Library guides

Computer science, Multimedia and Telecommunications

Theoretical framework of artificial intelligence

Artificial intelligence (AI) has existed for years, but in recent months it has evolved very rapidly. Why is this? What innovations has it entailed? In this library guide, you will find theoretical, basic and specific content on artificial intelligence, its types and its uses.

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Basic concepts

What is AI?

¿Qué es y cómo funciona la INTELIGENCIA ARTIFICIAL?

Video explaining what artificial intelligence is, how it works, and the types of AI.

What is AI?

Video explaining what AI is, how it works, and its advantages and disadvantages.

Artificial intelligence (AI) link opens in a new window

What artificial intelligence is, what types there are, and how it is used in different fields.

What is artificial intelligence (AI)? link opens in a new window

Set of resources and terms that can help understand AI in greater depth.

Intel·ligència artificial link opens in a new window

Episode of "Quèquicom" explaining the resources and terms that can help understand AI and how it is programmed in more depth.

Intel·ligència artificial: qui té por de la màquina? link opens in a new window

Episode of "Quan arribin els marcians" about the uses of AI and how we will incorporate it into our daily lives.

Types of data (structured and unstructured)

Datos estructurados y no estructurados: ¿en qué se diferencia? link opens in a new window

The types of data there are in relation to artificial intelligence, and the differences between them.

Structured vs Unstructured Data: 5 key differences link opens in a new window

Differences between structured and unstructured data, and the uses of each of them.

Machine learning pocket reference: working with structured data in python link opens in a new window

Reference book with examples to understand the basics of structured machine learning.

_{Harrison, M. (2019) Machine learning pocket reference: working with structured data in python. Beijing: O'Reilly. Available at: https://learning.oreilly.com/library/view/machine-learning-pocket/9781492047537/?sso_link=yes&sso_link_from=Catalunya (Accessed: June 27, 2024).}

An overview of distance and similarity functions for structured data link opens in a new window

Article exploring the various functions of structured data.

_{Ontañón, S. (2020) 'An overview od distance and similarity functions for structured data', The Artificial Intelligence Review, 53(7), p. 5309-5351. Available at: https://www.proquest.com/scholarly-journals/overview-distance-similarity-functions-structured/docview/2441910394/se-2?accountid=15299 (Accessed: June 27, 2024).}

Combining structured and unstructured data for predictive models: a deep learning approach link opens in a new window

Article on the uses of structured data in the clinical field and the representation of information.

_{Zhang, D., Yin, C., Zeng, J., Yuan, X. and Zhang, P. (2020) 'Combining structured and unstructured data for predictive models: a deep learning approach', BMC Medical Informatics and Decision Making, 20, p. 1-11. Available at: https://doi.org/10.1186/s12911-020-01297-6 (Accessed: June 27, 2024).}

AI models

Neural networks

Cómo funcionan las redes neuronales – Inteligencia Artificial

Video to understand how neural networks work.

¿Qué es una red neuronal? link opens in a new window

What neural networks in artificial intelligence are, what types there are, and how they are trained.

What are neural networks? link opens in a new window

What neural networks are, their history and their relationship with deep learning.

Neural Network in 5 minutes

Video to understand how neural networks act and the various layers of data.

Artificial neural networks as models of neural information processing link opens in a new window

The research presented in this book provides various perspectives on the use of artificial neural networks as neural information processing models.

_{Van Gerven, M. and Bohte, S. (2018) Artificial neural networks as models of neural information processing. Lausana: Frontiers Media SA. Available at: https://directory.doabooks.org/handle/20.500.12854/41357 (Accessed: June 27, 2024).}

Decision trees

Decision tree classification clearly explained!

Video about decision trees in AI and their components.

¿Qué es un árbol de decisión? link opens in a new window

Advantages and disadvantages of using decision trees for data mining tasks.

Deep neural network initialization with decision trees link opens in a new window

Article presenting a new automated process to build and initialize deep neural networks based on decision trees.

_{Humbird, K., Peterson, J. and Macclarren, R. (2019) 'Deep neural network initialization with decision trees', IEEE Transactions on Neural Networks and Learning Systems, 30(5), p. 1286-1295. Available at: https://doi.org/10.1109/TNNLS.2018.2869694 (Accessed: June 27, 2024).}

Using AI: prompting

What is prompt engineering? (in about a minute)

One-minute video to learn what prompt engineering is and its importance in relation to chatbots.

Cómo crear prompts efectivos

Video explaining the steps to follow to create effective prompts and receive better responses from AI.

Qué es un prompt y por qué son tan importantes para usar la inteligencia artificial link opens in a new window

What prompts are, and how they relate to the use of the latest forms of AI.

Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing link opens in a new window

This article studies and sorts research works into a new paradigm in natural language processing called "prompt-based learning".

_{Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H. and Neubig, G. (2023) 'Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing', ACM Computing Surveys, 55(9), p. 1-35. Available at: https://doi.org/10.1145/3560815 (Accessed: June 27, 2024).}

Types of learning

Machine learning

What's the difference between AI, Machine Learning, and Deep Learning?

Video explaining the differences between machine learning and deep learning.

Introduction to Machine Learning: exploring its many forms link opens in a new window

What machine learning is, how it works, and what types there are.

Amazon Science Blog link opens in a new window

Collection of materials to understand the uses and functions of machine learning.

Hands-on machine learning with Scikit-Learn and TensorFlow link opens in a new window

Book teaching how to use simple and efficient tools to implement programs capable of learning from data.

_{Géron, A. (2017) Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. Sebastopol, CA: O'Reilly Media, Inc. Available at: https://discovery.biblioteca.uoc.edu/permalink/34CSUC_UOC/1asfcbc/alma991000554169706712 (Accessed: June 27, 2024).}

Introduction to machine learning with Python: a guide for data scientists link opens in a new window

Book teaching practical ways to create your own machine learning solutions with Python.

_{Müller, A. and Guido, S. (2017) Introduction to machine learning with Python: a guide for data scientists. Sebastopol, CA: O'Reilly Media, Inc. Available at: https://learning.oreilly.com/library/view/introduction-to-machine/9781449369880/?sso_link=yes&sso_link_from=Catalunya (Accessed: June 27, 2024).}

Pattern recognition and machine learning link opens in a new window

This book presents approximate inference algorithms that allow quick approximate answers to be provided in situations in which exact answers are not feasible.

_{Bishop, C. (2006) Pattern recognition and machine learning. New York: Springer. Available at: https://discovery.biblioteca.uoc.edu/permalink/34CSUC_UOC/1asfcbc/alma991000215569706712 (Accessed: June 27, 2024).}

Supervised learning

¿Qué es el aprendizaje supervisado? link opens in a new window

How supervised learning works, and how it can be used to create high-precision machine learning models.

What is supervised learning? link opens in a new window

What supervised learning is, how it works, and its benefits and limitations.

Semi-supervised learning

Google Research Blog - Semi-Supervised Learning link opens in a new window

Specific resources with more in-depth information on semi-supervised machine learning.

Semi-Supervised Learning, explained with examples link opens in a new window

What semi-supervised learning is, and how it differs from other types.

What is semi-supervised learning? A guide for beginners link opens in a new window

A short and simple guide to understanding what semi-supervised learning is and how to use it.

Unsupervised learning

Google Research Blog - Unsupervised Learning link opens in a new window

Specific resources for delving deeper into unsupervised machine learning.

What is unsupervised learning? link opens in a new window

How unsupervised learning works, and how it can be used to explore and group data.

Clustering: qué es y qué aplicaciones tiene link opens in a new window

What is clustering, and its uses in AI.

Generative AI

Generative deep learning: teaching machines to paint, write, compose, and play link opens in a new window

How to create generative deep learning models, such as variational automatic encoders, generative adversarial networks (GANs), encoder-decoder models and world models.

_{Foster, D. (2019) Generative deep learning: teaching machines to paint, write, compose, and play. Beijing: O'Reilly. Available at: https://learning.oreilly.com/library/view/generative-deep-learning/9781492041931/?sso_link=yes&sso_link_from=Catalunya (Accessed: June 27, 2024).}

MIT Deep Generative Modeling link opens in a new window

Video about generative models and their types.

Generative adversarial networks link opens in a new window

Framework for generative models through an adversarial process in which two models are trained simultaneously: a generative (G) model that captures data distribution and a discriminative (D) model that estimates the probability that a sample comes from training data.

Les increïbles possibilitats de la intel·ligència artificial, with Josep Maria Ganyet link opens in a new window

Section of the programme "Tot es mou" about the possibilities of AI.

Language models are few-shot learners link opens in a new window

Article explaining that scaling up language models greatly improves task-agnostic, few-shot performance, reaching competitiveness with state-of-the-art fine-tuning approaches.

MIT Text-to-Image generation link opens in a new window

Video about deep learning models and text-to-image generation.

Zero-shot text-to-image generation link opens in a new window

Article describing a text-to-image generation approach based on a transformer that autoregressively models text and image tokens as a single stream of data.

Reinforcement learning

Reinforcement learning, second edition: an introduction link opens in a new window

Book focusing on the basic algorithms of online learning, with mathematical material.

_{Sutton, R. and Barto, A. (2018) Reinforcement learning, second edition: an introduction. Camdridge: MIT Press. Available at: https://ebookcentral.proquest.com/lib/bibliouocsp-ebooks/detail.action?docID=6260249 (Accessed: June 27, 2024).}

Reinforcement Learning link opens in a new window

Video about the various algorithms involved in reinforcement learning.

Google Research Blog - Reinforcement Learning link opens in a new window

Specific resources for learning more about reinforcement learning.

Reinforcement learning frameworks link opens in a new window

Collection of materials on reinforcement learning models in artificial intelligence.

Deep learning

DeepLearningAI link opens in a new window

YouTube channel to learn more about deep learning and artificial intelligence.

Deep learning link opens in a new window

Introduction to a wide range of deep learning topics covering mathematical and conceptual background, deep learning techniques used in industry and research perspectives.

MIT Introduction to Deep learning link opens in a new window

Video about deep learning and neural networks.

NVIDIA Deep Learning Institute link opens in a new window

The NVIDIA Deep Learning Institute (DLI) provides resources for diverse learning needs ranging from learning materials to personalized training.

Google Research Blog - Deep Learning link opens in a new window

Specific resources to learn more about deep learning.

Other types of learning

Active learning

What is Active Learning? The Future for Training AI Models

Video explaining what active learning is, with demonstrations.

Active Learning: Strategies, Tools, and Real-World Use Cases link opens in a new window

What active learning is, what it is for, how it works and how to use it.

Transfer learning

What is Transfer Learning? [Explained in 3 minutes]

Short video explaining what Transfer Learning is and how it works.

¿Qué es el Transfer Learning? link opens in a new window

What transfer learning is, and its relationship with deep learning.

Federated learning

Training AI Models with Federated Learning

Video in which Martin Keen talks about the forms, benefits and challenges of federated learning.

Fair and autonomous sharing of federate learning models in mobile Internet of Things link opens in a new window

Article on the protocols followed in federated learning models.

Applications

Recommender systems

Recommender systems the textbook link opens in a new window

This book comprehensively covers recommender systems, which provide users with personalized recommendations for products or services based on their previous searches or purchases.

_{Aggarwal, C. (2016) Recommender systems: the textbook. Cham: Springer International Publishing. Available at: https://discovery.biblioteca.uoc.edu/permalink/34CSUC_UOC/l8oqf8/alma991000903306306712 (Accessed: June 27, 2024).}

Artificial intelligence in recommender systems link opens in a new window

Article that systematically discusses the basic methodologies and prevailing techniques used in recommender systems and how AI can effectively improve technological development and the application of recommender systems.

_{Zhang, Q., Lu, J. and Jin, Y. (2021) 'Artificial intelligence in recommender systems', Complex & Intelligent Systems, 7(1), p. 439-457. Available at: https://doi.org/10.1007/s40747-020-00212-w (Accessed: June 27, 2024).}

Applied Recommender Systems with Python Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques link opens in a new window

This book teaches you how to create recommender systems with machine learning algorithms using Python.

_{Kulkarni, A. (2023) Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques. Berkeley, CA: Apress. Available at: https://learning.oreilly.com/library/view/applied-recommender-systems/9781484289549/?sso_link=yes&sso_link_from=Catalunya (Accessed: June 27, 2024).}

Natural Language Processing (NLP)

Practical natural language processing: a comprehensive guide to building real-world NLP systems link opens in a new window

Book that provides a guide to creating natural language processing systems.

_{Sowmya, V. B., Majumder, B., Gupta, A. and Surana, H. (2020) Practical natural language processing: a comprehensive guide to building real-world NLP systems. Sebastopol, CA: O'Reilly Media. Available at: https://discovery.biblioteca.uoc.edu/permalink/34CSUC_UOC/1asfcbc/alma991000735048406712 (Accessed: June 27, 2024).}

Google Research Blog - Natural Language Processing link opens in a new window

Specific resources to learn more about natural language processing.

KDnuggets - Natural Language Processing link opens in a new window

Blog posts on natural language processing in AI.

Language Models link opens in a new window

Constantly updated list of language models.

Speech processing

Deep neural networks for music link opens in a new window

Collection of learning resources on deep neural networks in music.

Google Research Blog - Audio link opens in a new window

Collection of materials on audio processing with AI.

Speech recognition link opens in a new window

AI speech recognition methods and materials.

Introducing Whisper link opens in a new window

Neural network called "Whisper" that approaches human level robustness and accuracy in English speech recognition.

Computer vision

Computer vision: a modern approach link opens in a new window

This book discusses modern computer vision methods.

_{Forsyth, A. and Ponce, J. (2012) Computer vision: a modern approach. Boston: Pearson Education. Available at: https://discovery.biblioteca.uoc.edu/permalink/34CSUC_UOC/1asfcbc/alma991000470159706712 (Accessed: June 27, 2024).}

Computer vision algorithms and applications link opens in a new window

Reference book that takes a scientific approach to the formulation of computer vision problems. These problems are analysed using the latest conventional and deep learning models and are solved using engineering principles.

Google Research Blog - Computer Vision link opens in a new window

Collection of materials on computer vision with AI.

KDnuggets - Computer vision link opens in a new window

Blog posts about computer vision in AI.

Computer vision link opens in a new window

Computer vision materials and methods.

How we're teaching computers to understand pictures link opens in a new window

TED talk on how computers are being trained to understand images.

How computers learn to recognize objects instantly link opens in a new window

TED talk explaining how computers learn to recognize objects, with a live demonstration.

Ethics and reliability

Responsible Artificial Intelligence Institute link opens in a new window

The Responsible AI Institute (RAI Institute) focuses on providing tools for AI organizations and professionals to build, buy and deliver secure and trusted AI systems.

Responsible AI practices (Google) link opens in a new window

Set of considerations for software systems when it comes to designing AI systems, according to Google.

Responsible AI practices (Microsoft) link opens in a new window

Microsoft's internal guide on how to design, build and test AI systems.

Facebook's five pillars of Responsible AI (Meta AI) link opens in a new window

Facebook's list of values for a responsible use of AI at the company.

Responsible AI in the generative era (Amazon Science blog) link opens in a new window

Blog post about the problems entailed in AI and how to address them.

Model d'autoavaluació PIO link opens in a new window

PIO (Principles, Indicators and Observables) self-assessment model to move forward with the ethical evaluation of data and AI systems by means of a checklist.

Informe OIEAC link opens in a new window

Report on the ethical and responsible use of AI in society.

First draft of the Recommendation on the Ethics of Artificial Intelligence link opens in a new window

UNESCO draft recommendations on the ethics of AI.

Presentació del treball #OEIAC: Intel·ligència artificial, ètica i societat link opens in a new window

Video explaining how the report of the Catalan Observatory for Ethics in Artificial Intelligence (OEIAC) on the ethics of AI was prepared.

Interview with Cecilio Angulo, president of the Catalan Association for Artificial Intelligence link opens in a new window

Interview with Cecilio Angulo, president of the Catalan Association for Artificial Intelligence, about the advantages of AI and the regulations that must be put in place.

Librarians responsible for this library guide

Lis Balcells
Operative subgroup: Librarian for Computer Science, Multimedia and Telecommunications, vocational training Operative group: Library for Learning
Adrián de Mon
Operative subgroup: Computer Science, Multimedia and Telecommunications resource manager, vocational training Operative group: Library for Learning

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