. "Algorithms, Data Structures, Complexity, And Computability, Modeling Complex Systems"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Symbolic computation and control"@en . . "6.0" . "This course consists of two interrelated parts. In the first part, we focus on basic techniques for the digital control of linear dynamical systems using feedback. We start by addressing system stability and we discuss the technique of pole placement by state feedback to solve the regulation problem. Then we introduce state observers to solve the regulation problem by output feedback. Next, we extend our scope to tracking problems. This involves the design of additional dynamics to characterize the relevant class of reference signals, which are then integrated with the earlier set-up for output feedback. Finally, we discuss the classical topic of optimal control, which can be employed to avoid using prototype systems for pole placement, and which allows the user to design a feedback law by trading off the cost involved in generating large inputs against the achieved tracking accuracy. In the second part, we address computational issues, related to the field of systems and control. Classically, computers have been designed primarily to perform approximate numerical arithmetic. Modern software packages for mathematical computation, such as Maple and Mathematica, allow one to perform exact and symbolic computation too. We shall explore this new area. It is demonstrated how speed, efficiency and memory usage considerations often lead to surprising and fundamentally different algorithmic solutions in a symbolic or exact context. Applications and examples involve stability of linear systems, model approximation, and linear matrix equations with free parameters. Practical classes serve to demonstrate the techniques and to make the student familiar with exact and symbolic computation.\n\nPrerequisites\nDesired Prior Knowledge: Linear Algebra, Calculus, Mathematical Modelling.\n\nRecommended reading\nRichard J. Vaccaro, Digital Control - A State-Space Approach, McGraw-Hill International Editions, 1995. ISBN 0-07-066781-0.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/463211/symbolic-computation-and-control" . . "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 . . . . . . . . . . . . . . . . . . . . . . . . . .