. "Artificial Intelligence"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Deep Learning"@en . . . "https://www.sdu.dk/en/uddannelse/sdu_summer_school/summerschool_courses/2023_software_engineering_and_computer_science"@en . "Campusvej 55, 5230 Odense, Denmark" . "5.0" . "Presential"@en . " Course introduction\nMachine learning has become a part in our everyday lifes, from simple product recommendations to personal electronic assistants to self-driving cars. Especially Deep Learning has gained a lot of interest in the media and has demonstrated impressive results. This intensive course will introduce the student to the exciting world of deep learning. We will learn about the theoretical background and concepts driving deep learning and highlight and discuss the most noteworthy applications of deep learning but also their limitations. Furthermore, all content will immediately put into practice by suitable exercises and programming tasks.\n\nIn relation to the competence profile of the degree it is the explicit focus of the course to:\nGive knowledge and understanding of a collection of specialized models and methods developed within Computer Science based on research on highest international level, as well as of models and methods aimed at applications in other subject areas.\nGive skills to describe, analyze and solve computational problems by using the methods learnt, to analyze pros and cons of different methods in Computer Science, as well as to develop new variants of the methods learnt where the problem at hands requires this.\nGive the competence to plan and execute scientific projects on a high technical level.\n Expected learning outcome\nThe learning objectives of the course is that the student demonstrates the ability to:\nDescribe the principles of deep neural networks in a scientific and precise language and notation\nAnalyze the various types of neural networks, the different layers and their interplay\nDiscuss the feasibility of deep learning approaches to concrete problems\nDescribe the theoretical mathematical foundations of the field\nImplement and apply deep learning frameworks for solving concrete problems\nUtilize state-of-the-art deep learning frameworks for implementing deep neural networks\n\n Content\nThe following main topics are contained in the course:\nfeedforward neural networks\nrecurrent neural networks\nconvolutional neural networks\nbackpropagation algorithm\nregularization" . "0.5" . "Summer School"@en . . . "The University of Southern Denmark (SDU)"@en . "Campusvej 55, 5230 Odense, Denmark"@en . "no data" . "no data" . "164"^^ . "no data"@en . . "English"@en . .