Kevin P. Murphy – författare

In a 1907 lecture to Harvard undergraduates, Theodore Roosevelt warned against becoming "too fastidious, too sensitive to take part in the rough hurly-burly of the actual work of the world." Roosevelt asserted that colleges should never "turn out mollycoddles instead of vigorous men," and cautioned that "the weakling and the coward are out of place in a strong and free community." A paradigm of ineffectuality and weakness, the mollycoddle was "all inner life," whereas his opposite, the "red blood," was a man of action. Kevin P. Murphy reveals how the popular ideals of American masculinity coalesced around these two distinct categories. Because of its similarity to the emergent "homosexual" type, the mollycoddle became a powerful rhetorical figure, often used to marginalize and stigmatize certain political actors. Issues of masculinity not only penetrated the realm of the elite, however. Murphy''s history follows the redefinition of manhood across a variety of classes, especially in the work of late nineteenth-century reformers, who trumpeted the virility of the laboring classes. By highlighting this cross-class appropriation, Murphy challenges the oppositional model commonly used to characterize the relationship between political "machines" and social and municipal reformers at the turn of the twentieth century. He also revolutionizes our understanding of the gendered and sexual meanings attached to political and ideological positions of the Progressive Era.

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today''s Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.