Machine learning and deep learning

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.Comment: Published online first in Electronic Market

Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output. The fields of pattern recognition and machine learning study ways of constructing such classifiers. The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class? This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem. In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner

Computer Simulation and the Practice of Oral Medicine and Radiology

The practice of Oral Medicine and Radiology has long been considered an art form. Collecting and collimating the enormous amount of information each patient brings has always tested the best of our abilities as diagnosticians. However, as the tide of smartphones, cheaper data access, and automation rises, it threatens to wash away all that we have held sacrosanct about conventional clinical practices. In this tussle between what is traditional and what is tantalizing, it is time to question, as diagnosticians, how much can we accede to the invasion of algorithms. How does computer simulation affect the practice of diagnosis in the field of Oral Medicine and Radiology

The role of the musical intelligence in whole brain education

This study was prompted by the recent increase in academic and public interest in neuromusical brain research, which provides information about how the brain processes music. It is the task of neural science to explain how the individual units of the brain are used to control behaviour, and how the functioning of these units is influenced by an individual's specific environment and relationships with other people. However, the concept of neuromusical research is relatively new to music education. In any learning experience, brain processing (of information) is not an end in itself. The skill of 'thinking' is dependent on the whole integrated mind/body system, with skills being a manifestation of conscious physical responses that demonstrate knowledge acquisition. Howard Gardner's 'Theory of Multiple Intelligences' lists the musical intelligence as one of eight autonomous intelligences: linguistic, logic-mathematical, spatial, bodily-kinesthetic, musical, intrapersonal, interpersonal, and environmental. All of these intelligences can be developed to a reasonably high level. This thesis uses David Elliott's praxial philosophy as a conceptual basis. Elliott's four meanings of music education: education in music, by music, for music, and by means of music, have been selected to determine the parameters for an 'inclusive' understanding of musical intelligence. Scientific research findings, brain based data, and behavioural results with educational implications have been used to define what is meant by the musical intelligence, and its role in whole brain learning. Whole brain learning (also referred to as 'accelerated' learning or 'super' learning) is examined in the framwork of IQ (intellectual quotient/intelligence), EQ (emotional intelligence), and SQ (spiritual intelligence). It is important to note that the brain imposes certain constraints on the learning ability of individuals, but that there are also numerous benefits to be derived from an awarenss of brain functions pertaining to education in general and music education in particular. These constraints and benefits are an important feature of whole brain learning, with the musical intelligence playing a vital role.Dissertation (DMus)--University of Pretoria, 2003.Musicunrestricte

Characteristics and effects of motivational music in exercise

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The research programme had three principal objectives. First, the evaluation and extension of the extant conceptual framework pertaining to motivational music in exercise settings. Second, the development of a valid instrument for assessing the motivational qualities of music: The Brunel Music Rating Inventory-2 (BMRI-2). Third, to test the effects of motivational and oudeterous (lacking in both motivational and de-motivational qualities) music in an externally-valid setting. These objectives were addressed through 4 studies. First, a series of open-ended interviews were conducted with exercise leaders and participants (N = 13), in order to investigate the characteristics and effects of motivational music in the exercise setting. The data were content analysed to abstract thematic categories of response. These categories were subsequently evaluated in the context of relevant conceptual frameworks. Subsequently, a sample of 532 health-club members responded to a questionnaire that was designed to assess the perceived characteristics of motivational music. The responses were analysed across age groups, gender, frequency of attendance (low, medium, high), and time of attendance (morning, afternoon, evening). The BMRI-2 was developed in order to address psychometric weaknesses that were associated with its forbear, the BMRI. A refined item pool was created which yielded an 8-item instrument that was subjected to confirmatory factor analysis. A single-factor model demonstrated acceptable fit indices across three different pieces of music, two samples of exercise participants, and both sexes. The BMRI-2 was used to select 20 pieces of motivational music, which were delivered in a health club gymnasium. It was found that health club members (N = 112) exercised for longer under the condition of motivational music as opposed to oudeterous music (the club’s typical output); however, no differences were noted in terms of affective response. (Jun 2004)David Lloyd Leisure (part of Whitbread PLC

Version control software in the open source process: A performative view of learning and organizing in the Linux collectif.

This research describes a study of learning and organizing within the Linux kernel open source collective. For its empirical focus it concentrates on Linux kernel development activities and this collective's debates about the role of, and need for, an agreed approach to version control software. This is studied over a period of eight years from 1995-2003. A textual analysis of messages in the Linux Kernel mailing list is used as the primary data source, supported by other contemporary accounts. In this work learning and organizing are understood to be mutually constitutive, where one entails and enables the other. Learning is about interacting with the environment, organizing is about reflecting this in the collective. The thesis uses the theoretical approach of actor network theory, Bateson's levels of learning and Weick's concept of organizing, to analyze learning and organizing in the kernel collective. The analysis focuses on the discourse and interplay between relevant actors (human and non-human), and the ongoing debates among kernel developers over whether to use version control software, and then which version control software to adopt. The persistence and passion of this debate (it spans the 8 years studied and is ongoing) is evident, and allows a longitudinal study of the becoming of learning and organizing. Drawing on actor network theory, the thesis emphasizes the performative (worked out, lived, 'in the doing of', in other words the becoming) character of learning and organizing. The findings of the study reveal how learning is understood in the collective and is, to a degree, reflected in its organizing activity. Key themes that emerge include: the organizing of time and space, maintaining of transparency and the overall concern with sustaining the assemblage. The thesis offers a distinctive account of technical actors as an essential part of the open source process. In conclusion, it re-emphasizes the significance of code and the agency of non-human actors

Characteristics and effects of motivational music in exercise

The research programme had three principal objectives. First, the evaluation and extension of the extant conceptual framework pertaining to motivational music in exercise settings. Second, the development of a valid instrument for assessing the motivational qualities of music: The Brunel Music Rating Inventory-2 (BMRI-2). Third, to test the effects of motivational and oudeterous (lacking in both motivational and de-motivational qualities) music in an externally-valid setting. These objectives were addressed through 4 studies. First, a series of open-ended interviews were conducted with exercise leaders and participants (N = 13), in order to investigate the characteristics and effects of motivational music in the exercise setting. The data were content analysed to abstract thematic categories of response. These categories were subsequently evaluated in the context of relevant conceptual frameworks. Subsequently, a sample of 532 health-club members responded to a questionnaire that was designed to assess the perceived characteristics of motivational music. The responses were analysed across age groups, gender, frequency of attendance (low, medium, high), and time of attendance (morning, afternoon, evening). The BMRI-2 was developed in order to address psychometric weaknesses that were associated with its forbear, the BMRI. A refined item pool was created which yielded an 8-item instrument that was subjected to confirmatory factor analysis. A single-factor model demonstrated acceptable fit indices across three different pieces of music, two samples of exercise participants, and both sexes. The BMRI-2 was used to select 20 pieces of motivational music, which were delivered in a health club gymnasium. It was found that health club members (N = 112) exercised for longer under the condition of motivational music as opposed to oudeterous music (the club’s typical output); however, no differences were noted in terms of affective response. (Jun 2004)EThOS - Electronic Theses Online ServiceDavid Lloyd Leisure (part of Whitbread plc)GBUnited Kingdo

Teaching to Learn ~ Learning to Teach Classroom Research Tools for Second Language Learning: A Case Study Based on English as a Foreign Language (EFL) and Spanish as a Foreign Language (SFL) in a Secondary School and a University in Ontario, Canada

Universidad de Valladolid. Facultad de Filosofía y LetrasDepartamento de Filología Ingles