Machine learning and deep learning have become ubiquitous in many applications and research fields and have greatly pushed the state-of-the-art in many disciplines. In this course the student will focus on understanding and expanding their knowledge in the field of deep artificial neural networks (ANNs).
Prerequisites:
Students are expected to have a good knowledge on machine learning, calculus, linear algebra and probability theory. Moreover, at least basic knowledge of python programming, and image processing is required. Along with introducing the concepts of deep learning, the lectures will provide a refresher on relevant concepts from image processing and machine learning whenever required. However, familiarity with these concepts would be necessary (see lecture Machine Learning, offered in WS).
Content:
Details of deep learning architectures with a main focus of image-based problems will be discussed. The course will start with a refresher on machine learning and ANNs and during some first lectures the concepts of deep learning (DL) and convolutional neural networks (CNNs) will be presented. Implementing the well-known DL networks will also help the students to be prepared to tackle real world problems during their study and later in their carrier. The course would be a hands-on and project-based course. Hence, open-source and free libraries and frameworks form Python programming ecosystem will be used to implement various DL concepts on freely available datasets. Upon the completion of the final project of the course, students will acquire the skills to apply the learned concepts in a specific application area.
ECTS: 5 (Lecture+Lab session)
Class time: Tuesdays, 11:30 - 13:00, Fridays 13:15-14:45
Location: Seminar room (Bienroder Weg, 81, 38106 Braunschweig)