Introduction to artificial intelligence

  • Course teacher Kristina Šekrst
  • 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

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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)

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

Student responsibilities

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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.