This chapter introduces unsupervised learning, where algorithms analyze data without predefined labels or target outcomes. It covers three main clustering approaches: agglomerative clustering (bottom-up approach merging similar data points) and divisive clustering (top-down approach, exemplified by k-means algorithm that partitions data into k groups by minimizing distances to centroids).

The chapter explains Expectation Maximization (EM) algorithm for handling incomplete data and finding maximum likelihood parameters in statistical models. It includes a section on reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments to maximize rewards.

Key topics include distance matrices, dendrograms, cluster evaluation metrics (AIC, BIC), and practical applications. The chapter emphasizes the artistic nature of unsupervised learning, requiring careful design decisions about thresholds, cluster numbers, and technique selection. Hands-on R examples demonstrate each method using real datasets.

This chapter introduces machine learning as a subset of artificial intelligence that enables computers to learn from data without explicit programming. It defines machine learning using Tom Mitchell’s formal framework and explores practical applications like self-driving cars, optical character recognition, and recommendation systems. The chapter focuses on regression as a fundamental machine learning technique, explaining linear regression for modeling relationships between variables. A key section covers gradient descent, an optimization algorithm that iteratively finds the best model parameters by minimizing error functions. Through hands-on Python examples, students learn to implement both linear regression and gradient descent algorithms, visualizing how models improve over iterations. The chapter emphasizes practical considerations for choosing appropriate algorithms, including accuracy, training time, linearity assumptions, and the number of parameters, preparing students for more advanced supervised and unsupervised learning techniques.

This chapter covers unsupervised learning, where algorithms analyze data without known true labels or outcomes. Unlike supervised learning, the goal is to discover hidden patterns and structures in data.

The chapter explores three main techniques: Agglomerative clustering works bottom-up, starting with individual data points and merging similar ones into larger clusters. Divisive clustering (including k-means) takes a top-down approach, splitting data into smaller groups. Both methods use distance matrices and dendrograms to visualize cluster relationships.

Expectation Maximization (EM) handles incomplete data by iteratively estimating missing parameters using maximum likelihood estimation. Model quality is assessed using AIC and BIC criteria.

The chapter also introduces reinforcement learning, where agents learn optimal actions through trial-and-error interactions with environments, receiving rewards or penalties. Applications include robotics, gaming, and autonomous systems. Throughout, the chapter emphasizes the creative, interpretive nature of unsupervised learning compared to more structured supervised approaches.

This chapter educates the reader on the main ideas that have enabled various advancements in Artificial Intelligence (AI) and Machine Learning (ML). Using various examples, and taking the reader on a journey through history, it showcases how the main ideas developed by the pioneers of AI and ML are being used in our modern era to make the world a better place. It communicates that our lives are surrounded by algorithms that work based on a few main ideas. It also discusses recent advancements in Generative AI, including the main ideas that led to the creation of Large Language Models (LLMs) such as Chat GPT. The chapter also discusses various societal considerations in AI and ML and ends with various technological advancements that could further improve our abilities in using the main ideas.

An introduction to AI, including an overview of essential technologies such as machine learning and deep learning, and a discussion on generative AI and its potential limitations. The chapter includes an exploration of AI's history, including its relationship to cybernetics, its role as a codebreaker, periods of optimism and “AI winters,” and today's global development with generative AI. Chapter 1 also include an analysis of AI's role in the international and national context, focusing on potential conflicts of goals and threats that can arise from technology.

Generative artificial intelligence has a long history but surged into global prominence with the introduction in 2017 of the transformer architecture for large language models. Based on deep learning with artificial neural networks, transformers revolutionised the field of generative AI for production of natural language outputs. Today’s large language models, and other forms of generative artificial intelligence, now have unprecedented capability and versatility. This emergence of these forms of highly capable generative AI poses many legal issues and questions, including consequences for intellectual property, contracts and licences, liability, data protection, use in specific sectors, potential harms, and of course ethics, policy, and regulation of the technology. To support the discussion of these topics in this Handbook, this chapter gives a relatively non-technical introduction to the technology of modern artificial intelligence and generative AI.

In this chapter, we introduce the reader to basic concepts in machine learning. We start by defining the artificial intelligence, machine learning, and deep learning. We give a historical viewpoint on the field, also from the perspective of statistical physics. Then, we give a very basic introduction to different tasks that are amenable for machine learning such as regression or classification and explain various types of learning. We end the chapter by explaining how to read the book and how chapters depend on each other.

ML methods are increasingly being used in (corporate) finance studies, with impressive applications. ML methods can be applied with the aim of reducing prediction error in the models, but can also be used to extend the existing traditional econometric methods. The performance of the ML models depends on the quality of the input data and the choice of model. There are many ML models, but all come with their own specific details. It is therefore essential to select accurate model(s) for the analysis. This chapter briefly reviews some broad types of ML methods. It covers supervised learning, which tends to achieve superior prediction performance by using more flexible functional forms than OLS in the prediction model. It explains unsupervised learning methods that derive and learn structural information from conventional data. Finally, the chapter also discusses some limitations and drawbacks of ML, as well as potential remedies.

Starting with the perceptron, in Chapter 6 we discuss the functioning, the training, and the use of neural networks. For the different neural network structures, the corresponding script in Matlab is provided and the limitations of the different neural network architectures are discussed. A detailed discussion and the underlying mathematical concept of the Backpropagation learning algorithm is accompanied with simple examples as well as sophisticated implementations using Matlab. Chapter 6 also includesconsiderations on quality measures of trained neural networks, such as the accuracy, recall, specificity, precision, prevalence, and some of the derived quantities such as the F-score and the receiver operating characteristic plot. We also look at the overfitting problem and how to handle it during the neural network training process.

This chapter provides an overview of the common machine learning algorithms used in psychological measurement (to measure human attributes). They include algorithms used to measure personality from interview videos; job satisfaction from open-ended text responses; and group-level emotions from social media posts and internet search trends. These algorithms enable effective and scalable measures of human psychology and behavior, driving technological advancements in measurement. The chapter consists of three parts. We first discuss machine learning and its unique contribution to measurement. We then provide an overview of the common machine learning algorithms used in measurement and their example applications. Finally, we provide recommendations and resources for using machine learning algorithms in measurement.

Under unsupervised learning, clustering or cluster analysis is first studied. Clustering methods are grouped into non-hierarchical (including K-means clustering) and hierarchical clustering. Self-organizing maps can be used as a clustering method or as a discrete non-linear principal component analysis method. Autoencoders are neural network models that can be used for non-linear principal component analysis. Non-linear canonical correlation analysis can also be performed using neural network models.

The historical development of statistics and artificial intelligence (AI) is outlined, with machine learning (ML) emerging as the dominant branch of AI. Data science is viewed as being composed of a yin part (ML) and a yang part (statistics), and environmental data science is the intersection between data science and environmental science. Supervised learning and unsupervised learning are compared. Basic concepts of underfitting/overfitting and the curse of dimensionality are introduced.