Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Building Leadership Effectiveness This program encourages leaders to develop practices that transform values into action, vision into realities, obstacles into innovations, and risks into rewards. Participants will be introduced to the five practices of exemplary leadership: modeling the way, inspiring a shared vision, challenging the process, enabling others to act, and encouraging the heart Coaching & Communicating for Performance Coaching & Communicating for Performance is a highly interactive program that will give supervisors and managers the opportunity to build skills that will enable them to share expectations and set objectives for employees, provide constructive feedback, more effectively engage in learning conversations, and coaching opportunities. Skillful Conflict Management for Leaders As a leader, it is important to understand conflict and be effective at conflict management because the way conflict is resolved becomes an integral component of our university’s culture. This series of conflict management sessions help leaders learn and put into practice effective strategies for managing conflict.

기독교 사역과 Leadership

The Social Psychology of Organizations.

Ethical leadership: A social learning perspective for construct development and testing

\"What are your assumptions (implicit as well as explicit) about the most effective way to manage people?\" So began Douglas McGregor in this 1960 management classic. It was a seemingly simple question he asked, yet it led to a fundamental revolution in management. Today, with the rise of the global economy, the information revolution, and the growth of knowledge-driven work, McGregor's simple but provocative question continues to resonate-perhaps more powerfully than ever before. Heralded as one of the most important pieces of management literature ever written, a touchstone for scholars and a handbook for practitioners, The Human Side of Enterprise continues to receive the highest accolades nearly half a century after its initial publication. Influencing such major management gurus such as Peter Drucker and Warren Bennis, McGregor's revolutionary Theory Y-which contends that individuals are self-motivated and self-directed-and Theory X-in which employees must be commanded and controlled-has been widely taught in business schools, industrial relations schools, psychology departments, and professional development seminars for over four decades. In this special annotated edition of the worldwide management classic, Joel Cutcher-Gershenfeld, Senior Research Scientist in MIT's Sloan School of Management and Engineering Systems Division, shows us how today's leaders have successfully incorporated McGregor's methods into modern management styles and practices. The added quotes and commentary bring the content right into today's debates and business models. Now more than ever, the timeless wisdom of Douglas McGregor can light the path towards a management style that nurtures leadership capability, creates effective teams, ensures internal alignment, achieves high performance, and cultivates an authentic, value-driven workplace--lessons we all need to learn as we make our way in this brave new world of the 21st century.

The Human Side of Enterprise.

Determinants of innovative behavior: A study among SME knowledge workers Determinants of innovative behavior: A study among SME knowledge workers Jeroen P.J. de Jong & Deanne N. den Hartog, Gedrag & Organisatie, Volume 18, October 2005, p. 235-259. In order to develop new products, services and/or better work processes in organizations, innovative behavior of individual employees is an important requirement. This also applies to knowledge workers in small and medium-sized enterprises such as consultancy, research and engineering firms. Yet, in previous research SME knowledge workers' determinants of innovative behavior have rarely been examined. This study investigates the relationship between a strategic organizational focus on innovation and innovative behavior of knowledge workers. It also explores the moderating influences of various work and environmental characteristics. Multilevel analyses, applied on survey data from 360 knowledge workers in 36 firms, show that a strategic focus on innovation is positively related to the innovative behavior of knowledge workers. The effect increases when specific work characteristics apply, namely perceived job variation, job autonomy and an innovative working climate. Moreover, some work characteristics are directly related to innovative behavior of employees: once again variation and autonomy, as well as having frequent external contacts. Finally, the analysis demonstrates that knowledge workers behave more innovative in sectors where competition is based on new or improved products and services.

Determinanten van innovatief gedrag: een onderzoek onder kenniswerkers in het MKB

Background:
Early warning scores (EWS) may aid the prediction of major adverse events in hospitalized patients. Recently, an expanded EWS was introduced in the Netherlands. The aim of this study was to assess the relationship between this EWS and the occurrence of major adverse clinical events during hospitalization of patients admitted to a general and trauma surgery ward.

Methods:
This was a prospective cohort study of consecutive patients admitted to the general and trauma surgery ward of a university medical centre (March–September 2009). Follow-up was limited to the time the patient was hospitalized. Logistic regression analysis was used to assess the relationship between the EWS and the occurrence of the composite endpoint consisting of death, reanimation, unexpected intensive care unit admission, emergency surgery and severe complications. Performance of the EWS was analysed using sensitivity, specificity, predictive values and receiver operating characteristic (ROC) curves.

Results:
A total of 572 patients were included. During a median follow-up of 4 days, 46 patients (8.0 per cent) reached the composite endpoint (two deaths, two reanimations, 17 intensive care unit admissions, 44 severe complications, one emergency operation). An EWS of at least 3, adjusted for baseline American Society of Anesthesiology classification, was associated with a significantly higher risk of reaching the composite endpoint (odds ratio 11·3, 95 per cent confidence interval (c.i.) 5·5 to 22·9). The area under the ROC curve was 0·87 (95 per cent c.i. 0·81 to 0·93). When considering an EWS of at least 3 to be a positive test result, sensitivity was 74 per cent and specificity was 82 per cent.

Conclusion:
An EWS of 3 or more is an independent predictor of major adverse events in patients admitted to a general and trauma surgery ward. Copyright © 2011 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd.

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Accuracy of an expanded early warning score for patients in general and trauma surgery wards.

Taking an interdisciplinary approach, this volume focuses on the trust processes between people within organizations, with an emphasis on empirical studies. Rational foundations and psychological motivations for trust are taken into account through conceptual and empirical chapters. The authors begin by summarizing a number of key elements from the literature including how trust develops in time, and how its development is affected by social-psychological phenomena. This includes the notion of ‘framing': the interpretive context in which actions are perceived and evaluated. A conceptual framework is then used to analyse trust and power in the internal relationships of the organization. The contributors take up this issue in an evolutionary analysis of competition between trust and cheating.

Trusting others in organizations: leaders, management and co-workers

In this team-level study, we present and test a model in which two aspects of interactional justice climate, its level and its strength, interact to moderate the effects of national diversity on team performance. Connecting the literatures on team diversity and (interactional) justice climate, we hypothesize that a high level of interactional justice exhibited by the line manager promotes the performance of nationally diverse teams, but only when team members share this perception (i.e. when the climate is strong). Using time-lagged performance measurement, we conducted a multisource field study using data from 272 employees clustered in 45 teams who worked for a local subsidiary of a multinational enterprise. The findings confirm our hypothesis and have implications for the diversity and justice literatures, while advancing knowledge that promotes the effective management of increasingly diverse workforces in this era of globalization.

Deanne N. Den Hartog

Papers

Determinanten van innovatief gedrag: een onderzoek onder kenniswerkers in het MKB

Accuracy of an expanded early warning score for patients in general and trauma surgery wards.

Trusting others in organizations: leaders, management and co-workers

National diversity and team performance: the moderating role of interactional justice climate

Choosing your words carefully: Leaders' narratives of complex emergent problem resolution