In brisk and engaging prose, this comprehensive introductory textbook traverses the broad sweep of US history since 1945. Winds of Hope, Storms of Discord explores how Americans of all walks of life –a political leaders, businesspeople, public intellectuals, workers, students, activists, migrants, and others – struggled to define the nation’s political, economic, geopolitical, demographic, and social character. It chronicles the nation’s ceaseless ferment, from the rocky conversion to peacetime in the early aftermath of World War II; to the frightening emergence of the Cold War and repeated US military adventures abroad; to the struggles of African Americans and other minorities to claim a share of the American Dream; to the striking transformations in social attitudes catalysed by the women’s movement and struggles for gay and lesbian liberation; to the dynamic force of political, economic, and social conservatism. Carrying the story to the spring of 2022, Winds of Hope also shows how dizzying technological changes at times threatened to upend the nation’s civic and political life.

As discussed so far in this book, the standard formulation of machine learning makes the following two basic assumptions: 1. Statistical equivalence of training and testing. The statistical properties of the data observed during training match those to be experienced during testing – i.e., the population distribution underlying the generation of the data is the same during both training and testing. 2. Separation of learning tasks. Training is carried out separately for each separate learning task – i.e., for any new data set and/or loss function, training is viewed as a new problem to be addressed from scratch.

In this chapter, we use the optimization tools presented in Chapter 5 to develop supervised learning algorithms that move beyond the simple settings studied in Chapter 4 for which the training problem could be solved exactly, typically by addressing an LS problem. We will focus specifically on binary and multi-class classification, with a brief discussion at the end of the chapter about the (direct) extension to regression problems. Following Chapter 4, the presentation will mostly concentrate on parametric model classes, but we will also touch upon mixture models and non-parametric methods.

Starting with Christianity’s Jewish heritage and Greco-Roman context, this chapter surveys the early theological efforts of the “apostolic fathers,” the apologists under Roman persecution, and the emergence of a fledgling “orthodoxy” through the crucial theological work of the church fathers against the backdrop of “heresy.” It then explores the canonization of the New Testament text, the development of the doctrines of the Trinity and christology, and the key contributions of Augustine in the West and the Cappadocians in the East as the apex of patristic theology.

This chapter addresses the formal doctrine of the Trinity, which is the distinctive Christian understanding of God and the linchpin and framework for all other Christian doctrines. After laying out this doctrine’s biblical basis and historical development, it presents a typology of trinitarian models, finding special promise in a social model for its biblical-orthodox soundness, conceptual coherence, and ethical implications.

In brisk and engaging prose, this comprehensive introductory textbook traverses the broad sweep of US history since 1945. Winds of Hope, Storms of Discord explores how Americans of all walks of life –a political leaders, businesspeople, public intellectuals, workers, students, activists, migrants, and others – struggled to define the nation’s political, economic, geopolitical, demographic, and social character. It chronicles the nation’s ceaseless ferment, from the rocky conversion to peacetime in the early aftermath of World War II; to the frightening emergence of the Cold War and repeated US military adventures abroad; to the struggles of African Americans and other minorities to claim a share of the American Dream; to the striking transformations in social attitudes catalysed by the women’s movement and struggles for gay and lesbian liberation; to the dynamic force of political, economic, and social conservatism. Carrying the story to the spring of 2022, Winds of Hope also shows how dizzying technological changes at times threatened to upend the nation’s civic and political life.

This chapter focuses on three key problems that underlie the formulation of many machine learning methods for inference and learning, namely variational inference (VI), amortized VI, and variational expectation maximization (VEM). We have already encountered these problems in simplified forms in previous chapters, and they will be essential in developing the more advanced techniques to be covered in the rest of the book. Notably, VI and amortized VI underpin optimal Bayesian inference, which was used, e.g., in Chapter 6 to design optimal predictors for generative models; and VEM generalizes the EM algorithm that was introduced in Chapter 7 for training directed generative latent-variable models.

This chapter surveys important modern developments connected to the Enlightenment of the 18th century, which put Christianity in a defensive position, having to justify its basic beliefs in light of a new prime criterion (i.e., not revelation but reason) and a new defining context (i.e., history). This context led some thinkers (e.g., Protestant liberalism) to significant departures from traditional forms of theology and other thinkers (e.g., neo-orthodoxy) to significant attempts to rethink Christian orthodoxy in a new mode.

The previous chapters have adopted a limited range of probabilistic models, namely Bernoulli and categorical distributions for discrete rvs and Gaussian distributions for continuous rvs. While these are common modeling choices, they clearly do not represent many important situations of interest for machine learning applications. For instance, discrete data may a priori take arbitrarily large values, making categorical models unsuitable. Continuous data may need to satisfy certain constraints, such as non-negativity, rendering Gaussian models far from ideal.

This chapter examines the concept of revelation, including the traditional Christian distinction between general and special revelation (especially scripture). After exploring the relationship of these two forms of revelation, it examines some of the complexities involved in articulating a knowledge of God.

So far, this book has focused on conventional centralized learning settings in which data are collected at a central server, which carries out training. When data originate at distributed agents, such as personal devices, organizations, or factories run by different companies, this approach has two clear drawbacks: • First, it requires transferring data from the agents to the server, which may incur a prohibitive communication load. • Second, in the process of transferring, storing, and processing the agents’ data, sensitive information may be exposed or exploited.