This document describes machine learning techniques for linear classification and logistic regression. It discusses parameter learning for probabilistic classification models using maximum likelihood estimation. Gradient ascent is presented as an algorithm for finding the optimal coefficients that maximize the likelihood function through iterative updates of the coefficients in the direction of the gradient. Derivatives of the log-likelihood function are computed to determine the gradient for use in gradient ascent optimization.
This document describes an introduction to machine learning classifiers for sentiment analysis. It discusses linear classifiers that predict the sentiment of text, such as restaurant reviews, as either positive or negative. The classifier learns weighting coefficients for words during training and uses these to calculate an overall score for new text, comparing it to a decision boundary to predict the sentiment class. Predicting class probabilities rather than just labels provides more information about the confidence of predictions. Generalized linear models can learn to estimate these conditional probabilities from training data.
This document discusses machine learning techniques for classification including logistic regression and regularization. It begins with an overview of training and evaluating classifiers. It then covers topics such as overfitting in classification models and how increasing model complexity can lead to overconfident predictions. The document also discusses how regularization helps address overfitting by penalizing large coefficients, balancing model fit and complexity. It provides examples visualizing the effects of regularization on learned logistic regression probabilities.
Solution of ordinary differential equetionArdha Aslam
This document provides the general solution to an ordinary differential equation near x=0. It gives the functions a1(x) and a2(x), their initial values, and their derivatives. By setting the derivatives equal to 0 and solving the resulting identity term by term, it determines coefficients A0, A1, A2, etc. The final general solution is given as an infinite series involving these coefficients.
The document defines x as a random variable that takes on values x1 and x2 with probabilities p and 1-p respectively. It then defines the expected value of x as E(x) = px1 + (1-p)x2 and the variance of x as Var(x) = p(x1 - E(x))^2 + (1-p)(x2 - E(x))^2.
El documento presenta datos sobre la prevalencia del consumo de diferentes sustancias como el tabaco, alcohol y drogas ilícitas en España y Extremadura. Muestra que el consumo de tabaco es mayor en Extremadura que en el promedio nacional y causa aproximadamente 1830 muertes al año en la región. También describe los programas y leyes implementados para reducir la exposición al humo de tabaco y disminuir la prevalencia del consumo, así como los criterios y servicios de tratamiento disponibles.
Daisy Marlow is a freelance stylist and fashion consultant based in London. She has 8 years of experience working for luxury fashion brands, designers, and digital retailers in roles involving editorial styling, production, marketing, and events. Her experience also includes freelance styling for magazines and celebrities, and previous roles as a junior fashion editor and production coordinator at Avenue 32 and as a fashion advisor at Net-A-Porter. She is proficient in relevant computer programs and has a strong understanding of digital marketing, e-commerce, and business principles.
Este documento describe diferentes medicamentos utilizados para retrasar el parto prematuro, incluyendo antagonistas de calcio como el sulfato de magnesio, la isoprupina y la ritodrina, que inhiben la bomba de calcio-magnesio y relajan el miometrio, así como la indometacina y el misoprostol, que actúan sobre el miometrio y maduran el cuello uterino. También se mencionan los ergóticos como la ergonovina y la metilergonovina, así como las prostaglandinas, que causan
This document contains county-level estimates of diagnosed diabetes among adults aged 20 years and older in the United States from 2004 to 2008 according to the Centers for Disease Control and Prevention. The CDC provides percentage estimates of diabetes for each county over this 5-year period to track trends in diabetes diagnoses at the local level.
This document describes an introduction to machine learning classifiers for sentiment analysis. It discusses linear classifiers that predict the sentiment of text, such as restaurant reviews, as either positive or negative. The classifier learns weighting coefficients for words during training and uses these to calculate an overall score for new text, comparing it to a decision boundary to predict the sentiment class. Predicting class probabilities rather than just labels provides more information about the confidence of predictions. Generalized linear models can learn to estimate these conditional probabilities from training data.
This document discusses machine learning techniques for classification including logistic regression and regularization. It begins with an overview of training and evaluating classifiers. It then covers topics such as overfitting in classification models and how increasing model complexity can lead to overconfident predictions. The document also discusses how regularization helps address overfitting by penalizing large coefficients, balancing model fit and complexity. It provides examples visualizing the effects of regularization on learned logistic regression probabilities.
Solution of ordinary differential equetionArdha Aslam
This document provides the general solution to an ordinary differential equation near x=0. It gives the functions a1(x) and a2(x), their initial values, and their derivatives. By setting the derivatives equal to 0 and solving the resulting identity term by term, it determines coefficients A0, A1, A2, etc. The final general solution is given as an infinite series involving these coefficients.
The document defines x as a random variable that takes on values x1 and x2 with probabilities p and 1-p respectively. It then defines the expected value of x as E(x) = px1 + (1-p)x2 and the variance of x as Var(x) = p(x1 - E(x))^2 + (1-p)(x2 - E(x))^2.
El documento presenta datos sobre la prevalencia del consumo de diferentes sustancias como el tabaco, alcohol y drogas ilícitas en España y Extremadura. Muestra que el consumo de tabaco es mayor en Extremadura que en el promedio nacional y causa aproximadamente 1830 muertes al año en la región. También describe los programas y leyes implementados para reducir la exposición al humo de tabaco y disminuir la prevalencia del consumo, así como los criterios y servicios de tratamiento disponibles.
Daisy Marlow is a freelance stylist and fashion consultant based in London. She has 8 years of experience working for luxury fashion brands, designers, and digital retailers in roles involving editorial styling, production, marketing, and events. Her experience also includes freelance styling for magazines and celebrities, and previous roles as a junior fashion editor and production coordinator at Avenue 32 and as a fashion advisor at Net-A-Porter. She is proficient in relevant computer programs and has a strong understanding of digital marketing, e-commerce, and business principles.
Este documento describe diferentes medicamentos utilizados para retrasar el parto prematuro, incluyendo antagonistas de calcio como el sulfato de magnesio, la isoprupina y la ritodrina, que inhiben la bomba de calcio-magnesio y relajan el miometrio, así como la indometacina y el misoprostol, que actúan sobre el miometrio y maduran el cuello uterino. También se mencionan los ergóticos como la ergonovina y la metilergonovina, así como las prostaglandinas, que causan
This document contains county-level estimates of diagnosed diabetes among adults aged 20 years and older in the United States from 2004 to 2008 according to the Centers for Disease Control and Prevention. The CDC provides percentage estimates of diabetes for each county over this 5-year period to track trends in diabetes diagnoses at the local level.
El documento describe cuatro tipos de personas en relación con la tecnología digital: 1) nativos digitales, jóvenes nacidos durante la era digital; 2) inmigrantes digitales, personas mayores que tuvieron que aprender sobre tecnología; 3) el papel del docente inmigrante digital es conocer la tecnología para ayudar a estudiantes; y 4) el alumno digital es aquel que sistematiza conocimientos del profesor y los aplica.
Social Sentiment Indices Powered by X-Scoreskcortis
This paper was presented at The Second International Conference on Big Data, Small Data, Linked Data and Open Data - ALLDATA 2016 (http://www.iaria.org/conferences2016/ALLDATA16.html) in Lisbon, Portugal on 22 February 2016. It was also awarded the "Best Paper" award (http://www.iaria.org/conferences2016/AwardsALLDATA16.html).
The full paper can be found at: http://thinkmind.org/index.php?view=article&articleid=alldata_2016_1_40_90041
Kit de Mídia Conexão Home care, apresenta os espaços publicitários disponíveis no portal Conexão. Para divulgar em Conexão Home Care, fale com a Gente.
Twitter es una red social creada en 2006 que permite compartir mensajes de texto de hasta 140 caracteres. Actualmente tiene más de 300 millones de usuarios activos diarios. Inicialmente se usó para comunicación social pero ahora también se usa con fines profesionales y de información por empresas, clientes y otros. Permite la comunicación en varios idiomas e introduce nuevos términos como tweet, retweet, seguir y hashtags a la jerga digital.
La internet profunda permite mayor privacidad y seguridad al ocultar sitios y páginas protegidas con contraseña. Contiene información que no es indexada por los motores de búsqueda, como enciclopedias y diccionarios que requieren iniciar sesión para acceder a los contenidos. Aunque ofrece más privacidad, también alberga sitios ilegales relacionados con terrorismo y narcotráfico, por lo que se debe tener cuidado de no acceder a información prohibida.
Library outreach program for Al Akhawayn staff membersAziz EL Hassani
Library Outreach Definition
“Outreach is the process whereby a library service investigates the activities of the community it serves and becomes fully involved in supporting community, activities, whether or not centered on library premises.” (Anderson, 2010).
El documento presenta información sobre la Agenda Digital Peruana, incluyendo sus objetivos, líneas de acción y antecedentes. Define la Agenda Digital Peruana como el plan de desarrollo de la sociedad de la información que tiene como objetivo permitir que la sociedad peruana acceda a los beneficios del desarrollo de las tecnologías de la información y la comunicación. Describe brevemente algunos de sus ocho objetivos estratégicos, como desarrollar infraestructura, capacidades humanas, aplicación de TIC en programas sociales, y g
Este documento proporciona información sobre los músculos del cuerpo humano, incluyendo su origen, inserción e inervación. Describe los músculos del hombro, brazo, antebrazo, mano y dedos, y especifica los nervios que inervan cada músculo.
Social Media Listening also known as Social Media Monitoring, is the process of identifying and assessing what is being said about a company, individual, product or brand on the Internet.
El documento describe la estrategia 2 del tercer objetivo de la agenda digital, cuyo fin es fortalecer el acceso a la información y los servicios de justicia mediante el uso de las tecnologías de la información y la comunicación (TIC). Esto requiere dotar a los funcionarios y al sistema de justicia de los medios necesarios para implementar una justicia totalmente informatizada, interconectada e interoperable, desarrollando sistemas que faciliten los procesos administrativos y jurisdiccionales de manera electrónica.
Teoria das Cores Aplicada ao VestuárioDébora Cseri
O documento explica os conceitos básicos do círculo cromático e dos sistemas de cores RGB e CMYK. Ele descreve como as cores primárias, secundárias e terciárias são derivadas no círculo cromático e como as cores são misturadas nos sistemas RGB e CMYK para produzir outras cores ou imagens. O documento também discute conceitos como matiz, intensidade, valor, clareamento, escurecimento e saturação.
Nurse Burnout Acute Care Capstone PresentationKatelyn Duncan
Nurse burnout is a phenomenon where the cumulative effects of a stressful work environment gradually overwhelm nurses, forcing them to withdraw psychologically. It can occur in any nursing setting and is a real problem. Common causes include heavy workloads, long shifts, overtime, lack of support, and high patient needs. Signs are emotional exhaustion, depersonalization, and decreased personal accomplishment. Interventions to prevent burnout include empowerment, social support, reduced hours and overtime, relaxation techniques, and cognitive strategies. While some stress can improve performance, excessive pressure becomes harmful.
Una intoxicación alimentaria es una enfermedad causada por la ingestión de alimentos contaminados con bacterias, virus, parásitos u otras toxinas. Los síntomas comunes incluyen vómitos, dolor abdominal y diarrea. Se estima que cada año 600 millones de personas en todo el mundo enferman debido a alimentos contaminados. La prevención requiere buenas prácticas de higiene en la producción, manipulación y almacenamiento de alimentos.
Pandora is an internet radio service that provides personalized music based on a user's tastes. It was founded in 2000 and went public in 2011. Pandora uses the Music Genome Project which analyzes over 450 musical characteristics of every song to understand how songs are related. Pandora currently has around 80 million monthly active users and generates revenue through advertising, though it is hinting at expanding into on-demand streaming. Pandora's mission is to be the effortless source of personalized music enjoyment and discovery.
Dokumen tersebut membahas skema kerja dalam perencanaan sumber daya manusia (SDM), meliputi analisis strategi perusahaan, analisis jabatan, kebutuhan SDM, ketersediaan SDM, dan kesesuaian antara kebutuhan dan ketersediaan untuk menentukan kebutuhan rekrutmen.
Based on case study of Accor Hotels. It will provide you brief introduction of all category of brands and services as well as history and origin of the Accor Hotels.
Principales novedades del Esquema Nacional de Seguridad (ENS)Miguel A. Amutio
El documento resume las principales novedades en la actualización del Esquema Nacional de Seguridad, incluyendo una mayor énfasis en la gestión continua de la seguridad, la introducción de profesionales cualificados, y mejoras en la gestión de incidentes y auditorías de seguridad. También discute cómo la norma ISO/IEC 27001 puede servir como soporte para el cumplimiento del ENS y los retos continuo de mejorar la ciberseguridad de las administraciones públicas.
This document discusses stochastic gradient descent as an optimization technique for machine learning models. Stochastic gradient descent improves on gradient descent by using mini-batches of training data rather than the full dataset for each model update. This allows the algorithm to scale to massive datasets with billions of examples. While stochastic gradient descent is faster per iteration, it converges more slowly and noisily than batch gradient descent. The document outlines practical techniques for implementing stochastic gradient descent, such as shuffling training data to avoid bias.
The document describes the AdaBoost algorithm for ensemble learning. AdaBoost combines weak learners into a strong learner as follows:
1. It starts by assigning equal weights to all training points.
2. It trains a weak learner on the weighted training data and calculates the learner's weight based on its error rate.
3. It increases the weights of misclassified points and decreases the weights of correctly classified points.
4. It repeats steps 2-3 for a number of iterations, each time focusing the next learner on the points that previous learners misclassified. The final ensemble predicts by taking a weighted vote of the individual learners.
The document discusses ridge regression as a technique for regulating overfitting when using many features in linear regression models. Ridge regression works by adding a penalty term that prefers coefficients with smaller magnitudes to the standard least squares cost function. This has the effect of balancing the model's fit to the training data and the complexity of the model. Ridge regression can be fitted in closed form by minimizing the combined cost function, resulting in a solution that shrinks coefficient estimates toward zero.
El documento describe cuatro tipos de personas en relación con la tecnología digital: 1) nativos digitales, jóvenes nacidos durante la era digital; 2) inmigrantes digitales, personas mayores que tuvieron que aprender sobre tecnología; 3) el papel del docente inmigrante digital es conocer la tecnología para ayudar a estudiantes; y 4) el alumno digital es aquel que sistematiza conocimientos del profesor y los aplica.
Social Sentiment Indices Powered by X-Scoreskcortis
This paper was presented at The Second International Conference on Big Data, Small Data, Linked Data and Open Data - ALLDATA 2016 (http://www.iaria.org/conferences2016/ALLDATA16.html) in Lisbon, Portugal on 22 February 2016. It was also awarded the "Best Paper" award (http://www.iaria.org/conferences2016/AwardsALLDATA16.html).
The full paper can be found at: http://thinkmind.org/index.php?view=article&articleid=alldata_2016_1_40_90041
Kit de Mídia Conexão Home care, apresenta os espaços publicitários disponíveis no portal Conexão. Para divulgar em Conexão Home Care, fale com a Gente.
Twitter es una red social creada en 2006 que permite compartir mensajes de texto de hasta 140 caracteres. Actualmente tiene más de 300 millones de usuarios activos diarios. Inicialmente se usó para comunicación social pero ahora también se usa con fines profesionales y de información por empresas, clientes y otros. Permite la comunicación en varios idiomas e introduce nuevos términos como tweet, retweet, seguir y hashtags a la jerga digital.
La internet profunda permite mayor privacidad y seguridad al ocultar sitios y páginas protegidas con contraseña. Contiene información que no es indexada por los motores de búsqueda, como enciclopedias y diccionarios que requieren iniciar sesión para acceder a los contenidos. Aunque ofrece más privacidad, también alberga sitios ilegales relacionados con terrorismo y narcotráfico, por lo que se debe tener cuidado de no acceder a información prohibida.
Library outreach program for Al Akhawayn staff membersAziz EL Hassani
Library Outreach Definition
“Outreach is the process whereby a library service investigates the activities of the community it serves and becomes fully involved in supporting community, activities, whether or not centered on library premises.” (Anderson, 2010).
El documento presenta información sobre la Agenda Digital Peruana, incluyendo sus objetivos, líneas de acción y antecedentes. Define la Agenda Digital Peruana como el plan de desarrollo de la sociedad de la información que tiene como objetivo permitir que la sociedad peruana acceda a los beneficios del desarrollo de las tecnologías de la información y la comunicación. Describe brevemente algunos de sus ocho objetivos estratégicos, como desarrollar infraestructura, capacidades humanas, aplicación de TIC en programas sociales, y g
Este documento proporciona información sobre los músculos del cuerpo humano, incluyendo su origen, inserción e inervación. Describe los músculos del hombro, brazo, antebrazo, mano y dedos, y especifica los nervios que inervan cada músculo.
Social Media Listening also known as Social Media Monitoring, is the process of identifying and assessing what is being said about a company, individual, product or brand on the Internet.
El documento describe la estrategia 2 del tercer objetivo de la agenda digital, cuyo fin es fortalecer el acceso a la información y los servicios de justicia mediante el uso de las tecnologías de la información y la comunicación (TIC). Esto requiere dotar a los funcionarios y al sistema de justicia de los medios necesarios para implementar una justicia totalmente informatizada, interconectada e interoperable, desarrollando sistemas que faciliten los procesos administrativos y jurisdiccionales de manera electrónica.
Teoria das Cores Aplicada ao VestuárioDébora Cseri
O documento explica os conceitos básicos do círculo cromático e dos sistemas de cores RGB e CMYK. Ele descreve como as cores primárias, secundárias e terciárias são derivadas no círculo cromático e como as cores são misturadas nos sistemas RGB e CMYK para produzir outras cores ou imagens. O documento também discute conceitos como matiz, intensidade, valor, clareamento, escurecimento e saturação.
Nurse Burnout Acute Care Capstone PresentationKatelyn Duncan
Nurse burnout is a phenomenon where the cumulative effects of a stressful work environment gradually overwhelm nurses, forcing them to withdraw psychologically. It can occur in any nursing setting and is a real problem. Common causes include heavy workloads, long shifts, overtime, lack of support, and high patient needs. Signs are emotional exhaustion, depersonalization, and decreased personal accomplishment. Interventions to prevent burnout include empowerment, social support, reduced hours and overtime, relaxation techniques, and cognitive strategies. While some stress can improve performance, excessive pressure becomes harmful.
Una intoxicación alimentaria es una enfermedad causada por la ingestión de alimentos contaminados con bacterias, virus, parásitos u otras toxinas. Los síntomas comunes incluyen vómitos, dolor abdominal y diarrea. Se estima que cada año 600 millones de personas en todo el mundo enferman debido a alimentos contaminados. La prevención requiere buenas prácticas de higiene en la producción, manipulación y almacenamiento de alimentos.
Pandora is an internet radio service that provides personalized music based on a user's tastes. It was founded in 2000 and went public in 2011. Pandora uses the Music Genome Project which analyzes over 450 musical characteristics of every song to understand how songs are related. Pandora currently has around 80 million monthly active users and generates revenue through advertising, though it is hinting at expanding into on-demand streaming. Pandora's mission is to be the effortless source of personalized music enjoyment and discovery.
Dokumen tersebut membahas skema kerja dalam perencanaan sumber daya manusia (SDM), meliputi analisis strategi perusahaan, analisis jabatan, kebutuhan SDM, ketersediaan SDM, dan kesesuaian antara kebutuhan dan ketersediaan untuk menentukan kebutuhan rekrutmen.
Based on case study of Accor Hotels. It will provide you brief introduction of all category of brands and services as well as history and origin of the Accor Hotels.
Principales novedades del Esquema Nacional de Seguridad (ENS)Miguel A. Amutio
El documento resume las principales novedades en la actualización del Esquema Nacional de Seguridad, incluyendo una mayor énfasis en la gestión continua de la seguridad, la introducción de profesionales cualificados, y mejoras en la gestión de incidentes y auditorías de seguridad. También discute cómo la norma ISO/IEC 27001 puede servir como soporte para el cumplimiento del ENS y los retos continuo de mejorar la ciberseguridad de las administraciones públicas.
This document discusses stochastic gradient descent as an optimization technique for machine learning models. Stochastic gradient descent improves on gradient descent by using mini-batches of training data rather than the full dataset for each model update. This allows the algorithm to scale to massive datasets with billions of examples. While stochastic gradient descent is faster per iteration, it converges more slowly and noisily than batch gradient descent. The document outlines practical techniques for implementing stochastic gradient descent, such as shuffling training data to avoid bias.
The document describes the AdaBoost algorithm for ensemble learning. AdaBoost combines weak learners into a strong learner as follows:
1. It starts by assigning equal weights to all training points.
2. It trains a weak learner on the weighted training data and calculates the learner's weight based on its error rate.
3. It increases the weights of misclassified points and decreases the weights of correctly classified points.
4. It repeats steps 2-3 for a number of iterations, each time focusing the next learner on the points that previous learners misclassified. The final ensemble predicts by taking a weighted vote of the individual learners.
The document discusses ridge regression as a technique for regulating overfitting when using many features in linear regression models. Ridge regression works by adding a penalty term that prefers coefficients with smaller magnitudes to the standard least squares cost function. This has the effect of balancing the model's fit to the training data and the complexity of the model. Ridge regression can be fitted in closed form by minimizing the combined cost function, resulting in a solution that shrinks coefficient estimates toward zero.
The document discusses multiple linear regression models for predicting an output variable based on multiple input features. It introduces polynomial regression as a way to fit nonlinear relationships between a single input and output. More complex regression models are described that can incorporate multiple inputs, including basis expansion techniques to transform input features into a higher-dimensional space. Nonlinear and non-parametric functions of the inputs can be modeled to fit complex relationships between features and the target.
The document discusses nearest neighbor and kernel regression methods for nonparametric machine learning models. It introduces 1-nearest neighbor regression, which predicts values based on the single closest data point. The document notes limitations with 1-NN and then describes k-nearest neighbor regression, which bases predictions on the average of the k closest data points. This helps address issues with noise and sparse data regions. Weighted k-nearest neighbor regression is also introduced, which weights closer neighbors more heavily than distant ones. The document provides examples and visualizations of how these different nearest neighbor methods work.
This document provides an overview of machine learning concepts related to linear classifiers and predicting sentiment from text reviews. It discusses logistic regression models for classification, extracting features from text, learning coefficients to predict sentiment probabilities, and using decision boundaries to separate positive and negative predictions. Graphs and equations are presented to illustrate linear classifier models for two classes.
The document discusses assessing the performance of machine learning models. It introduces three types of error: training error, generalization error, and test error. Training error is calculated on the training data used to fit the model, but may be overly optimistic. Generalization error is the expected error on all possible data, but cannot be directly calculated. Test error uses a held-out test set not used in training as an approximation of generalization error. Lower test error indicates better predictive performance on new data.
The document is a presentation on machine learning and simple linear regression. It introduces the concepts of a regression model, fitting a linear regression line to data by minimizing the residual sum of squares, and using the fitted line to make predictions. It discusses representing the linear regression model as an equation relating the output variable (y) to the input or feature (x), with parameters (w0, w1) estimated from training data. The parameters can be estimated by taking the gradient of the residual sum of squares and setting it equal to zero to find the optimal values for w0 and w1 that best fit the data.
This document discusses nearest neighbor algorithms for document retrieval. It describes representing documents as vectors using techniques like TF-IDF and measuring the similarity between documents using distance metrics like Euclidean distance. It then explains how 1-nearest neighbor and k-nearest neighbor algorithms can be used to retrieve the most similar documents to a query document by computing distances and finding the closest neighbors.
This document provides information about an online math homework help service called Homework1. It includes:
- Contact information for Homework1, including their address, phone number, email, and social media links.
- An overview of the services provided, which include math homework help, writing assistance, and teaching students the solutions to help them learn.
- Several examples and solutions to common math problems to illustrate the type of homework help offered.
08.29.2017 Daily Lesson Properities of Ratioanl Numbers.pptxArianeSantiago7
1. This document contains an agenda for a math lesson on rational numbers including properties of rational numbers. The agenda includes a ticket in the door and out with math problems, reviewing previous lessons, and a current lesson on applying properties of rational numbers.
2. The document defines rational numbers as real numbers that can be written as a ratio of two integers and may be terminating or repeating decimals. It then explains four properties - distributive, commutative, associative, and identity properties of one and zero - and provides examples of each.
3. The exit slip asks students to name properties illustrated in equations and simplify expressions using properties, justifying each step.
Worried about due completion of math homework? You can count on us. In Homework1.com we have excellent infrastructure to serve you even at the most critical hours of assignment submission! Try our service today and get excellent score in math exam.
Looking for best statistics assignment help to complete your statistics project? Contact economicshelpdesk for immediate assistance by our enrolled subject matter experts and secure great grade in your exam. Log on our website to know more details.
This document provides an overview of logistic regression. It discusses the hypothesis representation using a sigmoid function to output probabilities between 0 and 1. It describes using maximum likelihood estimation to learn the parameters θ by minimizing the cost function. Gradient descent is used to optimize the cost function. The document also briefly mentions regularization and multi-class classification extensions.
This document provides an overview of machine learning and artificial intelligence concepts. It begins with definitions of learning and machine learning. Machine learning is described as programming computers to optimize performance using example data. Common machine learning algorithms like linear regression, naive Bayes, decision trees, and k-means clustering are discussed along with applications. Artificial neural networks and the perceptron learning algorithm are explained in detail. The key point is that perceptron learning will always find weights to correctly classify inputs if a linear solution exists.
This document discusses several techniques for evaluating capital budgeting projects: payback period, net present value, discounted payback period, internal rate of return, profitability index, and average rate of return. It provides examples of calculating each of these metrics for two potential projects, Projects X and Y, and compares the results to determine which project has more favorable financial indicators based on the given criteria.
The document provides lessons on complex numbers. It defines a complex number as being of the form z = x + iy, where x and y are real numbers. It discusses operations like addition, subtraction, multiplication and division of complex numbers. It also defines the complex conjugate and gives some examples of performing operations on complex numbers.
Properties of addition and multiplicationShiara Agosto
This document discusses properties of addition and multiplication. It explains the commutative, associative, identity, and distributive properties and provides examples of how each applies to addition and multiplication. Practice problems are included for students to identify which properties are being used and to solve expressions using the properties.
This document provides an overview of machine learning concepts including supervised learning, unsupervised learning, regression, classification, models, cost functions, and gradient descent. It defines machine learning as giving computers the ability to learn without explicit programming. It explains that supervised learning uses labeled training data to predict outputs for new inputs, while unsupervised learning organizes unlabeled data into clusters. Models describe characteristics of training data to predict outputs from inputs. Cost functions measure similarity between predictions and labels, with the goal of minimizing this value through gradient descent, which iteratively updates model parameters proportional to the slope of the cost function.
The document discusses mixed membership models for document clustering. It begins by introducing mixed membership models, which aim to discover multiple cluster memberships for each document rather than assigning documents to a single cluster like traditional clustering models. It then provides an example of applying mixed membership models to a sample document, showing how the document could have membership in both a science and technology topic cluster. The document continues building towards introducing Latent Dirichlet Allocation as a technique for mixed membership modeling of documents.
The document describes a mixture model approach to clustering data, specifically clustering images. A mixture model represents the overall data distribution as a weighted combination of Gaussian distributions, with each Gaussian distribution representing a distinct cluster. For images, simple pixel-based features can be modeled as Gaussians per cluster. The Expectation-Maximization algorithm is used to infer soft cluster assignments by computing responsibilities, which provide the probability that each data point belongs to each cluster, given the current model parameters. This allows the model to account for uncertainty in assignments.
The document discusses machine learning clustering techniques. It introduces clustering as a way to group related documents by topic into clusters. It then describes k-means clustering, an algorithm that assigns documents to clusters based on their distance to cluster centers. The k-means algorithm works by iteratively assigning documents to the closest cluster center and updating the cluster centers to be the mean of assigned documents. It converges to a local optimum clustering. The document also discusses evaluating clustering quality and choosing the number of clusters k.
This document proposes a new technique called LIME (Local Interpretable Model-agnostic Explanations) that can explain the predictions of any classifier or regressor in an interpretable and faithful manner. It does this by learning an interpretable model locally around the prediction. It also proposes a method called SP-LIME to select a set of representative individual predictions and their explanations in a non-redundant way to help evaluate whether a model as a whole can be trusted before being deployed. The authors demonstrate LIME on different models for text and image classification and show through experiments that explanations can help humans decide whether to trust a prediction, choose between models, improve an untrustworthy classifier, and identify cases where a classifier should not be
This document provides an overview of a machine learning specialization course on clustering and retrieval. The course covers topics like nearest neighbor search, k-means clustering, mixture models, and latent Dirichlet allocation. It introduces key concepts like retrieval, clustering, and their applications. The course modules cover algorithms and models for nearest neighbors, k-means, mixture models, and latent Dirichlet allocation. The goal is to provide foundational skills in unsupervised learning techniques.
This document discusses using machine learning to classify the sentiment of restaurant reviews in order to find positive quotes to promote a restaurant. It introduces precision and recall as important metrics for this task, as the goal is to find as many positive reviews as possible while minimizing false positives. Precision measures the fraction of positive predictions that are actually positive, while recall measures the fraction of actual positive reviews that are predicted positive. The document shows how varying a classification threshold can trade off between precision and recall, generating a precision-recall curve. Optimizing this tradeoff is important for the goal of finding genuine positive quotes to use in marketing.
This document discusses handling missing data in machine learning models. It presents three main strategies: 1) purification by skipping, which removes data points or features with missing values; 2) purification by imputing, which replaces missing values using techniques like mean imputation; and 3) adapting the learning algorithm to be robust to missing values, such as modifying decision trees to include branches for handling missing data. The document explores techniques within each strategy and discusses their pros and cons.
This document discusses techniques for preventing overfitting in decision trees, including early stopping and pruning. It describes three early stopping conditions for building decision trees: 1) limiting the depth of the tree, 2) stopping if no split causes a sufficient decrease in classification error, and 3) stopping if a node contains too few data points. Early stopping aims to find simpler trees that generalize better. However, early stopping conditions can be imperfect, so the document also introduces pruning as a way to simplify fully-grown trees after learning.
The document describes the process of learning decision trees from data using a greedy algorithm. It explains how a decision tree recursively partitions the data space based on feature values to perform classification. The algorithm works by starting with all the training data at the root node, and then recursively splitting the data into purer child nodes based on feature tests that minimize the classification error at each step. It provides examples of how potential splits are evaluated on different features to select the split that results in the lowest error.
The document discusses feature selection using lasso regression. It explains that lasso regression performs regularization which encourages sparsity to select important features. It explores using lasso regression for applications like housing price prediction and analyzing brain activity data to predict emotional states. The document shows an example of using lasso regression to iteratively fit models with increasing numbers of features selected from a housing dataset to determine the best subset of features.
This document provides an overview of a Machine Learning Specialization course on classification. The course covers classification models like linear classifiers, logistic regression, decision trees and ensembles. It explores algorithms like gradient descent, stochastic gradient descent and boosting. Topics include overfitting, handling missing data, precision-recall and online learning. The course assumes background in calculus, vectors, functions and basic Python programming.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Information and Communication Technology in EducationMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 2)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐈𝐂𝐓 𝐢𝐧 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐞𝐭:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.