274,748 research outputs found
Interactive learning online: Challenges and opportunities
Since the early 1990s online education and online learning systems have held the promise of increasing instructional productivity and reducing costs without sacrificing educational quality. There is no evidence to date that such promise has materialized. The impetus of the newest developments with free online courses to hundreds of thousands of students might drastically transform how we teach more and better with less. The innovation that prompted this panel is called Interactive Learning Online (ILO), and has the distinctive feature of highly interactive, machine-guided instruction that can be scaled to accommodate a large number of students who benefit from targeted and personalized learning. The panelists have experimented with online learning in different ways. Their perspectives will address challenges and opportunities with the adoption of ILO systems
A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species
Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay
Machine Learning Methods in Individual Migration Behavior
Machine learning is described as “a field of computer science that gives a machine the ability to learn”. In fact, machine learning is considered as a sub branch of Artificial Intelligence(AI). In recent years the rise of big data and cloud computing gives AI expert and specifically machine learning expert to dive deeply in data and extract knowledge from it by using machine learning algorithms. In this paper we try to introduce the basic concepts of machine learning algorithms including supervised learning, unsupervised learning and reinforcement learning and its usage in different applications. We describe specifically how to use machine learning in migration process modeling and focus on an approach for migration description, that is based on one of machine learning methods, the decision tree algorithm. We apply this method for the description of the economic behavior of an individual in the question of continuing his work in Russia based on the panel data and the data from the sociological survey. The accuracy of our estimation using decision tree is 67 percent for this specific task. All in all, the main objective of this paper is to introduce the important aspects of machine learning and its usages in the state-of-the-art technologies
Learning from medical data streams: an introduction
Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference
Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes
The application of deep learning algorithms to temporal panel datasets is
difficult due to heavy non-stationarities which can lead to over-fitted models
that under-perform under regime changes. In this work we propose a new machine
learning pipeline for ranking predictions on temporal panel datasets which is
robust under regime changes of data. Different machine-learning models,
including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and
without simple feature engineering are evaluated in the pipeline with different
settings. We find that GBDT models with dropout display high performance,
robustness and generalisability with relatively low complexity and reduced
computational cost. We then show that online learning techniques can be used in
post-prediction processing to enhance the results. In particular, dynamic
feature neutralisation, an efficient procedure that requires no retraining of
models and can be applied post-prediction to any machine learning model,
improves robustness by reducing drawdown in regime changes. Furthermore, we
demonstrate that the creation of model ensembles through dynamic model
selection based on recent model performance leads to improved performance over
baseline by improving the Sharpe and Calmar ratios of out-of-sample prediction
performances. We also evaluate the robustness of our pipeline across different
data splits and random seeds with good reproducibility of results
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
The accurate classification of lymphoma subtypes using hematoxylin and eosin
(H&E)-stained tissue is complicated by the wide range of morphological features
these cancers can exhibit. We present LymphoML - an interpretable machine
learning method that identifies morphologic features that correlate with
lymphoma subtypes. Our method applies steps to process H&E-stained tissue
microarray cores, segment nuclei and cells, compute features encompassing
morphology, texture, and architecture, and train gradient-boosted models to
make diagnostic predictions. LymphoML's interpretable models, developed on a
limited volume of H&E-stained tissue, achieve non-inferior diagnostic accuracy
to pathologists using whole-slide images and outperform black box deep-learning
on a dataset of 670 cases from Guatemala spanning 8 lymphoma subtypes. Using
SHapley Additive exPlanation (SHAP) analysis, we assess the impact of each
feature on model prediction and find that nuclear shape features are most
discriminative for DLBCL (F1-score: 78.7%) and classical Hodgkin lymphoma
(F1-score: 74.5%). Finally, we provide the first demonstration that a model
combining features from H&E-stained tissue with features from a standardized
panel of 6 immunostains results in a similar diagnostic accuracy (85.3%) to a
46-stain panel (86.1%).Comment: To be published in Proceedings of the 3rd Machine Learning for Health
symposium, Proceedings of Machine Learning Research (PMLR
Econometrics of Machine Learning Methods in Economic Forecasting
This paper surveys the recent advances in machine learning method for
economic forecasting. The survey covers the following topics: nowcasting,
textual data, panel and tensor data, high-dimensional Granger causality tests,
time series cross-validation, classification with economic losses
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