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

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