Relevant bibliographies by topics / Applied learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Applied learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Applied learning"

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Rudrappa, Gujanatti. "Machine Learning Models Applied for Rainfall Prediction." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 179–87. http://dx.doi.org/10.47059/revistageintec.v11i3.1926.

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Yusfiani, Marnida. "Teacher performance on students learning outcomes in applied chemistry." Jurnal Pendidikan Kimia 12, no. 1 (April 25, 2020): 20–25. http://dx.doi.org/10.24114/jpkim.v12i1.17709.

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Israel, Steven A., Philip Sallee, Franklin Tanner, Jonathan Goldstein, and Shane Zabel. "Applied Machine Learning Strategies." IEEE Potentials 39, no. 3 (May 2020): 38–42. http://dx.doi.org/10.1109/mpot.2019.2927899.

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Pehrson, Lea, Carsten Lauridsen, and Michael Nielsen. "Machine learning and deep learning applied in ultrasound." Ultraschall in der Medizin - European Journal of Ultrasound 39, no. 04 (August 2018): 379–81. http://dx.doi.org/10.1055/a-0642-9545.

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Fabián A. Aldana Herrera et al.,, Fabián A. Aldana Herrera et al ,. "Machine Learning Applied to Networking." International Journal of Mechanical and Production Engineering Research and Development 10, no. 6 (2020): 347–52. http://dx.doi.org/10.24247/ijmperddec202039.

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Dr Dara Hamid Mohammed. "APPLIED LINGUISTICS AND LANGUAGE LEARNING." Researchers World : Journal of Arts, Science and Commerce VIII, no. 3(1) (July 1, 2017): 123–28. http://dx.doi.org/10.18843/rwjasc/v8i3(1)/18.

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Saravanan, T. "ICT Applied Cooperative Learning Environment." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (August 30, 2017): 1630–35. http://dx.doi.org/10.22214/ijraset.2017.8231.

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Lipovetsky, Stan, and Jong-Min Kim. "Machine Learning in Applied Statistics." Model Assisted Statistics and Applications 12, no. 3 (August 31, 2017): 193–94. http://dx.doi.org/10.3233/mas-170404.

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Stewart, Jim. "Work-applied learning for change." Action Learning: Research and Practice 12, no. 2 (May 4, 2015): 244–47. http://dx.doi.org/10.1080/14767333.2015.1049461.

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Santos, Denise. "Conceptualising ‘Learning’ in Applied Linguistics." System 39, no. 4 (December 2011): 576–78. http://dx.doi.org/10.1016/j.system.2011.10.005.

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Dissertations / Theses on the topic "Applied learning"

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Mauricio, Palacio Sebastián. "Machine-Learning Applied Methods." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669286.

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The presented discourse followed several topics where every new chapter introduced an economic prediction problem and showed how traditional approaches can be complemented with new techniques like machine learning and deep learning. These powerful tools combined with principles of economic theory is highly increasing the scope for empiricists. Chapter 3 addressed this discussion. By progressively moving from Ordinary Least Squares, Penalized Linear Regressions and Binary Trees to advanced ensemble trees. Results showed that ML algorithms significantly outperform statistical models in terms of predictive accuracy. Specifically, ML models perform 49-100\% better than unbiased methods. However, we cannot rely on parameter estimations. For example, Chapter 4 introduced a net prediction problem regarding fraudulent property claims in insurance. Despite the fact that we got extraordinary results in terms of predictive power, the complexity of the problem restricted us from getting behavioral insight. Contrarily, statistical models are easily interpretable. Coefficients give us the sign, the magnitude and the statistical significance. We can learn behavior from marginal impacts and elasticities. Chapter 5 analyzed another prediction problem in the insurance market, particularly, how the combination of self-reported data and risk categorization could improve the detection of risky potential customers in insurance markets. Results were also quite impressive in terms of prediction, but again, we did not know anything about the direction or the magnitude of the features. However, by using a Probit model, we showed the benefits of combining statistic models with ML-DL models. The Probit model let us get generalizable insights on what type of customers are likely to misreport, enhancing our results. Likewise, Chapter 2 is a clear example of how causal inference can benefit from ML and DL methods. These techniques allowed us to capture that 70 days before each auction there were abnormal behaviors in daily prices. By doing so, we could apply a solid statistical model and we could estimate precisely what the net effect of the mandated auctions in Spain was. This thesis aims at combining advantages of both methodologies, machine learning and econometrics, boosting their strengths and attenuating their weaknesses. Thus, we used ML and statistical methods side by side, exploring predictive performance and interpretability. Several conditions can be inferred from the nature of both approaches. First, as we have observed throughout the chapters, ML and traditional econometric approaches solve fundamentally different problems. We use ML and DL techniques to predict, not in terms of traditional forecast, but making our models generalizable to unseen data. On the other hand, traditional econometrics has been focused on causal inference and parameter estimation. Therefore, ML is not replacing traditional techniques, but rather complementing them. Second, ML methods focus in out-of-sample data instead of in-sample data, while statistical models typically focus on goodness-of-fit. It is then not surprising that ML techniques consistently outperformed traditional techniques in terms of predictive accuracy. The cost is then biased estimators. Third, the tradition in economics has been to choose a unique model based on theoretical principles and to fit the full dataset on it and, in consequence, obtaining unbiased estimators and their respective confidence intervals. On the other hand, ML relies on data driven selection models, and does not consider causal inference. Instead of manually choosing the covariates, the functional form is determined by the data. This also translates to the main weakness of ML, which is the lack of inference of the underlying data-generating process. I.e. we cannot derive economically meaningful conclusions from the coefficients. Focusing on out-of-sample performance comes at the expense of the ability to infer causal effects, due to the lack of standard errors on the coefficients. Therefore, predictors are typically biased, and estimators may not be normally distributed. Thus, we can conclude that in terms of out-sample performance it is hard to compete against ML models. However, ML cannot contend with the powerful insights that the causal inference analysis gives us, which allow us not only to get the most important variables and their magnitude but also the ability to understand economic behaviors.
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Galimberti, Jaqueson Kingeski. "Adaptive learning for applied macroeconomics." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-learning-for-applied-macroeconomics(cde517d7-d552-4a53-a442-c584262c3a8f).html.

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The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms to depict the evolution of agents' beliefs over time. In this thesis we assess this practice from an applied perspective, focusing on the use of such algorithms for the computation of forecasts of macroeconomic variables. Our analysis develops around three issues we find to have been previously neglected in the literature: (i) the initialization of the learning algorithms; (ii) the determination and calibration of the learning gains, which are key parameters of the algorithms' specifications; and, (iii) the choice of a representative learning mechanism. In order to approach these issues we establish an estimation framework under which we unify the two main algorithms considered in this literature, namely the least squares and the stochastic gradient algorithms. We then propose an evaluation framework that mimics the real-time process of expectation formation through learning-to-forecast exercises. To analyze the quality of the forecasts associated to the learning approach, we evaluate their forecasting accuracy and resemblance to surveys, these latter taken as proxy for agents' expectations. In spite of taking these two criteria as mutually desirable, it is not clear whether they are compatible with each other: whilst forecasting accuracy represents the goal of optimizing agents, resemblance to surveys is indicative of actual agents behavior. We carry out these exercises using real-time quarterly data on US inflation and output growth covering a broad post-WWII period of time. Our main contribution is to show that a proper assessment of the adaptive learning approach requires going beyond the previous views in the literature about these issues. For the initialization of the learning algorithms we argue that such initial estimates need to be coherent with the ongoing learning process that was already in place at the beginning of our sample of data. We find that the previous initialization methods in the literature are vulnerable to this requirement, and propose a new smoothing-based method that is not prone to this critic. Regarding the learning gains, we distinguish between two possible rationales to its determination: as a choice of agents; or, as a primitive parameter of agents learning-to-forecast behavior. Our results provide strong evidence in favor of the gain as a primitive approach, hence favoring the use of surveys data for their calibration. In the third issue, about the choice of a representative algorithm, we challenge the view that learning should be represented by only one of the above algorithms; on the basis of our two evaluation criteria, our results suggest that using a single algorithm represents a misspecification. That motivate us to propose the use of hybrid forms of the LS and SG algorithms, for which we find favorable evidence as representatives of how agents learn. Finally, our analysis concludes with an optimistic assessment on the plausibility of adaptive learning, though conditioned to an appropriate treatment of the above issues. We hope our results provide some guidance on that respect.
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Serafini, Sara. "Machine Learning applied to OCR tasks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The content of this thesis describes the work done during a six-month internship at Datalogic, in its research laboratories in Pasadena (CA). The aim of my research was to implement and evaluate a classifier as part of an industrial OCR system for learning purposes and to see how well it could work in comparison to current best Datalogic products, since it might be simpler/faster, it might be a good alternative for implementing on an embedded system (where current Datalogic products may not be able to run fast enough).
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Mohd, Nawi Abdullah. "Applied Drama in English Language Learning." Thesis, University of Canterbury. School of Literacies and Arts in Education, 2014. http://hdl.handle.net/10092/9584.

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This thesis is a reflective exploration of the use and impact of using drama pedagogies in the English as a Second Language (ESL)/ English as a Foreign Language (EFL) classroom. It stems from the problem of secondary school English language learning in Malaysia, where current teaching practices appear to have led to the decline of the standard of English as a second language in school leavers and university graduates (Abdul Rahman, 1997; Carol Ong Teck Lan, Anne Leong Chooi Khaun, & Singh, 2011; Hazita et al., 2010; Nalliah & Thiyagarajah, 1999). This problem resonates with my own experiences at school, as a secondary school student, an ESL teacher and, later, as a teacher trainer. Consequently, these experiences led me to explore alternative or supplementary teaching methodologies that could enhance the ESL learning experience, drawing initially from drama techniques such as those advocated by Maley and Duff (1983), Wessels (1987), and Di Pietro (1983), and later from process drama pedagogies such as those advocated by Greenwood (2005); Heathcote and Bolton (1995); Kao and O'Neill (1998), and Miller and Saxton (2004). This thesis is an account of my own exploration in adapting drama pedagogies to ESL/EFL teaching. It examines ways in which drama pedagogies might increase motivation and competency in English language learning. The main methodology of the study is that of reflective practice (e.g. Griffiths & Tann, 1992; Zeichner & Liston, 1996). It tracks a learning journey, where I critically reflect on my learning, exploring and implementing such pedagogical approaches as well as evaluate their impact on my students’ learning. These critical reflections arise from three case studies, based on three different contexts: the first a New Zealand English for Speakers of Other Languages (ESOL) class in an intermediate school, the second a Malaysian ESL class in a rural secondary school, and the third an English proficiency class of adult learners in a language school. Data for the study were obtained through the following: research journal and reflective memo; observation and field notes; interview; social media; students’ class work; discussion with co-researchers; and through the literature of the field. A major teaching methodology that emerges from the reflective cycles is that of staging the textbook, where the textbook section to be used for the teaching programme is distilled, and the key focuses of the language, skills, vocabulary, and themes to be learnt are identified and extracted. A layer of drama is matched with these distilled elements and then ‘staged’ on top of the textbook unit, incorporating context-setting opportunities, potential for a story, potential for tension or complication, and the target language elements. The findings that emerge through critical reflection in the study relate to the drama methodologies that I learn and acquire, the impact of these methodologies on students, the role of culture in the application of drama methodologies, and language learning and acquisition. These findings have a number of implications. Firstly, they show how an English Language Teaching (ELT) practitioner might use drama methodologies and what their impact is on student learning. While the focus is primarily on the Malaysian context, aspects of the findings may resonate internationally. Secondly, they suggest a model of reflective practice that can be used by other ELT practitioners who are interested in using drama methodologies in their teaching. Thirdly, these findings also point towards the development of a more comprehensive syllabus for using drama pedagogies, as well as the development of reflective practice, in the teacher training programmes in Malaysia. The use of drama pedagogies for language learning is a field that has not been researched in a Malaysian context. Therefore, this account of reflective practice offers a platform for further research and reflection in this context.
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Drake, Adam C. "Practical Improvements in Applied Spectral Learning." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2546.

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Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to prove many interesting learnability results. These algorithms have also been successfully used in real-world applications. However, previous work has left open many questions about how to best use these methods in real-world learning scenarios. This dissertation presents several significant advances in real-world spectral learning. It presents new algorithms for finding large spectral coefficients (a key sub-problem in spectral learning) that allow spectral learning methods to be applied to much larger problems and to a wider range of problems than was possible with previous approaches. It presents an empirical comparison of new and existing spectral learning methods, showing among other things that the most common approach seems to be the least effective in typical real-world settings. It also presents a multi-spectrum learning approach in which a learner makes use of multiple representations when training. Empirical results show that a multi-spectrum learner can usually match or exceed the performance of the best single-spectrum learner. Finally, this dissertation shows how a particular application, sentiment analysis, can benefit from a spectral approach, as the standard approach to the problem is significantly improved by incorporating spectral features into the learning process.
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Domeniconi, Federico. "Deep Learning Techniques applied to Photometric Stereo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20031/.

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La tesi si focalizza sullo studio dello stato dell’arte della fotometria stereo con deep learning: Self-calibrating Deep Photometric Stereo Networks. Il modello è composto è composto di due reti, la prima predice la direzione e l’intensità delle luci, la seconda predice le normali della superficie. L’obiettivo della tesi è individuare i limiti del modello e capire se possa essere modifcato per avere buone prestazioni anche in scenari reali. Il progetto di tesi è basato su fine-tuning, una tecnica supervisionata di transfer learning. Per questo scopo un nuovo dataset è stato creato acquisendo immagini in laboratorio. La ground-truth è ottenuta tramite una tecnica di distillazione. In particolare la direzione delle luci è ottenuta utilizzando due algoritmi di calibrazione delle luci e unendo i due risultati. Analogamente le normali delle superfici sono ottenute unendo i risultati di vari algoritmi di fotometria stereo. I risultati della tesi sono molto promettenti. L’errore nella predizione della direzione e dell’intensità delle luci è un terzo dell’errore del modello originale. Le predizioni delle normali delle superfici possono essere analizzate solo qualitativamente, ma i miglioramenti sono evidenti. Il lavoro di questa tesi ha mostrato che è possibile applicare transfer-learning alla fotometria stereo con deep learning. Perciò non è necessario allenare un nuovo modello da zero ma è possibile approfittare di modelli già esistenti per migliorare le prestazioni e ridurre il tempo di allenamento.
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Weintraub, Ben Julian. "Learning control applied to a model helicopter." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/49921.

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Idowu, Samuel O. "Applied Machine Learning in District Heating System." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-68486.

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In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption. This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings. The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine,  feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.
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Martin, del Campo Barraza Sergio. "Unsupervised feature learning applied to condition monitoring." Doctoral thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63113.

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Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that characterize faults and abnormal operational conditions. Thus, bearings with embedded sensing, analysis and communication capabilities are developed.   However, the analysis of signals from bearings and the surrounding components is a challenging problem due to the high variability and complexity of the systems. For example, machines evolve over time due to wear and maintenance, and the operational conditions typically also vary over time. Furthermore, the variety of fault signatures and failure mechanisms makes it difficult to derive generally useful and accurate models, which enable early detection of faults at reasonable cost. Therefore, investigations of machine learning methods that avoid some of these difficulties by automated on-line adaptation of the signal model are motivated. In particular, can unsupervised feature learning methods be used to automatically derive useful information about the state and operational conditions of a rotating machine? What additional methods are needed to recognize normal operational conditions and detect abnormal conditions, for example in terms of learned features or changes of model parameters?   Condition monitoring systems are typically based on condition indicators that are pre-defined by experts, such as the amplitudes in certain frequency bands of a vibration signal, or the temperature of a bearing. Condition indicators are used to define alarms in terms of thresholds; when the indicator is above (or below) the threshold, an alarm indicating a fault condition is generated, without further information about the root cause of the fault. Similarly, machine learning methods and labeled datasets are used to train classifiers that can be used for the detection of faults. The accuracy and reliability of such condition monitoring methods depends on the type of condition indicators used and the data considered when determining the model parameters. Hence, this approach can be challenging to apply in the field where machines and sensor systems are different and change over time, and parameters have different meaning depending on the conditions. Adaptation of the model parameters to each condition monitoring application and operational condition is also difficult due to the need for labeled training data representing all relevant conditions, and the high cost of manual configuration. Therefore, neither of these solutions is viable in general.   In this thesis I investigate unsupervised methods for feature learning and anomaly detection, which can operate online without pre-training with labeled datasets. Concepts and methods for validation of normal operational conditions and detection of abnormal operational conditions based on automatically learned features are proposed and studied. In particular, dictionary learning is applied to vibration and acoustic emission signals obtained from laboratory experiments and condition monitoring systems. The methodology is based on the assumption that signals can be described as a linear superposition of noise and learned atomic waveforms of arbitrary shape, amplitude and position. Greedy sparse coding algorithms and probabilistic gradient methods are used to learn dictionaries of atomic waveforms enabling sparse representation of the vibration and acoustic emission signals. As a result, the model can adapt automatically to different machine configurations, and environmental and operational conditions with a minimum of initial configuration. In addition, sparse coding results in reduced data rates that can simplify the processing and communication of information in resource-constrained systems.   Measures that can be used to detect anomalies in a rotating machine are introduced and studied, like the dictionary distance between an online propagated dictionary and a set of dictionaries learned when the machine is known to operate in healthy conditions. In addition, the possibility to generalize a dictionary learned from the vibration signal in one machine to another similar machine is studied in the case of wind turbines.   The main contributions of this thesis are the extension of unsupervised dictionary learning to condition monitoring for anomaly detection purposes, and the related case studies demonstrating that the learned features can be used to obtain information about the condition. The cases studies include vibration signals from controlled ball bearing experiments and wind turbines; and acoustic emission signals from controlled tensile strength tests and bearing contamination experiments. It is found that the dictionary distance between an online propagated dictionary and a baseline dictionary trained in healthy conditions can increase up to three times when a fault appears, without reference to kinematic information like defect frequencies. Furthermore, it is found that in the presence of a bearing defect, impulse-like waveforms with center frequencies that are about two times higher than in the healthy condition are learned. In the case of acoustic emission analysis, it is shown that the representations of signals of different strain stages of stainless steel appear as distinct clusters. Furthermore, the repetition rates of learned acoustic emission waveforms are found to be markedly different for a bearing with and without particles in the lubricant, especially at high rotational speed above 1000 rpm, where particle contaminants are difficult to detect using conventional methods. Different hyperparameters are investigated and it is found that the model is useful for anomaly detection with as little as 2.5 % preserved coefficients.
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Wang, Shihai. "Boosting learning applied to facial expression recognition." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511940.

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Books on the topic "Applied learning"

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Michelucci, Umberto. Applied Deep Learning. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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Forsyth, David. Applied Machine Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7.

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Michelucci, Umberto. Advanced Applied Deep Learning. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4976-5.

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Conceptualising 'learning' in applied linguistics. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2010.

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Jean-Paul, Doignon, and Doignon Jean-Paul, eds. Learning spaces: Interdisciplinary applied mathematics. Heidelberg [Germany]: Springer, 2011.

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Seedhouse, Paul, Steve Walsh, and Chris Jenks, eds. Conceptualising 'Learning' in Applied Linguistics. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230289772.

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Beysolow II, Taweh. Applied Reinforcement Learning with Python. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5127-0.

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Sternberg, Robert J. Applied intelligence. New York: Cambridge University Press, 2008.

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Thurbin, Patrick J. Management development through applied open learning. Kingston: Kingston Regional Management Centre, 1986.

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Yoshihiko, Hasegawa, and Paul Topon Kumar, eds. Applied genetic programming and machine learning. Boca Raton, Fla: Taylor & Francis, 2009.

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Book chapters on the topic "Applied learning"

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Singh, Nirbhay N., and Ivan L. Beale. "Learning Disabilities." In Applied Clinical Psychology, 525–53. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0983-3_21.

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Pierce, W. David, and Carl D. Cheney. "Applied Behavior Analysis." In Behavior Analysis and Learning, 435–70. Sixth edition. | New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315200682-13.

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Williams, Peter M. "Probabilistic Learning Models." In Applied Logic Series, 117–34. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-017-1586-7_5.

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Forsyth, David. "Learning to Classify." In Applied Machine Learning, 3–19. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_1.

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Braha, Dan, and Oded Maimon. "Adaptive Learning for Successful Design." In Applied Optimization, 365–85. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2872-9_13.

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Forsyth, David. "Learning Sequence Models Discriminatively." In Applied Machine Learning, 333–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_14.

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Michelucci, Umberto. "Computational Graphs and TensorFlow." In Applied Deep Learning, 1–29. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8_1.

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Michelucci, Umberto. "Logistic Regression from Scratch." In Applied Deep Learning, 391–401. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8_10.

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Michelucci, Umberto. "Single Neuron." In Applied Deep Learning, 31–81. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8_2.

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Michelucci, Umberto. "Feedforward Neural Networks." In Applied Deep Learning, 83–136. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8_3.

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Conference papers on the topic "Applied learning"

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Chandnani, Kanchan, Deepti Chavan, Pratiti Desai, and Dhananjay R. Kalbande. "Machine learning applied to human learning." In 2013 Annual IEEE India Conference (INDICON). IEEE, 2013. http://dx.doi.org/10.1109/indcon.2013.6725908.

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Paulus, Sachar, Thomas Smits, Tobias Becht, and Serife Kol. "Ubiquitous Learning Applied to Coding." In ECSEE'18: European Conference of Software Engineering Education 2018. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209087.3209104.

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Pesare, Enrica. "Smart learning environments for social learning." In SAC 2015: Symposium on Applied Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2695664.2696069.

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Abelairas-Etxebarria, Patricia, and Jon Mentxaka. "SOCIAL NETWORKS APPLIED TO UNIVERSITY." In International Conference on Education and New Learning Technologies. IATED, 2017. http://dx.doi.org/10.21125/edulearn.2017.0275.

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Unda, Xavier L., and Valentina Ramos. "EXPECTANCY THEORY APPLIED TO AN EDUCATIONAL CONTEXT: A LONGITUDINAL STUDY APPLIED IN POSTGRADUATE COURSES." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.2027.

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Gamboa, Adriana Xiomara Reyes, Jovani Jimenez Builes, and Harry Puerta Monsalve. "Universal design for learning (UDL) applied to T-learning." In 2017 Computing Conference. IEEE, 2017. http://dx.doi.org/10.1109/sai.2017.8252236.

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Duffany, Jeffrey. "Active Learning Applied to Introductory Programming." In The Thirteenth Latin American and Caribbean Conference for Engineering and Technology. LACCEI, 2015. http://dx.doi.org/10.18687/laccei2015.1.1.246.

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Haupt, Sue Ellen, Jim Cowie, Seth Linden, Tyler McCandless, Branko Kosovic, and Stefano Alessandrini. "Machine Learning for Applied Weather Prediction." In 2018 IEEE 14th International Conference on e-Science (e-Science). IEEE, 2018. http://dx.doi.org/10.1109/escience.2018.00047.

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Karlsen, Robert E., and Gary Witus. "Adaptive learning applied to terrain recognition." In SPIE Defense and Security Symposium, edited by Grant R. Gerhart, Douglas W. Gage, and Charles M. Shoemaker. SPIE, 2008. http://dx.doi.org/10.1117/12.777289.

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Niculescu, Virginia, Dan Suciu, and Darius Bufnea. "Agile principles applied in learning contexts." In ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472673.3473963.

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Reports on the topic "Applied learning"

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Bannister, Joseph, Wei-Min Shen, Joseph Touch, Feili Hou, and Venkata Pingali. Applied Learning Networks (ALN). Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada462328.

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Curnow, Christina, Rebecca Mulvaney, Robert Calderon, Eric Weingart, Kenny Nicely, Heidi Keller-Glaze, and Jon Fallesen. Advanced Learning Theories Applied to Leadership Development. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada462784.

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Brown, David. Machine learning applied to classifying neutron resonances. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1673302.

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Griffiths, M., H. A. J. Russell, and C E Logan. Machine learning applied to geoscience: Geo-referenced character recognition. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/321092.

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Gorshkova, G. N. Distance learning course «Accounting for the direction of Applied Informatics». OFERNIO, June 2021. http://dx.doi.org/10.12731/ofernio.2021.24860.

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Kleinosova, N. P. Distance learning course "Modern applied business packages", training direction 38.03.05"Business Informatics. OFERNIO, June 2018. http://dx.doi.org/10.12731/ofernio.2018.23683.

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Dudnikov, V. Yu. Electronic course for distance learning "Basics of geodesy and topography" (UGSN 21.00.00 "Applied geology, mining, oil and gas and geodesy"). Science and Innovation Center Publishing House, 2016. http://dx.doi.org/10.12731/dudnikov.11082016.22085.

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Groeneveld, Caspar, Elia Kibga, and Tom Kaye. Deploying an e-Learning Environment in Zanzibar: Feasibility Assessment. EdTech Hub, July 2020. http://dx.doi.org/10.53832/edtechhub.0028.

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The Zanzibar Ministry of Education and Vocational Training (MoEVT) and the World Bank (the Bank) approached the EdTech Hub (the Hub) in April 2020 to explore the feasibility of implementing a Virtual Learning Environment (VLE). The Hub was requested to focus primarily on the deployment of a VLE in lower secondary education, and this report consequently focuses primarily on this group. The report is structured in four sections: An introduction to provide the background and guiding principles for the engagement with a short overview of the methodology applied. An analysis of the Zanzibar education system with a particular focus on elements relevant to deploying a VLE. This includes the status of ICT infrastructure, and a summary of the stakeholders who will play a role in using or implementing a VLE. A third section that discusses types of VLEs and content organisation, and their applicability to the Zanzibar ecosystem. A conclusion with recommendations for Zanzibar, including short- and long-term steps. In this collaboration with Zanzibar’s MoEVT, the Hub team sought to understand the purpose of the proposed VLE. Based on discussions and user scenarios, we identified two main education challenges a VLE may help to resolve. In the short term, students cannot go to school during the COVID-19 crisis, but need access to educational content. There is content, but no flexible and versatile platform to disseminate content to all students. In the long term, a mechanism to provide students with access to quality, curriculum-aligned content in school, or remotely, is required.
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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McGregor, Lisa, Sarah Frazer, and Derick Brinkerhoff. Thinking and Working Politically: Lessons from Diverse and Inclusive Applied Political Economy Analysis. RTI Press, April 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0038.2004.

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Political economy analysis (PEA) has emerged as a valuable approach for assessing context and the local systems where international development actors seek to intervene. PEA approaches and tools have grown and adapted over the last 40 years through innovations by donor agencies and practitioners. Our analysis of nine PEAs reveals the following findings: PEAs can make positive contributions to technical interventions; engaging project staff in PEAs increases the likelihood that they will be open to a thinking and working politically mindset and approach; inclusion of gender equity and social inclusion (GESI) in PEAs helps to uncover and address hidden power dynamics; and explicitly connecting PEA findings to project implementation facilitates adaptive management. Implementation lessons learned include careful consideration of logistics, timing, and team members. Our experience and research suggest applied PEAs provide valuable evidence for strengthening evidence-based, adaptive, international development programming. The findings highlight the promise of PEA as well as the need for ongoing learning and research to address continued challenges.
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