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# sitc
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# sitc
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Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects
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Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects
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- SITC (Sistemas Inteligentes y Tecnologías del Conocimiento) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
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- CDAW (Ciencia de datos y aprendizaje en automático en la web de datos) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
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- TIAD (Tecnologías Inteligentes de Análisis de Datos) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
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- ABID (Analítica de Big Data) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
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For following this course:
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For following this course:
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- Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda')
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- Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda')
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@ -9,11 +9,13 @@ For following this course:
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- Run in a terminal in the folder sitc: jupyter notebook (and enjoy)
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- Run in a terminal in the folder sitc: jupyter notebook (and enjoy)
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Topics
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Topics
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* Python: quick introduction to Python
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* Python: a quick introduction to Python
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* ML-1: introduction to machine learning with scikit-learn
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* ML-1: introduction to machine learning with scikit-learn
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* ML-2: introduction to machine learning with pandas and scikit-learn
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* ML-2: introduction to machine learning with pandas and scikit-learn
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* ML-21: preprocessing and visualizatoin
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* ML-3: introduction to machine learning. Neural Computing
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* ML-3: introduction to machine learning. Neural Computing
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* ML-4: introduction to Evolutionary Computing
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* ML-4: introduction to Evolutionary Computing
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* ML-5: introduction to Reinforcement Learning
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* ML-5: introduction to Reinforcement Learning
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* NLP: introduction to NLP
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* NLP: introduction to NLP
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* LOD: Linked Open Data, exercises and example code
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* LOD: Linked Open Data, exercises and example code
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* SNA: Social Network Analysis
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