (in Enlish or Slovak version).
Slovak or English
The course consists of 20 numerical and 5 text-based tasks. Numerical tasks are checked automatically, text-based tasks are evaluated anonymously by students.
1. What is artificial intelligence: related areas, AI philosophy.
2. Troubleshooting and UI: Browsing and troubleshooting, browsing and games
3. Probability and chance, Bayes' theorem, naive Bayesian classification.
4. Machine learning: nearest neighbor classifier, regression.
5. Neural networks: basics, creation, modern techniques.
6. Consequences: on predicting the future, the effects of AI on society, summary.
The student will get acquainted with selected basic concepts of artificial intelligence and their use in solving various practical tasks.
- Teachers' own electronic study materials published on the course website or in the Moodle system
- Gavora, Peter a kol. 2010. Elektronická učebnica pedagogického výskumu. [online]. Bratislava : Univerzita Komenského, 2010. Dostupné na: http://www.e-metodologia.fedu.uniba.sk/ ISBN 978–80–223–2951–4.
- Tharenou, P., Donohue, R. and Cooper, B., 2007. Management research methods. Cambridge University Press.
- Topping, A., 2015: The Quantitative-Qualitative Continium. In: Gerrish, K. and Lathlean, J., The Research Process in Nursing, p. 159-172
- Williamson, K. and Johanson, G. eds., 2017. Research methods: Information, systems, and contexts. Chandos Publishing.
Continuous assessment: active participation in research in an international student team (25%), presentation of work in a workshop (25%), scientific article (50%)
Indicative evaluation scale: A 90 %, B 80 %, C 70 %, D 60 %, E 50 %
English
- Research methodology
- Design and implementation of a research project in an international group (preferably interdisciplinary)
- Methods and tools for collaboration in virtual space, collaboration in science and practice
- Academic writing, presentation of research results through scientific articles; objectives, content and structure of scientific articles; forms of academic publication, publication forums and evaluation of their quality
- Quality assurance and feedback - peer review
- Communication of results through posters or conference presentations
100/0
Students will learn in the team to agree on a common research topic, formulate research questions, determine research methods for the problem, collect and evaluate data, discuss their findings, present research results to the professional public, analyze and evaluate the scientific work of their colleagues, prepare a scientific article suitable for publication
2/1 (lecture / individual work)
Clark, Timothy R., et al. Business Model Generation. Wiley, 2010
The condition for admission to the exam is active participation in at least 80% of the lessons.
The final assessment consists of a presentation of the semester project. To successfully complete the course, it is necessary to achieve at least 50% of the overall score.
Slovak
1. Commercialization of scientific research.
2. Fundamentals of entrepreneurship and startup terminology.
3. Identification of problems and customer needs analysis (design thinking).
4. Technology transfer. Technology Readiness Levels (TRL).
5. Intellectual property and its protection.
6. Market, customer, and market potential of a technological solution.
7. Business Model Canvas. Revenue models.
8. Sources of financing for technological projects.
9. Pitching and communication of the solution.
10. Fundamentals of management and leadership.
11. Innovation support and incubation structures at national and international levels.
0/100
After completing the course, students can describe the possibilities for commercialization of scientific and technological research. They can identify market needs, assess the market potential of a technological solution, and are familiar with the terminology of entrepreneurship, technology transfer, and intellectual property protection. They understand the overall structure of a business plan and the main forms of financing for technological projects. They are familiar with the principles of communication, teamwork, and team leadership and can apply them appropriately in project work and its presentation.
To improve the students´ physical fitness, health and sport performance through sport.
Slovak, English
To improve offensive and defensive game combinations in collective sports. Practicing of tactical and technical elements in individual sports. Compensatory exercises to correct wrong body posture. Stretching. Competition rules in sport disciplines.
100/0
To strengthen the procedural aspect of university sport, stressing its health and psychological importance. To improve the students´ physical fitness.
Slovak, English
Sport training for Faculty Championships in a selected sport with modified rules. Selection of sport-talented students into teams of the Faculty Sport League, University League of Bratislava Faculties, and participation in sport events of the Faculty and University.
100/0