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Introducción a Machine learning
En este curso abordaremos el aprendizaje automático de máquinas desde una perspectiva algebraica. Se abordarán cuatro temas, el primero de ellos será una introducción a los modelos de regresión y clasificación lineal, comenzando por la regresión lineal multivariada, sus aplicaciones y cómo evitar el sobre-ajuste utilizando regularización. Luego de esto introduciremos la regresión logística como uno de los métodos de clasificación más relevantes. La regresión logística nos permitirá realizar una conexión con la formulación de la arquitectura de una red neuronal artificial, ya que la neurona logística, la cual puede interpretarse como la unidad básica para el desarrollo de modelos de clasificación con redes neuronales, es el equivalente a una regresión logística. El tercer tema se enfoca en el estudio de diferentes metodologías utilizadas para el correcto entrenamiento de redes neuronales, tanto para regresión como clasificación, así mismo se introducirán algunos métodos utilizados para identificar los modelos que tienen el mejor rendimiento. Finalmente, se describirán diferentes métodos para el aprendizaje no supervisado. Específicamente se abordará PCA para la reducción de dimensionalidad y k-means para el desarrollo de modelos de agrupamiento. También se describirán algunas técnicas utilizadas para poder evaluar el rendimiento de estos modelos. Además, el curso abordará el uso de redes neuronales para el desarrollo de modelos de aprendizaje no supervisado, específicamente se explicarán las Redes de Hopfield que permiten el almacenamiento de patrones en la arquitectura de su red, mediante el uso de memoria asociativa; y los mapas autoorganizados o redes de Kohonen que permite identificar estructuras en los datos de entrenamiento y que pueden utilizarse para la reducción de dimensionalidad.
Created by: Universidad del Rosario
Level: Introductory
Created by: Universidad del Rosario
Level: Introductory
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Introducción a Machine learning
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