. "Algorithms, Data Structures, Complexity, And Computability, Modeling Complex Systems"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Data fusion"@en . . "6.0" . "ICT development, e.g., remote sensing, IoT, lead to an enormous growth of available data for analysis. To integrate this heterogeneous or multimodal data, data fusion approaches are used. Data fusion can be understood as a framework for the joint analysis of data from multiple sources (modalities) that allows achieving information/knowledge not recoverable by the individual ones.\n\nDuring this course, several approaches to data fusion will be discussed, such as:\n\nLow level data fusion, where data fusion methods are directly applied to raw data sets for exploratory or predictive purposes. A main advantage is the possibility to interpret the results directly in terms of the original variables. An example of a low level data fusion is measuring the same signal or phenomena with different sensors, in order to discover the original one. Traditionally, PCA based methods are used for this type of data fusion.\nMid level data fusion, where data fusion operates on features extracted from each data set. The obtained features are then fused in a “new” data set, which is modeled to produce the desired outcome. A main advantage is that the variance can be removed in the features extraction step, and thus the final models may show better performance. An example of a mid level data fusion is extracting numerical features from an image, and building a decision model based on those features.\nHigh level data fusion, also known as decision fusion, where decisions (models outcome) from processing of each data set are fused. It is used when the main objective is to improve the performance of the final model and reach an automatic decision. Several methods can be used for high-level DF, such as weighted decision methods, Bayesian inference, Dempstere Shafer’s theory of evidence, and fuzzy set theory. There is a link between high-level data fusion and ensemble methods.\nFederated learning. Federated learning enables multiple parties jointly train a machine learning model without exchanging the local data. In case of federated learning, we can talk about model fusion.\nPrerequisites\nNone.\n\nDesired prior knowledge: statistics and basic machine learning\n\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/462543/data-fusion" . . "Presential"@en . "FALSE" . . "Master in Data Science for Decision Making"@en . . "https://curriculum.maastrichtuniversity.nl/education/partner-program-master/data-science-decision-making" . "120"^^ . "Presential"@en . "Data Science for Decision Making will familiarise you with methods, techniques and algorithms that can be used to address major issues in mathematical modelling and decision making. You will also get hands-on experience in applying this knowledge through computer classes, group research projects and the thesis research. The unique blend of courses will equip you with all the knowledge and skills you’ll need to have a successful career.\n\nWidespread applications\nData Science for Decision Making links data science with making informed decisions. It has widespread applications in business and engineering, such as scheduling customer service agents, optimising supply chains, discovering patterns in time series and data, controlling dynamical systems, modelling biological processes, finding optimal strategies in negotiation and extracting meaningful components from brain signals. This means you'll be able to pursue a career in many different industries after you graduate.\n\nProgramme topics\nData Science for Decision Making covers the following topics:\n\n* production planning, scheduling and supply chain optimisation\n* modelling and decision making under randomness, for instance in queuing theory and simulation\n* signal and image processing with emphasis on wavelet analysis and applications in biology\n* algorithms for big data\n* estimation and identification of mathematical models, and fitting models to data\n* dynamic game theory, non-cooperative games and strategic decision making with applications in evolutionary game theory and biology\n* feedback control design and optimal control, for stabilisation and for tracking a desired behaviour\n* symbolic computation and exact numerical computation, with attention to speed, efficiency and memory usage\n* optimisation of continuous functions and of problems of a combinatorial nature"@en . . . "2"@en . "FALSE" . . "Master"@en . "Thesis" . "2314.00" . "Euro"@en . "18400.00" . "Recommended" . "Data science and big data are very important to companies nowadays, and this programme will provide you with all the training you’ll need be active in these areas. The comprehensive education, practical skills and international orientation of the programme will open the world to you. When applying for positions, graduates from Data Science for Decision Making are often successful because of their problem-solving attitude, their modern scientific skills, their flexibility and their ability to model and analyse complex problems from a variety of domains.\n\nGraduates have found positions as:\n* Manager Automotive Research Center at Johnson Electric\n* Creative Director at Goal043 | Serious Games\n* Assistant Professor at the Department of Advanced Computing Sciences, Maastricht University\n* BI strategy and solutions manager at Vodafone Germany\n* Scientist at TNO\n* Digital Analytics Services Coordinator at PFSweb Europe\n* Software Developer at Thunderhead.com\n* Data Scientist at BigAlgo\n* Researcher at Thales Nederland"@en . "2"^^ . "TRUE" . "Midstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . .