. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Deep learning"@en . . "8" . "The course aims at teaching the required skills to \r\nuse deep learning methods on applied problems. \r\nIt will show how to design and train a deep \r\nneural network for a given task, and the sufficient \r\ntheoretical basis to go beyond the topics directly \r\nseen in the course. The planned content of \r\nthe course:\r\n• What is deep learning, introduction to tensors.\r\n• Basic machine-learning, empirical risk \r\nminimization, simple embeddings.\r\n• Linear separability, multi-layer perceptrons, \r\nback-prop.\r\n• Generalized networks, autograd, batch processing, \r\nconvolutional networks.\r\n• Initialization, optimization, and regularization.\r\nDrop-out, activation normalization, skip \r\nconnections.\r\n• Deep models for Computer Vision.\r\n• Analysis of deep models.\r\n• Auto-encoders, embeddings, and generative models.\r\n• Deep learning for sequences - Recurrent neural \r\nnetworks (RNNs); vanishing and exploding \r\ngradients; Long Short-Term Memory (LSTM); \r\ndeep RNNs; bidirectional RNNs; combination of \r\nCNNs with RNNs - pytorch tensors, deep learning \r\nmodules, and internals.\n\nOutcome: Not Provided" . . "Presential"@en . "TRUE" . . "Master of Science in Data Science"@en . . "https://datascience.cy/" . "90"^^ . "Presential"@en . "The MSc in Data Science programme brings together the expertise of three departments in the University of Cyprus – Business and Public Administration, Computer Science, and Mathematics and Statistics – to prepare the next generation of data-oriented thinkers.\n\nData science is a modern interdisciplinary field that uses scientific methods, processes, algorithms and systems in order to extract knowledge and insights from data. The programme’s objective is to offer a strong understanding of basic and advanced methods in statistical inference, machine learning, data visualization, data mining, and business analytics.\n\nThe programme is designed for students that have a background in STEM or in Business and Economics, who can demonstrate good knowledge of English, and who, as part of their undergraduate degree, have completed an introductory statistics course, as well as a course in a programming language such as Python and/or R (see Admissions for further information).\n\n\nThe duration of the programme is 18 months (90 ECTS). The first two semesters are dedicated to core courses, and homogenize students’ knowledge on the basics of data science. The third semester allows students to select one of three tracks: Computational Science, Statistics, and Business Analytics. In the summer semester the Capstone project, brings students in contact with real world problems and helps them cement the knowledge and skills they acquired.\n\nOutcome:\nAt the completion of the programme, students will have gained skills that are critical in a modern data-driven world, and will be in a position to think across disciplines and to transform data into actionable insights.\n\nIntended learning outcomes:\nBuild strong background in Data Science\n_ Master powerful tools that address a wide range \nof topics in Data Science\n_ Acquire statistical skills at an appropriately \nadvanced level \n_ Acquire deep knowledge in one or more fields \nof Data Science \n_ Obtain familiarity with basic concepts in other \nNatural and/or Social Sciences, pertinent to \ndata-driven discovery \n• Build research foundations\n_ Get acquainted with faculty research in fields of \nData Science \n_ Demonstrate in depth understanding of a \nbreadth of disciplines, and become familiar with \nthe dominant research directions \n_ Acquire experience of independent work, ideally \nso in the context of class research projects \n• Learn to solve real-world problems\n_ Identify and assess the needs of an organization \nfor a data science task \n_ Collect and manage the data needed \n_ Interpret data science analysis outcomes \n_ Transform findings into actionable business \nstrategies \n_ Communicate data science-related information \neffectively using audience-appropriate format \nand delivery \n_ Value and safeguard the ethical use of data \n• Build multi-context skills\n_ Develop transferable skills such as: oral and \nwritten scientific communication, near fluent \nuse of scientific English, use of information/\ncommunication technology, organization and \nplanning of group work \n_ Exhibit versatility and innovative thinking in \naddressing and managing open questions in \na variety of contexts, as an essential asset \nfor careers in research, industry, commerce, \neducation and the public sector"@en . . . . "1.5"@en . "TRUE" . . "Master"@en . "None" . "5125.00" . "Euro"@en . "5125.00" . "Mandatory" . "No Job Prospects Listed"@en . "3"^^ . "TRUE" . "Midstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . .