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Einrichtungen >> Zentrale Universitätsverwaltung >> Zentrale Dezernate >> Dezernat Z/KOM - Kommunikation & Alumni >>
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xAI-DL-M: Deep Learning- Dozent/in
- Prof. Dr. Christian Ledig
- Angaben
- Vorlesung
Rein Präsenz
2,00 SWS
Zeit und Ort: Di 12:00 - 14:00, WE5/00.019 (außer Di 7.2.2023)
bis zum 31.1.2023
- Voraussetzungen / Organisatorisches
- MSc AI, MSc WI, MSc CitH
Sign up: VC Course
- Inhalt
- Vorkenntnisse / Prerequisites:
Good working knowledge of programming (e.g., in Python); Recommended completion of modules: Lernende System / Machine Learning [KogSys-ML-M], Einführung in die Künstliche Intelligenz / Introduction to AI [AI-KI-B], Mathematik für Informatik 2 (Lineare Algebra) [KTR-MfI-2], Algorithmen und Datenstrukturen [AI-AuD-B]
Description:
Deep Learning is a form of machine learning that learns hierarchical concepts and representations directly from data. Enabled by continuously growing dataset sizes, compute power and rapidly evolving open-source frameworks Deep Learning based AI systems continue to set the state of the art in many applications and industries. The course will provide an introduction to the most relevant techniques in the field of Deep Learning and a broad range of its applications.
The lecture will be held in English. The following is a selection of topics that will be addressed in the course:
- Relevant concepts in linear algebra, probability and information theory
- Deep feedforward networks
- Convolutional Neural Networks
- Regularization, Batch Normalization
- Optimization (Backpropagation, Stochastic Gradient Decent) and Cost Functions
- Classification (binary, multiclass, multilabel)
- Object Detection & Segmentation
- Generative Modelling
- Attention mechanisms & Transformer Networks
- Evaluation of ML approaches
Goals:
In this course students will learn/recap some fundamentals from mathematics and machine learning that are critical for the introduction of the concept of Deep Learning. Participants will learn about various foundational technical aspects including optimization and regularization strategies, cost functions and important network architectures such as Convolutional Networks. Students will further get an insight into more advanced concepts such as sequence modelling and generative modelling. Participants will further learn about representative architectures of important algorithm categories, e.g., classification, detection, segmentation, some of their concrete use cases and how to evaluate them.
The lecture is accompanied by exercises and assignments that will help participants develop practical, hands-on experience. In those exercises students will learn how to implement and evaluate Deep Learning algorithms using Python and its respective commonly used libraries.
- Empfohlene Literatur
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning, MIT Press, 2016
- Zhang, Lipton, et al.: Dive into Deep Learning (https://d2l.ai/)
Further literature will be announced at the beginning of the course.
- Englischsprachige Informationen:
- Title:
- xAI-DL-M: Deep Learning
- Credits: 6
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 50
- Zugeordnete Lehrveranstaltungen
- Ü (Rein Präsenz):xAI-DL-M: Deep Learning, Gruppe 1
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Dozentinnen/Dozenten: Sebastian Dörrich, M.Sc., Prof. Dr. Christian Ledig
Zeit und Ort: Mi 10:00 - 12:00, WE5/03.004
- Ü (Rein Präsenz):xAI-DL-M: Deep Learning, Gruppe 2
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Dozentinnen/Dozenten: Sebastian Dörrich, M.Sc., Prof. Dr. Christian Ledig
Zeit und Ort: Do 12:00 - 14:00, WE5/04.003
- Institution: Lehrstuhl für Erklärbares Maschinelles Lernen
Hinweis für Web-Redakteure:
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