. "Data Science, Data Analysis, Data Mining"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Information retrieval and text mining"@en . . "6.0" . "Using today’s search engines allows us to find the needle in the haystack much easier than before. But how do you find out what the needle looks like and where the haystack is? That is exactly the problem we will discuss in this course. An important difference with standard information retrieval (search) techniques is that they require a user to know what he or she is looking for, while text mining attempts to discover information that is not known beforehand. This is very relevant, for example, in criminal investigations, legal discovery, (business) intelligence, sentiment- & emotion mining or clinical research. Text mining refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text. Text mining encompasses several computer science disciplines with a strong orientation towards artificial intelligence in general, including but not limited to information retrieval (building a search engine), statistical pattern recognition, natural language processing, information extraction and different methods of machine learning (including deep learning), clustering and ultimately integrating it all using advanced data visualization and chatbots to make the search experience easier and better.\n\nIn this course we will also discuss ethical aspect of using Artificial Intelligence for the above tasks, including the need for eXplainable AI (XAI), training deep-learning large language-models more energy efficient, and several ethical problems that may arise related to bias, legal, regulatory and privacy challenges.\n\nThis course is closely related with the course Advanced Natural Language Processing (ANLP). In the ANLP course, the focus is more on advanced methods and architectures to deal with complex natural language tasks such as machine translation, and Q&A systems. IRTM focusses more on building search engines and using text-analytics to improve the search experience. In the IRTM course, we will use a number of the architectures that are discussed in more detail in ANLP. The overlap between the two courses is kept to a minimum. There is no need to follow the courses in a specific order.\n\nPrerequisites\nNone.\n\nRecommended reading\nIntroduction to Information Retrieval. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze. Cambridge University Press, 2008. In bookstore and online: http://informationretrieval.org.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/464235/information-retrieval-and-text-mining" . . "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 . . . . . . . . . . . . . . . . . . . . . . . . . .