There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

行銷硏究 : Marketing research

This handbook looks to provide academics and students with a comprehensive and holistic understanding of the phenomenon of innovation. Innovation spans a number of fields within the social sciences and humanities: Management, Economics, Geography, Sociology, Politics, Psychology, and History. Consequently, the rapidly increasing body of literature on innovation is characterized by a multitude of perspectives based on, or cutting across, existing disciplines and specializations. Scholars of innovation can come from such diverse starting points that much of this literature can be missed, and so constructive dialogues missed. The editors of The Oxford Handbook of Innovation have carefully selected and designed twenty-one contributions from leading academic experts within their particular field, each focusing on a specific aspect of innovation. These have been organized into four main sections, the first of which looks at the creation of innovations, with particular focus on firms and networks. Section Two provides an account of the wider systematic setting influencing innovation and the role of institutions and organizations in this context. Section Three explores some of the diversity in the working of innovation over time and across different sectors of the economy, and Section Four focuses on the consequences of innovation with respect to economic growth, international competitiveness, and employment. An introductory overview, concluding remarks, and guide to further reading for each chapter, make this handbook a key introduction and vital reference work for researchers, academics, and advanced students of innovation. Contributors to this volume - Jan Fagerberg, University of Oslo William Lazonick, INSEAD Walter W. Powell, Stanford University Keith Pavitt, SPRU Alice Lam, Brunel University Keith Smith, INTECH Charles Edquist, Linkoping David Mowery, University of California, Berkeley Mary O'Sullivan, INSEAD Ove Granstrand, Chalmers Bjorn Asheim, University of Lund Rajneesh Narula, Copenhagen Business School Antonello Zanfei, Urbino Kristine Bruland, University of Oslo Franco Malerba, University of Bocconi Nick Von Tunzelmann, SPRU Ian Miles, University of Manchester Bronwyn Hall, University of California, Berkeley Bart Verspagen , ECIS Francisco Louca, ISEG Manuel M. Godinho, ISEG Richard R. Nelson, Mario Pianta, Urbino Bengt-Ake Lundvall, Aalborg

The Oxford Handbook of Innovation

Cellular manufacturing system has been proved a vital approach for batch and job shop production systems. Group technology has been an essential tool for developing a cellular manufacturing system. The paper aims to discuss various cell formation techniques and highlights the significant research work done in past over the years and attempts to points out the gap in research of past studies. In general, descriptive procedures can be classified into three major classes. The first class, which is referred to as part families identification (PFI), begins the cell formation process by identifying the families of parts first and then allocates machines to the families. The second class, which is referred to as machine groups identification (MGI). The third class of the descriptive procedures, which is referred to as part families/machine grouping (PF/MG), identifies the part families and machine groups simultaneously. PFI methods can be sub- classified as those based on informal systems (e.g., rules of thumb, visual examination or other criteria) and those based on formal coding and classification systems. The role of group technology (GT) codes in the context of cellular manufacturing is primarily as an aid in identifying the part families to which production cells should be dedicated. Further analysis is required before a family of parts to be manufactured in a cell, and the machines, which will comprise that cell, can be specified. MGI procedures consider the CF problem as a two- stage process where in the first stage of their analysis, machines are grouped based on information available in part routings and then in the second stage, parts are allocated to machine groups. When a CF approach attempts to group parts into part families and machines into machine groups simultaneously, then such an approach can be classified as PF/MG. Burbidge (1971) proposed one of the earliest PF/MG descriptive approaches for the CF problem which is referred to as Production Flow Analysis (PFA). PFA is a technique, which analyses the information given in route cards to form cells. A manual method for CF called "Nuclear Synthesis" is proposed where manufacturing cells are created around "key machines". E1-Essawy (1972) proposed a method called Component Flow Analysis (CFA) at about the same time. In some respects, the methodology of CFA does differ from that of Burbidge's PFA procedure since the latter first partitions the problem, whereas the former does not.

Cell Formation Techniques - A Study

The generation of fly ash is supported by much effort to utilize it to the greatest extent possible. Present usage of around 58% of fly ash generated every year demands more effort. Around 6% of fly ash utilized is being used in road applications. More fly ash can be utilized in road sector because of its having huge potential mainly in embankments. Fifteen factors have been identified, studied, analysed, classified and presented as a model that suggests their significance towards accomplishing the objective of more fly-ash utilization in the road sector. Interpretive structural modelling (ISM) is applied to these factors to obtain their relative relationships. The key factor is local specific strategic planning, which is identified and recommended for implementation in the present scenario along with maintaining focus on all factors for successful implementation towards the desired objective.

Evaluating technology management factors for fly-ash utilization in the road sector using an ISM approach

A laconic capitulation of high pressure metrology

Virtual reality (VR) applications in dentistry: An innovative technology to embrace

The knowledge-based view of organizational behavior emphasizes the importance of knowledge for organizations to retain their competitive advantage. Thus, the success of knowledge sharing is important because, if successful, it results in shared intellectual capital. Knowledge is one of the most important assets of a business which influence competitiveness, thus we need to understand the relationship between knowledge sharing and competitiveness. This study investigates possible factors of resistance or support by knowledge workers to the sharing of knowledge within an organization. Further the relationship among knowledge sharing factors with competitiveness has been explored from the available research. Based on the study of various organizations it is found that the existing knowledge is not being effectively disseminated throughout the organization, resulting in lost productivity and opportunity as a result of failure to exploit available knowledge. Major factors of Knowledge-Sharing (KS), which are important to improve the competitiveness of organizations in international business, were identified, and an integrated model of these factors has been developed using interpretive structural modeling (ISM). The developed model has been further improved with the application of fuzzy MICMAC. The generated model may help in better sharing of knowledge in organizations and will help in correlating with competitiveness.

Abid Haleem

Papers

Cell Formation Techniques - A Study

Evaluating technology management factors for fly-ash utilization in the road sector using an ISM approach

A laconic capitulation of high pressure metrology

Virtual reality (VR) applications in dentistry: An innovative technology to embrace

Modeling knowledge sharing factors and understanding its linkage to competitiveness