Course teacher

Associate teachers

-

ECTS credits

5

Number of hours: Lectures + Seminars + Exercises

45 / 0 / 15

Course objectives

The course aims to equip students to explain the principal historical and conceptual approaches to artificial intelligence; describe its formal foundations, including logical inference, computational complexity, and information theory; compare the symbolic, connectionist, and probabilistic paradigms; explain the main methods of machine learning, reinforcement learning, and deep learning, including transformer architectures and large language models; and critically evaluate the cognitive capacities, interpretability, and ethical and social consequences of artificial systems.

Enrolment requirements and/or entry competences required for the course

-

Learning outcomes at the level of the programme to which the course contributes

• Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
• Apply specific knowledge and skills from selected disciplines constituting cognitive science.
• Employ diverse disciplinary tools in exploring and describing the nature of cognitive processes.
• Apply AI tools in concrete tasks and practical contexts.

Course content (syllabus)

• Artificial intelligence, mind, and consciousness. Searle's Chinese Room, the Turing Test, mental state attribution.
• Logic and complexity. Propositional and predicate logic, knowledge representation, computational complexity (P/NP).
• Cybernetics and information theory. Control, feedback, Shannon's theorems, entropy.
• The symbolic paradigm. Physical symbol systems, problem-solving, expert systems, the frame problem.
• The connectionist paradigm. Neural networks, the perceptron, backpropagation, the XOR problem.
• The probabilistic paradigm. Bayesian inference, generative models, perception.
• Machine learning: supervised and unsupervised. Classification, regression, clustering, overfitting.
• Reinforcement learning and decision-making. Markov decision processes, Q-learning, reward models, exploration versus exploitation.
• Deep neural networks. Feedforward networks, convolutional and recurrent networks, autoencoders.
• Transformers: architecture and foundations. Embeddings, attention, positional encoding, residual connections.
• Large language models and generative AI. Pretraining, fine-tuning, RLHF, in-context learning, hallucinations.
• Retrieval, memory, and agentic AI. Retrieval-augmented generation, vector databases, agents, production systems.
• Explainability, interpretability, and machine reasoning. Mechanistic interpretability, chain-of-thought, faithfulness.
• AI ethics and the social role of AI. Bias, fairness, alignment, privacy, labor concerns.
• Artificial consciousness and final remarks. Mental state attribution, moral status.

Student responsibilities

-

Required literature

• Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach (4th ed.), Pearson, 2021. (selected chapters)
• Margaret A. Boden, AI: Its Nature and Future, Oxford University Press, 2016. (selected chapters)
• Kristina Šekrst, The Illusion Engine: The Quest for Machine Consciousness, Springer, 2025. (selected chapters)
• Selected chapters of weekly readings.

Optional literature

• Alan M. Turing, Computing machinery and intelligence, Mind, 59(236), 1950, 433–460.
• John R. Searle, Minds, brains, and programs, Behavioral and Brain Sciences, 3(3), 1980, 417–424.
• Scott Aaronson, Why philosophers should care about computational complexity, in B. J. Copeland et al. (Eds.), Computability: Turing, Gödel, Church, and Beyond, MIT Press, 2013.
• Iris van Rooij, The tractable cognition thesis, Cognitive Science, 32(6), 2008, 939–984.
• Norbert Wiener, Cybernetics: Or Control and Communication in the Animal and the Machine, MIT Press, 1948.
• Claude E. Shannon, A mathematical theory of communication, Bell System Technical Journal, 27, 1948, 379–423.
• Allen Newell, Herbert A. Simon, Computer science as empirical inquiry: Symbols and search, Communications of the ACM, 19(3), 1976, 113–126.
• Daniel C. Dennett, Cognitive wheels: The frame problem of AI, in C. Hookway (Ed.), Minds, Machines and Evolution, Cambridge University Press, 1984.
• John McCarthy, Patrick J. Hayes, Some philosophical problems from the standpoint of artificial intelligence, in B. L. Webber, N. J. Nilsson (Eds.), Readings in Artificial Intelligence, Morgan Kaufmann, 1969, 431–450.
• Warren S. McCulloch, Walter Pitts, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5, 1943, 115–133.
• David Rumelhart, Geoffrey Hinton, Ronald Williams, Learning representations by back-propagating errors, Nature, 323, 1986, 533–536.
• Jerry A. Fodor, Zenon W. Pylyshyn, Connectionism and cognitive architecture: A critical analysis, Cognition, 28(1–2), 1988, 3–71.
• Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, Noah D. Goodman, How to grow a mind: Statistics, structure, and abstraction, Science, 331(6022), 2011, 1279–1285.
• Nick Chater, Joshua B. Tenenbaum, Alan Yuille, Probabilistic models of cognition: Conceptual foundations, Trends in Cognitive Sciences, 10(7), 2006, 287–291.
• Michael I. Jordan, Tom M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science, 349(6245), 2015, 255–260.
• Richard S. Sutton, Andrew G. Barto, Reinforcement Learning: An Introduction (2nd ed.), MIT Press, 2018.
• Yann LeCun, Yoshua Bengio, Geoffrey Hinton, Deep learning, Nature, 521(7553), 2015, 436–444.
• Sandro Skansi, Kristina Šekrst, Introduction to Deep Learning: Neural Networks, Large Language Models and Agentic AI, Springer, 2026.
• Ashish Vaswani, et al., Attention is all you need, NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, 6000–6010.
• Matthew Botvinick, et al., Reinforcement learning, fast and slow, Trends in Cognitive Sciences, 23(5), 2019, 408–422.
• Tom B. Brown, et al., Language models are few-shot learners, NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020, 1877–1901.
• Patrick Lewis, et al., Retrieval-augmented generation for knowledge-intensive NLP tasks, NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, 2020, 9459–9474.
• Jason Wei, et al., Chain-of-thought prompting elicits reasoning in large language models, NIPS'22: Proceedings of the 36th International Conference on Neural Information Processing Systems, 2022, 24824–24837.
• Luca Longo, et al., Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions, Information Fusion, 106, 2024, 102301.
• Jack Lindsey, et al., On the biology of a large language model, Transformer Circuits Thread, 2025.
• David J. Chalmers, Could a large language model be conscious?, Boston Review, 2023.
• Vincent C. Müller, Ethics of artificial intelligence and robotics, in E. N. Zalta, U. Nodelman (Eds.), The Stanford Encyclopedia of Philosophy (Summer 2026 ed.).
• Anil K. Seth, Conscious artificial intelligence and biological naturalism, Behavioral and Brain Sciences, 2025, 1–42.