Hector Cuesta Arvizu successfully completed an online machine learning course provided by Stanford University through Coursera on December 06, 2012. The course provided a broad introduction to machine learning, data mining, and statistical pattern recognition, covering several supervised and unsupervised learning methods as illustrated through case studies and applications. The statement of accomplishment does not confer an official Stanford University grade, credit, or degree.
Este documento explica cómo programar directamente para la GPU utilizando CUDA. CUDA permite ejecutar kernels (funciones) de forma paralela en miles de hilos simultáneos. Un kernel se divide en una malla de bloques de hilos, donde cada bloque se ejecuta en un procesador de la GPU. El documento incluye un ejemplo Hola Mundo en CUDA y concluye que programar para GPU puede acelerar algoritmos que analizan grandes cantidades de datos.
(Spanish) An overview of machine/statistical learning, what it is, what's it for, and some firms that are using it to drive up revenue and create new products.
This document provides advice on continuing data science and AI education through various options like online courses, academic programs, and professional groups. It acknowledges challenges like a lack of data scientists in Mexico and firms implementing data science projects incorrectly. However, it argues Mexico must become a data science and AI hub to remain economically competitive as automation increases and other countries specialize. Specific recommendations include pursuing a full academic program, building an independent master's with MOOCs, and joining data science communities and groups in Mexico.
Este documento describe y compara dos modelos de comprensión lectora: el modelo ascendente (bottom-up) y el modelo descendente (top-down). El modelo ascendente se centra en la decodificación secuencial de letras, palabras y texto. El modelo descendente considera tanto el texto como los conocimientos y experiencias previas del lector, quien utiliza esta información para anticipar y comprender el significado del texto.
What if we designed our organizations like we design our systems? Applying scalability principles that we know from building large-scale distributed systems, as well as practical lessons learned at eBay and Google, this session covers how we can design and evolve our engineering organizations to scale.
Leading the Data Asset Management Team: CDO or Top Data Job?Data Blueprint
Join Peter Aiken, Ph.D. and Micheline Casey for this interactive discussion on the role of Chief Data Officer (CDO) or Top Data Job (TDJ). While most agree that data challenges are getting – dare we say it, bigger? – the range of approaches reveals no emerging consensus as to the best way to address these challenges. This webinar features a wide-ranging discussion of a number of aspects of this exciting new career path. For each of these aspects, new data leaders can be congratulated but sometimes they also ought to be consoled. Ms. Casey (as the very first state CDO) and Dr. Aiken will bring certain considerations to the table. They hope to sample the pulse of the community and move towards consensus on a number of issues, including:
What is in a name/title?
Who are this individual’s peers?
Where does one obtain the requisite background to qualify?
How does RACI (a responsibility assignment matrix) apply?
When does data influence IT development efforts?
Why are these issues not better understood?
Hector Cuesta Arvizu successfully completed an online machine learning course provided by Stanford University through Coursera on December 06, 2012. The course provided a broad introduction to machine learning, data mining, and statistical pattern recognition, covering several supervised and unsupervised learning methods as illustrated through case studies and applications. The statement of accomplishment does not confer an official Stanford University grade, credit, or degree.
Este documento explica cómo programar directamente para la GPU utilizando CUDA. CUDA permite ejecutar kernels (funciones) de forma paralela en miles de hilos simultáneos. Un kernel se divide en una malla de bloques de hilos, donde cada bloque se ejecuta en un procesador de la GPU. El documento incluye un ejemplo Hola Mundo en CUDA y concluye que programar para GPU puede acelerar algoritmos que analizan grandes cantidades de datos.
(Spanish) An overview of machine/statistical learning, what it is, what's it for, and some firms that are using it to drive up revenue and create new products.
This document provides advice on continuing data science and AI education through various options like online courses, academic programs, and professional groups. It acknowledges challenges like a lack of data scientists in Mexico and firms implementing data science projects incorrectly. However, it argues Mexico must become a data science and AI hub to remain economically competitive as automation increases and other countries specialize. Specific recommendations include pursuing a full academic program, building an independent master's with MOOCs, and joining data science communities and groups in Mexico.
Este documento describe y compara dos modelos de comprensión lectora: el modelo ascendente (bottom-up) y el modelo descendente (top-down). El modelo ascendente se centra en la decodificación secuencial de letras, palabras y texto. El modelo descendente considera tanto el texto como los conocimientos y experiencias previas del lector, quien utiliza esta información para anticipar y comprender el significado del texto.
What if we designed our organizations like we design our systems? Applying scalability principles that we know from building large-scale distributed systems, as well as practical lessons learned at eBay and Google, this session covers how we can design and evolve our engineering organizations to scale.
Leading the Data Asset Management Team: CDO or Top Data Job?Data Blueprint
Join Peter Aiken, Ph.D. and Micheline Casey for this interactive discussion on the role of Chief Data Officer (CDO) or Top Data Job (TDJ). While most agree that data challenges are getting – dare we say it, bigger? – the range of approaches reveals no emerging consensus as to the best way to address these challenges. This webinar features a wide-ranging discussion of a number of aspects of this exciting new career path. For each of these aspects, new data leaders can be congratulated but sometimes they also ought to be consoled. Ms. Casey (as the very first state CDO) and Dr. Aiken will bring certain considerations to the table. They hope to sample the pulse of the community and move towards consensus on a number of issues, including:
What is in a name/title?
Who are this individual’s peers?
Where does one obtain the requisite background to qualify?
How does RACI (a responsibility assignment matrix) apply?
When does data influence IT development efforts?
Why are these issues not better understood?
This document discusses examples of artificial intelligence applications for fintech and retail startups. It summarizes using AI for coaching/advising clients, assessing client risk profiles, valuation models, pricing, credit approval/risk, customer churn/segmentation, contract analysis, and fraud detection. It then provides more detail on using decision trees and random forests for a credit approval classification model, including data acquisition, preprocessing, normalization, model training, evaluation using cross-validation and a confusion matrix, and achieving 87.8% accuracy. It recommends a machine learning book for further learning.
La comparación elástica de imágenes es un problema abierto en la computación. Primero por su complejidad inherente y segundo por la enorme cantidad de imágenes existentes. En el presente artículo, se introduce un método elástico de comparación de imágenes, el cual ofrece una forma no-supervisada de búsqueda de similitud.
Simulación De Enfermedades Infecciosas En Grandes Poblaciones A Través De Un ...Hector Cuesta Arvizu
Simulación De Enfermedades Infecciosas En Grandes Poblaciones A Través De Un Autómata Celular Estocástico Paralelizado Por Gpu Con C-Cuda. Presentado en ISUM 2012 Guanajuato Mexico
Presentacion: Modelado para estudio de brotes epidémicos usando un Autómata C...Hector Cuesta Arvizu
Para la proyección de brotes epidemiológicos se
involucra la aplicación de métodos estocásticos para encontrar
patrones, describir frecuencias de enfermedades que son de
interés del sector salud y para el monitoreo de alertas epidémicas
en la población. En el presente trabajo se propone el uso de un
modelo computacional basado en un Autómata Celular estocástico
global para la simulación del modelo epidemiológico SEIR
(Susceptibles, Expuestos, Infectados, Recuperados) con el que se
pueda observar la morfología de eventos epidemiológicos y poder
simular estrategias de vacunación (Inmunización). Para validar la
morfología se contrasta el modelo computacional con un modelo
matemático clásico.
Para la proyección de brotes epidemiológicos se
involucra la aplicación de métodos estocásticos para encontrar
patrones, describir frecuencias de enfermedades que son de
interés del sector salud y para el monitoreo de alertas epidémicas
en la población. En el presente trabajo se propone el uso de un
modelo computacional basado en un Autómata Celular estocástico
global para la simulación del modelo epidemiológico SEIR
(Susceptibles, Expuestos, Infectados, Recuperados) con el que se
pueda observar la morfología de eventos epidemiológicos y poder
simular estrategias de vacunación (Inmunización). Para validar la
morfología se contrasta el modelo computacional con un modelo
matemático clásico.
Hector Cuesta-Arvizu presented an epidemic and endemic outbreak simulator at the University of North Texas on October 24, 2011. The simulator uses a global stochastic cellular automata approach to model the spread of infectious diseases. It incorporates SEIR and SEIRS disease models and allows simulation of different intervention strategies like vaccination programs. The simulator was developed using C# and allows visualization of epidemic and endemic curves during outbreak simulations.
1. The document presents a mathematical model examining the effects of disease on seasonally reproducing host populations. The model divides the host population into four classes and uses differential equations to model changes in population densities over time.
2. The model predicts a variety of long-term multi-year dynamics depending on parameters like reproductive season length and disease effects on fecundity. These include disease dying out, endemic disease, and regular or irregular multi-year population cycles.
3. Analysis of the model found that slow recovery of reproductive function and reduced fecundity post-recovery can induce multi-year cycles. The model also predicted delayed density-dependent reproduction timing and seroprevalence in cases of disease-
This document discusses examples of artificial intelligence applications for fintech and retail startups. It summarizes using AI for coaching/advising clients, assessing client risk profiles, valuation models, pricing, credit approval/risk, customer churn/segmentation, contract analysis, and fraud detection. It then provides more detail on using decision trees and random forests for a credit approval classification model, including data acquisition, preprocessing, normalization, model training, evaluation using cross-validation and a confusion matrix, and achieving 87.8% accuracy. It recommends a machine learning book for further learning.
La comparación elástica de imágenes es un problema abierto en la computación. Primero por su complejidad inherente y segundo por la enorme cantidad de imágenes existentes. En el presente artículo, se introduce un método elástico de comparación de imágenes, el cual ofrece una forma no-supervisada de búsqueda de similitud.
Simulación De Enfermedades Infecciosas En Grandes Poblaciones A Través De Un ...Hector Cuesta Arvizu
Simulación De Enfermedades Infecciosas En Grandes Poblaciones A Través De Un Autómata Celular Estocástico Paralelizado Por Gpu Con C-Cuda. Presentado en ISUM 2012 Guanajuato Mexico
Presentacion: Modelado para estudio de brotes epidémicos usando un Autómata C...Hector Cuesta Arvizu
Para la proyección de brotes epidemiológicos se
involucra la aplicación de métodos estocásticos para encontrar
patrones, describir frecuencias de enfermedades que son de
interés del sector salud y para el monitoreo de alertas epidémicas
en la población. En el presente trabajo se propone el uso de un
modelo computacional basado en un Autómata Celular estocástico
global para la simulación del modelo epidemiológico SEIR
(Susceptibles, Expuestos, Infectados, Recuperados) con el que se
pueda observar la morfología de eventos epidemiológicos y poder
simular estrategias de vacunación (Inmunización). Para validar la
morfología se contrasta el modelo computacional con un modelo
matemático clásico.
Para la proyección de brotes epidemiológicos se
involucra la aplicación de métodos estocásticos para encontrar
patrones, describir frecuencias de enfermedades que son de
interés del sector salud y para el monitoreo de alertas epidémicas
en la población. En el presente trabajo se propone el uso de un
modelo computacional basado en un Autómata Celular estocástico
global para la simulación del modelo epidemiológico SEIR
(Susceptibles, Expuestos, Infectados, Recuperados) con el que se
pueda observar la morfología de eventos epidemiológicos y poder
simular estrategias de vacunación (Inmunización). Para validar la
morfología se contrasta el modelo computacional con un modelo
matemático clásico.
Hector Cuesta-Arvizu presented an epidemic and endemic outbreak simulator at the University of North Texas on October 24, 2011. The simulator uses a global stochastic cellular automata approach to model the spread of infectious diseases. It incorporates SEIR and SEIRS disease models and allows simulation of different intervention strategies like vaccination programs. The simulator was developed using C# and allows visualization of epidemic and endemic curves during outbreak simulations.
1. The document presents a mathematical model examining the effects of disease on seasonally reproducing host populations. The model divides the host population into four classes and uses differential equations to model changes in population densities over time.
2. The model predicts a variety of long-term multi-year dynamics depending on parameters like reproductive season length and disease effects on fecundity. These include disease dying out, endemic disease, and regular or irregular multi-year population cycles.
3. Analysis of the model found that slow recovery of reproductive function and reduced fecundity post-recovery can induce multi-year cycles. The model also predicted delayed density-dependent reproduction timing and seroprevalence in cases of disease-