Model averaging and ensemble methods for risk corporate estimation - Silvia Figini, Marika Vezzoli. September, 18 2013

Este documento resume una propuesta de intervención durante el Practicum I para estudiantes de la Mención TICE en la Facultad de Educación de Toledo. El objetivo es conocer y reflexionar sobre el uso de las TIC en la organización del centro, la docencia y el aprendizaje del alumnado. Se describe el contexto de la práctica en un colegio público de primaria en Toledo y se analizan aspectos como los recursos TIC disponibles, su uso por el profesorado y alumnado, y la formación del profesorado.

Valoración del practicumtice (1)

Este documento presenta un análisis de las fortalezas, debilidades, oportunidades y amenazas (DAFO) relacionadas con el uso de las tecnologías de la información y la comunicación (TIC) en un colegio público español. Se identifican algunas fortalezas como recursos tecnológicos básicos y el interés del alumnado en las TIC. Sin embargo, también se señalan debilidades como la falta de formación del profesorado y la escasa integración de las TIC en la enseñanza. El an

Valoración del practicumtice

Este documento resume un caso de estudio sobre el uso de las TIC en un aula de primer grado de primaria. Presenta información sobre el contexto de la escuela y el aula, incluyendo que los estudiantes de sexto grado tienen acceso a computadoras portátiles. Describe las herramientas TIC utilizadas, como computadoras de escritorio y pizarras digitales, y las actividades didácticas realizadas con ellas. También resume las perspectivas del maestro y los estudiantes sobre el uso de las TIC, encontrando que si

Ple pilar

Pecha Kucha

Hannahfaith__

Movie marker

María R. Guijarro Domínguez

Movie Maker es un software de edición de video creado por Microsoft en 2000 que permite importar imágenes, video y audio para crear videos educativos. Tiene ventajas como la facilidad de uso para trabajos sencillos pero también desventajas como ser lento para archivos pesados y solo poder ver el video creado en Windows Media Player. Se propone su uso didáctico en el aula desde edades tempranas para motivar e incentivar el aprendizaje de los alumnos de forma visual y complementaria.

Gestión de personas

El objetivo de la gestión de personas es dotar a las empresas de las plantillas adecuadas, no sólo cuantitativamente, sino también cualitativamente, para la consecución de sus objetivos, tanto actuales como del medio y largo plazo. Las herramientas de gestión de personas deben aplicarse no sólo al personal contratado de la empresa, sino también a los propietarios de las mismas que trabajen en ellas, en el caso de empresas familiares y a los colaboradores externos. Igualmente se debe actuar con y aplicar herramientas de gestión de personas a propietarios de empresas familiares que no trabajen en las mismas, siempre que fuese necesario para garantizar el buen funcionamiento de la empresa, la consecución de sus objetivos y compatibilizar y coordinar todo ello con los intereses de la propiedad

El documento resume los principales programas y recursos tecnológicos educativos mencionados, incluyendo PowerPoint, Photoshop, Jclic, Nero, Frontpage, pizarra digital interactiva, y libro virtual. Para cada recurso, describe brevemente sus ventajas y desventajas para su uso en el aula desde la perspectiva del autor. El autor concluye que una combinación efectiva involucraría el uso de tanto recursos digitales como tradicionales de papel.

Práctica expresión plástica_pilar.garcía

History of the music industry

mediamahfuzur

The document provides a history of the music industry from the late 1800s to the 1990s. It makes the following key points: 1. The music industry began in the late 1800s with Thomas Edison's invention of the phonograph, which could record and play back sound. World War II boosted the industry as vinyl records became a way to distribute music to troops. 2. The 1940s-1950s saw the rise of genres like jazz and rock n' roll, which blurred racial lines in music and appealed to the new teenage market. Artists like Elvis Presley helped define rock n' roll and widened the generation gap. 3. The 1960s were the era of bands like The Beat

Ramon van den Akker. Fairness of machine learning models an overview and prac...

Lviv Startup Club

This document discusses fairness in machine learning models. It begins with motivating examples of algorithms that were found to be biased, such as a recidivism prediction tool that was biased against black individuals. It then covers operationalizing fairness through frameworks like transparency and explainability. Finally, it discusses approaches for achieving fairness by design, such as preprocessing the data, adding randomness to predictions, or tailoring new algorithms with fairness constraints. The author notes there are inherent tradeoffs between performance and fairness that require difficult choices.

j.eswa.2019.03.014.pdf

JAHANZAIBALVI3

The document describes a new credit risk modeling technique using a Bayesian network with a latent variable. It introduces a discrete Bayesian network model containing a latent variable that represents different classes of probability distributions for credit risk. The model allows evaluating credit risk and clustering loan subscribers. The document then provides details of the Bayesian network model and proposes a customized Expectation Maximization algorithm to learn the model parameters from data. The model and learning approach are applied to a real loan data set to classify loans and analyze credit risk profiles.

Ensemble models: theory and applications, Figini, Vezzoli. September, 3 2013

Machine Learning and Causal Inference

NBER

This document summarizes a discussion between Susan Athey and Guido Imbens on the relationship between machine learning and causal inference. It notes that while machine learning excels at prediction problems using large datasets, it has weaknesses when it comes to causal questions. Econometrics and statistics literature focuses more on formal theories of causality. The document proposes combining the strengths of both fields by developing machine learning methods that can estimate causal effects, accounting for issues like endogeneity and treatment effect heterogeneity. It outlines some open problems and directions for future research at the intersection of these fields.

A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

Engr Mirza S Hasan

This document lists the academic qualifications of Dr. Mohammad Khasro Miah. It includes two post-doctoral degrees in Human Resource Management from Fulbright Commission, Northeastern University and Japan Society for the Promotion of Science at Nagoya University. It also lists a Ph.D. in Human Resource Management from Nagoya University as well as several Master's degrees and a Bachelor's degree, all related to business or management. The qualifications are submitted to Dr. Mohammad Khasro Miah.

Endogeneity and Entrepreneurship Research

Brian Anderson

This document discusses endogeneity in entrepreneurship research and provides practical tips for addressing it. It begins by defining endogeneity and explaining how it violates assumptions in linear models, resulting in inconsistent estimates. Common sources of endogeneity are discussed, along with myths about how to address it. The gold standard for dealing with endogeneity is randomized experiments, but instrumental variables and selection models are better options for most research. These methods are illustrated using an example looking at the relationship between risk taking and strategic learning. The document stresses the importance of properly specifying and testing for endogeneity, especially in mediation models, to avoid type I and II errors. Strong measurement models and theoretical justification of instruments can also help minimize endogeneity.

doctoral study prospectus9Nature of the StudyTo conduct

DustiBuckner14

doctoral study prospectus 9 Nature of the Study To conduct the current study, qualitative, quantitative, and mixed research approaches were considered. I selected the quantitative method because it helps test hypotheses through a deductive approach. Quantitative method involves measuring constructs through quantitative variables and statistical tools to test hypotheses to address research questions (O'Dwyer & Bernauer, 2016). In contrast, qualitative research is characterized by an inductive approach, where perceptions and subjective experiences of individuals are used to develop themes of a research phenomenon (Östlund et al., 2011). The mixed approach, which involves aspects from both the quantitative and the qualitative approaches, is useful in studies specifically suited for that purpose, as such a combination involves limitations inherent in both approaches (Bryman, 2006). I will not use the qualitative method as the purpose of this study does not require an inductive approach, nor will I use mixed methods approach as the additional requirements for qualitative elements are not necessary in this study. For research design, I considered descriptive design and correlational design. I selected the correlational design because the purpose of this study involves examining the relationship between variables. Correlational research design entails the measurement of two or more relevant variables, as well as assessing the relationship between variables (Crawford, 2014). In contrast, descriptive research design is used to gather quantifiable data and describe the nature of a demographic segment (Mertens, 2014). I will not use descriptive research design as the purpose in this study is not to describe the nature of employees at the selected business organization but to examine the relationship between transformational leadership components, namely idealized influence, inspirational motivation, and individualized consideration and employee retention. References Anitha, J., & Begum, F. N. (2016). Role of organisational culture and employee commitment in employee retention. ASBM Journal of Management, 9(1), 17-28. https://www.semanticscholar.org/paper/Role-of-Organisational-Culture-and-Employee-in-Anitha-Begum/78f5caf30944c582f3c1fe4f8ae82f77d6a9cafd Avolio, B., & Bass, B. (2002). Developing potential across a full range of leadership cases on transactional and transformational leadership. Lawrence Erlbaum Associates. Avolio, B., Waldman, D., & Yammarino, F. (1991). Leading in the 1990s: The four I’s of transformational leadership. Journal of European Industrial Training, 15(4), 9-16. https://doi.org/10.1108/03090599110143366 Boamah, S. A., Laschinger, H. K. S., Wong, C., & Clarke, S. (2018). Effect of transformational leadership on job satisfaction and patient safety outcomes. Nursing Outlook, 66(2), 180-189. https://doi.org/10.1016/j.outlook.2017.10.004 Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Quali ...

Data Analysis and Statistics

T.S. Lim

This document provides an overview of data analysis and statistics concepts for a training session. It begins with an agenda outlining topics like descriptive statistics, inferential statistics, and independent vs dependent samples. Descriptive statistics concepts covered include measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), and charts. Inferential statistics discusses estimating population parameters, hypothesis testing, and statistical tests like t-tests, ANOVA, and chi-squared. The document provides examples and online simulation tools. It concludes with some practical tips for data analysis like checking for errors, reviewing findings early, and consulting a statistician on analysis plans.

SPSS statistics - get help using SPSS

csula its training

This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.

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butest

This document summarizes ensemble classification methods including bagging, boosting, and random forests. It discusses discriminative vs generative models and reviews literature on various machine learning algorithms. It provides details on bagging, boosting, random forests algorithms and compares their pros and cons. It discusses empirical comparisons of algorithm performance on different datasets and problems.

Repurposing Classification & Regression Trees for Causal Research with High-D...

Keynote at WOMBAT 2019 (Monash University) https://www.monash.edu/business/wombat2019 Abstract: Studying causal effects and structures is central to research in management, social science, economics, and other areas, yet typical analysis methods are designed for low-dimensional data. Classification & Regression Trees ("trees") and their variants are popular predictive tools used in many machine learning applications and predictive research, as they are powerful in high-dimensional predictive scenarios. Yet trees are not commonly used in causal-explanatory research. In this talk I will describe adaptations of trees that we developed for tackling two causal-explanatory issues: self selection and confounder detection. For self selection, we developed a novel tree-based approach adjusting for observable self-selection bias in intervention studies, thereby creating a useful tool for analysis of observational impact studies as well as post-analysis of experimental data which scales for big data. For tackling confounders, we repurose trees for automated detection of potential Simpson's paradoxes in data with few or many potential confounding variables, and even with very large samples. I'll also show insights revealed when applying these trees to applications in eGov, labor economics, and healthcare.

Statistical Modeling in 3D: Describing, Explaining and Predicting

This document discusses statistical modeling approaches for explaining, predicting, and describing. It notes that explanatory modeling focuses on testing causal hypotheses, predictive modeling focuses on predicting new observations, and descriptive modeling approximates distributions or relationships. The document argues that these goals are different and the best model for one purpose is not necessarily best for another. It cautions against conflating explanation and prediction, and notes that explanatory power does not necessarily indicate predictive power or vice versa. The document examines differences in how data is approached and models are designed and evaluated for these different purposes.

Regoli fairness deep_learningitalia_20220127

Matteo Testi

1) Fairness in AI is important when algorithms are used to make important decisions that can impact people's lives, such as for loan approvals, hiring, etc. There are various definitions of fairness that can conflict with each other. 2) There are many different metrics for measuring fairness, including demographic parity, equalized odds, and individual fairness. Choosing the appropriate metric depends on the context and goals. 3) Potential mitigation strategies to address bias include preprocessing the data, training fairer models, and postprocessing model outputs. The bank studied credit lending applications and tested various techniques to improve fairness metrics while balancing predictive performance.

PREDICT 422 - Module 1.pptx

VikramKumar790542

This document provides an overview of an introductory machine learning course. The first module will cover basic machine learning concepts, the learning problem, and an introduction to R programming. The goals are to understand supervised vs unsupervised learning, regression vs classification, assessing model accuracy, and familiarity with R. Topics covered include what machine learning is, examples of learning problems, research areas, applications, predicting and inferring relationships from data, and the bias-variance tradeoff in learning algorithms.

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Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12- month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset. Moreover, an experiment was set up to show the importance of oversampling for rare event prediction. The result also shows that Bayesian Network is comparatively more robust than other models without oversampling.

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PM508 - Week 1, Organization Risk Tolerance, Behavior, and Perception

cityuelearning

This document summarizes the key topics covered in a lecture on organizational tolerance to risk and risk management. The lecture discusses how a company's perception of and behavior toward risk is shaped by trial and error learning as well as mental models. It also examines how to analyze a company's risk tolerance by assessing factors within various organizational components and its external environment. Finally, the value of risk management is described as helping organizations become more robust and able to gain from randomness.

Propensity Score Matching Using SAS Enterprise Guide

Ian Morton

The document discusses using propensity score matching to estimate the difference in an outcome between two groups while controlling for biases from differences in their characteristics. It describes a two-step process: 1) using logistic regression to calculate propensity scores representing the probability of being in the treatment group based on background characteristics, and 2) matching individuals in the treatment and control groups based on similar propensity scores. Examples are given of using this method to estimate differences in recidivism rates, medical outcomes, and dropout rates while accounting for differences in offender characteristics, health factors, and country of study.

Algorithmic Bias In Education

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This document summarizes a paper that reviews algorithmic bias in education. It discusses how algorithms used in education can encode biases from their developers or surrounding society, producing discriminatory predictions for some groups. The paper focuses on understanding which groups are impacted and how biases emerge from how variables are operationalized and what data is used. It reviews evidence that algorithms exhibit biases related to race, gender, nationality and other attributes. The paper proposes moving from unknown bias to known bias to fairness, and discusses efforts needed to mitigate algorithmic bias in educational technology.

Alleviating Privacy Attacks Using Causal Models

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Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80\% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks. Paper available at https://arxiv.org/abs/1909.12732

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