. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Machine learning for the built environment"@en . . "5" . "This is an introductory course for machine learning to equip students with the basic knowledge and skills for further study and research of machine learning. It introduces the theory/methods of well-established machine learning and state-of-the-art deep learning techniques for processing geospatial data (e.g., point clouds). The students will also gain hands-on experiences by applying commonly used machine learning techniques to solve practical problems through a series of lab exercises and assignments. \n\nAfter the course, the students will be able to:\r\n- explain the impact, limits, and dangers of machine learning; give use cases of machine learning for the built environment;\r\n- explain the main concepts in machine learning (e.g., regression, classification, unsupervised learning, supervised learning, overfitting, training, validation, cross-validation, learning curve);\r\n- explain the principles of well-established unsupervised and supervised machine learning techniques (e.g., clustering, linear regression, Bayesian classification, logistic regression, SVM, decision tree, random forest, and neural networks);\r\n- preprocess data (e.g., labelling, feature design, feature selection, train-test split) for applying machine learning techniques;\r\n- select and apply the appropriate machine learning method for a specific geospatial data processing task (e.g., object classification);\r\n- evaluate the performance of machine learning models." . . "Presential"@en . "FALSE" . . "Master of Science Geomatics"@en . . "https://www.tudelft.nl/en/education/programmes/masters/geomatics/msc-geomatics" . "120"^^ . "Presential"@en . "With Geomatics for the Built Environment you will learn to use advanced techniques in data analysis, geographic data modelling and the visualisation of data. To be able to do this, you will develop expert skills in different programming languages such as Python, C++ and SQL, as they are essential for solving many of the Geomatics-related questions we need to consider.\r\n\r\nThe remote sensing techniques that you will learn in this programme give you the ability to measure and observe our environment and especially those features that cannot be seen with the human eye. Data management and analysis techniques allow you to turn these measurements into useful information and knowledge, with which patterns can be identified, behaviour over time can be tracked and a future state can be predicted. You will apply your skills in 3D modelling, GIS, mapping, serious gaming, simulation and visualisation to a wide range of fields such as urban planning, disaster management, geodesign, location-based services (LBS) and land administration. \r\n\r\nBeyond that, you will acquire knowledge about the use and governance of geographic data. In Geomatics you will implement the principles of open science to the greatest extent possible. In your assignments you will use open data and open source software, and your research results will be openly published in the university’s repository."@en . . . "2"@en . "FALSE" . . "Master"@en . "Thesis" . "2314.00" . "Euro"@en . "20560.00" . "Recommended" . "Geomatics graduates are specialists with a broad base who are able to position themselves across the entire breadth of the Geomatics spectrum. The Geomatics working field is expanding quickly so there is ample opportunity to find a position that suits you best or to start your own company. \n\nProspects:\n- geovernmental and private organisations (companies)\n- PhD research"@en . "no data" . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . . . . . . .