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Artificial Intelligence (AI)

This guide to Artificial Intelligence (AI) is designed as a starting point for your study and research. It provides an introductory overview of useful resources in all AI-related disciplines, such as Machine Learning (ML), Neural Networks (NN), Deep Learning (DL), and other relevant areas of interest. Here you can find links to a large number of printed and electronic books and journals available through NTK, supplemented by other AI-related online resources.

Interested in academic uses of AI? Go straight to our research guide on AI tools for research. Wish to discuss a specific AI-related issue? Schedule a free individual consultation with one of our specialists.

Main Types of Resources on AI

This subject guide will help you find information on artificial intelligence from different perspectives and through different channels. You can use it to

  • locate books, e-books and journal articles on AI available through NTK,
  • search relevant databases for further resources,
  • connect to other useful websites related not just to various AI materials but also experts prominent in the field. 

The book cover images below are intended to illustrate different facets of the AI domain. Click on them to find these books in the NTK catalogue:

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AI News

Newly Arrived Journal Issues in AI

Most recent print journals are available in our Periodicals Reading Room (third floor, to the right of the elevator).

AI Associations: An Indicative Overview

Below you will find just several associations active in the AI domain:

Czech

  • CzechAI is a non-profit organization associating everyone who is interested in artificial intelligence (AI) from the perspective of its practical application; or from the perspective of fundamental research of algorithms and approaches.
  • Prague’s International Researchers community (prg.ai) was founded in 2019 by academics from the Czech Technical University, Charles University, and the Czech Academy of Sciences

European

American


General Search

To find print and electronic books, use the following NTK search tools:

  • NTK Discovery Tool – search both print and electronic collections (full-text search)
  • Catalog – print collection, availability, location
  • eBook Search - search by title, ISBN, or author

To find out how to search NTK resources, read our tutorials.

Electronic Books

Print Books

AI books typically have LCC call numbers Q334, Q335, Q336.

To browse AI books sorted by their LCC call number, go to the LCC number link in the table below to find out the exact location/shelf number in NTK:

Call Number-Based Book Locations

Call Number Subject Shelf
Q334 Artificial intelligence: general, social aspects, ethics 6C/125, 127
Q335 Artificial intelligence: learning systems, robotics 6C/132, 6C/133, 6C/134, 
Q336 Artificial intelligence: data and natural languages processing, chatbots, bioinformatics 6C/134

Subject-Based Book Locations

Some Artificial Intelligence books may be classified in relation to other subjects, such as Law, Medicine, Physics, Social Sciences, and others.

To browse AI books by subject area, click on the NTK location link in the table below to find out the exact location/shelf number of those books in NTK:

Domain Subject Location
Artificial intelligence Agriculture NTK
Artificial intelligence Education NTK
Artificial intelligence Fine Arts NTK
Artificial intelligence Geography, Anthropology, Recreation NTK
Artificial intelligence Historical Sciences  NTK
Artificial intelligence Language and Literature NTK
Artificial intelligence Law NTK
Artificial intelligence Library Science NTK
Artificial intelligence Medicine NTK
Artificial intelligence Military Science NTK
Artificial intelligence Music NTK
Artificial intelligence Naval Science  NTK
Artificial intelligence Philosophy, Psychology, Religion NTK
Artificial intelligence Political Science NTK
Artificial intelligence Social Sciences  NTK
Artificial intelligence  Technology NTK

General Search

To find print and electronic articles, use the following NTK search tools:

To find out how to search NTK resources, read our tutorials.

Online Articles

Web Pages with Useful Literature on AI

The following list of freely available web-based books and other resources is intended as an indicative overview to illustrate different aspects of the AI subject area. The suggested level of difficulty is no more than a rough “guesstimate” provided for quick orientation and as a starting point for further research.

Please note: Materials listed here are free to access as of 26 January 2026. If you spot a dysfunctional link, please let us know.

Beginner Level
The Hundred-Page Machine Learning Book by Andriy Burkov
Description: This concise volume presents a structured overview of core machine learning principles, algorithms, and practical considerations. It equips beginners with the conceptual foundation necessary to understand contemporary AI systems and supports preparation for applied roles and technical interviews.
Interpretable Machine Learning by Christoph Molnar
Description: This book systematically examines theoretical and practical methods for interpreting machine learning models. It covers both model-agnostic and model-specific approaches, making it particularly valuable for ensuring transparency, accountability, and trust in AI systems.
Python Data Science Handbook by Jake VanderPlas
Description: This handbook introduces essential Python-based tools for data science, including data manipulation, visualization, and machine learning libraries. It emphasizes practical workflows widely used in research and industry settings.
Machine Learning Yearning by Andrew Ng
Description: This text focuses on the strategic structuring of machine learning projects. It provides methodological guidance on error diagnosis, system improvement, and prioritization of development efforts in applied AI systems.
Artificial Intelligence through Prolog by Neil C. Rowe
Description: This introductory text presents foundational AI concepts through Prolog programming, emphasizing symbolic reasoning and knowledge representation as core components of classical artificial intelligence.
A Brief Introduction to Neural Networks by David Kriesel
Description: This work offers a mathematically grounded introduction to neural network models and learning algorithms, balancing theoretical clarity with accessibility for technically oriented beginners.
Computers and Thought by Mike Sharples et al.
Description: This interdisciplinary introduction examines artificial intelligence from cognitive, philosophical, and social viewpoints, making it accessible to readers without extensive technical backgrounds.

Intermediate Level
Artificial Intelligence: Foundations of Computational Agents by David Poole & Alan Mackworth
Description: This comprehensive text presents AI through the unifying framework of intelligent agents, integrating search, reasoning, learning, and decision-making methodologies.
Mathematics for Machine Learning by Deisenroth, Faisal, Ong
Description: This book develops the mathematical foundations required for modern machine learning, including linear algebra, probability theory, and optimization, while linking theory to algorithmic practice.
The Elements of Statistical Learning by Hastie, Tibshirani, Friedman
Description: This foundational reference book presents statistical learning techniques for data mining, inference, and prediction, with an emphasis on conceptual understanding of supervised and unsupervised learning models.
Neural Networks and Deep Learning by Michael Nielsen
Description: This online text introduces neural networks and deep learning through intuitive explanations supported by practical implementation examples built from first principles.
Reinforcement Learning by Richard S. Sutton & Andrew G. Barto
Description: This volume presents the theoretical foundations and principal algorithms of reinforcement learning, focusing on value-based and policy-based methods.
Dive into Deep Learning by a collective of authors
Description: This interactive online resource integrates theoretical explanations with hands-on coding exercises, providing a practical and mathematically grounded introduction to deep learning.
Deep Learning by Goodfellow, Bengio, Courville
Description: This graduate-level textbook offers a comprehensive treatment of deep learning, including representation learning, optimization strategies, probabilistic modeling, and advanced neural architectures.
An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani
Description: This applied introduction to statistical learning presents core modeling techniques with an emphasis on practical implementation, offering an accessible bridge between theory and real-world data analysis.

Major Figures in AI

Here we have put together an indicative list of some widely recognized experts in various AI domains. Sorted alphabetically, the list is neither authoritative nor exhaustive: it is meant to inspire, provide basic orientation in the highly fluid field of AI, and serve as a starting point for more research.

Author Primary Role Field of Expertise Key Contribution/Success
Altman Sam CEO, OpenAI General Artificial Intelligence Co-founder of OpenAI and Loopt; former President of Y Combinator; author of the essay "The Intelligence Age". 
Bishop Christopher Technical Fellow, Microsoft Probabilistic Machine Learning Author of widely adopted machine learning textbooks, including Deep Learning: foundations and concepts (2024).
Brunton L.Steven Professor at University of Washington Dynamical Systems Pioneered physics-informed neural networks (PINNs).
Chollet François Engineer, Google DL Frameworks Created Keras; published the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) benchmark and launched the ARC Prize for the winner of a reasoning intelligence competition.
Crawford Kate Researcher, Microsoft Social Impact Leading voice on the material/labor costs of building AI systems, author of The Atlas of AI (2021).
Fleuret François Professor at University of Geneva Machine Learning Known for distilling complex neural network architectures into the pocket-sized Little Book of Deep Learning.
Géron Aurélien Engineer/Author Applied ML Author of Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. Former lead of YouTube’s video classification team.
Hinton Geoffrey Professor/Researcher Deep Learning (DL) Nobel Prize in Physics 2024; “Godfather of AI”; developed back-propagation.
Huyen Chip  Engineer/Founder MLOps & Systems Created popular Stanford courses on machine learning design; expert in Production AI infrastructure; author of AI engineering : building applications with foundation models.
LeCun Yann  Chief Scientist, Meta Neural Networks 2018 Turing Award winner; advanced convolutional neural networks (the basis for computerized image recognition).
Lee Kai-Fu Venture Capitalist AI Strategy Former President of Google China; expert on AI geopolitics, author of AI 2041: ten visions for our future.
Li Fei-Fei Professor at Stanford University Computer Vision Created ImageNet, which sparked the 2012 Deep Learning revolution.
Mollick Ethan Professor at the Wharton School, University of Pennsylvania Applied AI Expert on AI productivity and work transformation. Author of Co-Intelligence.
Narayanan Arvind Professor at Princeton AI Ethics & Privacy Researcher in data privacy and de-anonymization of data; author of AI snake oil: what artificial intelligence can do, what it can't, and how to tell the difference
Ng Andrew CEO, Landing AI AI Education Co-founded Google Brain & Coursera.
Nielsen Michael Scientist/Author Quantum & Neural Comp. Author of a widely recognized, freely available online book on neural networks, Neural Networks and Deep Learning; “visual proofs” of back-propagation.
Prince Simon J. D. Professor/Researcher Computer Vision His book Understanding Deep Learning (2023/24) is a key text for visual learners of neural network math.
Raschka Sebastian Lead AI Educator ML & LLMs Made LLM architecture accessible; creator of the Lightning AI curriculum.
Rosebrock Adrian Founder, PyImageSearch Computer Vision Developed training programs for applied Deep Learning in medical and industrial vision.
Russell Stuart J. Professor at University of California, Berkeley AI Safety & Logic Author of the AI textbook Artificial intelligence: a modern approach; pioneer in human-compatible AI.
Suleyman Mustafa CEO, Microsoft AI AI Policy & Ethics Co-founded DeepMind & Inflection AI; author of The Coming Wave, leading the “Containment” movement.
Tegmark Max Professor at MIT AI Safety/Physics Co-founded Future of Life Institute; one of the movers behind the 2023 “Pause AI” open letter initiative.
Trask Andrew W.  Senior Researcher, DeepMind Privacy-Preserving AI Founder of OpenMined; showed how to build neural networks using only NumPy; author of Grokking deep learning.

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Alexey Valentinovič Ryzhkov

Alexey Valentinovič Ryzhkov

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Artificial Intelligence (AI)

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Editor: Alexey Valentinovič Ryzhkov Last modified: 16.4. 2026 11:04