Search for dissertations about: "Machine Learning"

Showing result 1 - 5 of 349 swedish dissertations containing the words Machine Learning.

  1. 1. Modularization of the Learning Architecture Supporting Learning Theories by Learning Technologies

    University dissertation from Stockholm : KTH

    Author : Fredrik Paulsson; Yngve Sundblad; Miguel Angel-Cecilia; [2008]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Computer Science; Technology Enhanced Learning; e-learning; Semantic Web; Service Orientation; Learning Object; Virtual Learning Environment; TECHNOLOGY Information technology Computer science; TEKNIKVETENSKAP Informationsteknik Datavetenskap;

    Abstract : This thesis explores the role of modularity for achieving a better adaptation of learning technology to pedagogical requirements. In order to examine the interrelations that occur between pedagogy and computer science, a theoretical framework rooted in both fields is applied. READ MORE

  2. 2. Using Learning Analytics to Understand and Support Collaborative Learning

    University dissertation from Stockholm : Department of Computer and Systems Sciences, Stockholm University

    Author : Mohammed Saqr; Uno Fors; Jalal Nouri; Barbara Wasson; [2018]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Learning analytics; Social Network Analysis; Collaborative Learning; Medical Education; Interaction Analysis; Machine Learning; informationssamhället; Information Society;

    Abstract : Learning analytics (LA) is a rapidly evolving research discipline that uses insights generated from data analysis to support learners and optimize both the learning process and learning environment. LA is driven by the availability of massive data records regarding learners, the revolutionary development of big data methods, cheaper and faster hardware, and the successful implementation of analytics in other domains. READ MORE

  3. 3. Representation learning for natural language

    University dissertation from Gothenburg : Chalmers tekniska högskola

    Author : Olof Mogren; [2018]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; artificial neural networks; artificial intelligence; natural language processing; deep learning; machine learning; summarization; representation learning;

    Abstract : Artificial neural networks have obtained astonishing results in a diverse number of tasks. One of the reasons for the success is their ability to learn the whole task at once (endto-end learning), including the representations for data. READ MORE

  4. 4. Understanding Complex Diseases and Disease Causative Agents The Machine Learning way

    University dissertation from Uppsala : Acta Universitatis Upsaliensis

    Author : Zeeshan Khaliq; Jan Komorowski; Steven Bosinger; [2017]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Pathogens; Influenza A viruses; Human immunodeficiency virus; Simian immunodeficiency virus; Pathogenicity; Cancer; long noncoding RNAs; Machine learning; Host specificity; Host-specific signatures; Bioinformatics; Bioinformatik;

    Abstract : Diseases can be caused by foreign agents – pathogens – such as viruses, bacteria and other parasites, entering the body or by an internal malfunction of the body itself. The partial understanding of diseases like cancer and the ones caused by viruses, like the influenza A viruses (IAVs) and the human immunodeficiency virus, means we still do not have an efficient cure or defence against them. READ MORE

  5. 5. Manifold Learning in Computational Biology

    University dissertation from Centre for Mathematical Sciences, Lund University

    Author : Jens Nilsson; [2008]
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Nonlinear Dimensionality Reduction; Gene Expression Data; Computational Biology; Machine Learning; Manifold Learning;

    Abstract : This thesis deals with manifold learning techniques and their application in gene expression data analysis. Manifold learning is the study of methods that aim to infer geometrical structure from data sampled from manifolds, enabling nonlinear solutions to various machine learning tasks. READ MORE