. "Advanced natural language processing"@en . . "6.0" . "For decades, teaching a computer to deal with natural language processing (NLP) was a long-time dream of humankind. Task such as machine translation, summarization, question-answering, speech recognition or chatting remained a challenge for computer program. Around 2020, major improvements were made. Starting with machine translation and ultimately in late 2022 with ChatGPT. Why were these large-language models suddenly so good? How did we get here? What can we do with these new algorithms to improve them even more?\n\nThis course will provide the skills and knowledge to understand and develop state-of-the-art (SOTA) solutions for these natural language processing (NLP) tasks. After a short introduction to traditional generative grammars and statistical approaches to NLP, the course will focus on deep learning techniques. We will discuss Transformers, variations on their architecture (including BERT and GPT) in depth, which models works best for which tasks, their capacities, limitations and how to optimize these.\n\nAlthough that we have algorithms that can deal with Natural Language Processing in ways that can no longer be distinguished from humans, we still have some major problems to address: (i) we do not fully understand what these algorithms know and what they do not know. So, there is a strong need for eXplainable AI (XAI) in NLP. (ii) Training the deep-learning large language-models costs too much energy. We need to develop models that are less computationally (and thus energy) intensive. (iii) Now that these algorithms operate at human-level quality, several ethical problems arise related to computer generated fake-news, fake profiles, bias, and other abuse. But there are also ethical, legal, regulatory and privacy challenges. In this courses, these important topics will also be discussed.\n\nThis course is closely related with the course Information Retrieval and Text-Mining (IRTM). In this course the focus is more on advanced methods and architectures to deal with complex natural language tasks. The IRTM course focusses more on building search engines and text-analytics, but also uses a number of the architectures which are discussed in more depth in this course. 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\nPapers published in top international conferences and journals in machine learning field.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/465515/advanced-natural-language-processing" . . "Presential"@en . "FALSE" . . "Language"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "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 . . . . . . . . . . . . . . . . . . . . . . . . . .