Modul RO4100-KP08

Dauer

2 Semester

Angebotsturnus

Jährlich, kann sowohl im SoSe als auch im WiSe begonnen werden

Leistungspunkte

8

Studiengang, Fachgebiet und Fachsemester:

• Master Robotics and Autonomous Systems 2019, Pflicht, Pflicht-Lehrmodule, 1. und 2. Fachsemester

Lehrveranstaltungen:

• CS4295-Ü: Deep Learning (Übung, 1 SWS)
• CS4575-V: Sequence Learning (Vorlesung, 2 SWS)
• CS4575-Ü: Sequence Learning (Übung, 1 SWS)
• CS4295-V: Deep Learning (Vorlesung, 2 SWS)

Workload:

• 120 Stunden Eigenständige Projektarbeit
• 120 Stunden Selbststudium
• 60 Stunden Präsenzübung
• 60 Stunden Präsenzstudium

Lehrinhalte:

• Foundations and Deep Learning Basics (Learning Paradigms, Classification and Regression, Underfitting and Overfitting)
• Shallow Neural Networks (Basic Neuron Model, Multilayer Perceptions, Backpropagation, Computational Graphs, Universal Approximation Theorem, No-Free Lunch Theorems, Inductive Biases)
• Optimization (Stochastic Gradient Descent, Momentum Variants, Adaptive Optimizer)
• Convolutional Neural Networks (1D Convolution, 2D Convolution, 3D Convolution, ReLUs and Variants, Down and Up Sampling Techniques, Transposed Convolution)
• Regularization (Early Stopping, L1 and L2 Regularization, Label Smoothing, Dropout Strategies, Batch Normalization)
• Very Deep Networks (Highway Networks, Residual Blocks, ResNet Variants, DenseNets)
• Dimensionality Reduction (PCA, t-SNE, UMAP, Autoencoder)
• Generative Neural Networks (Variational Autoencoder, Generative Adversarial Networks, Diffusion Models)
• Graph Neural Networks (Graph Convolutional Networks, Graph Attention Networks)
• Fooling Deep Neural Networks (Adversarial Attacks, White Box and Black Box Attacks, One-Pixel Attacks)
• Physics-Aware Deep Learning (Physical Knowledge as Inductive Bias, PINN, PhyDNet, Neural ODE, FINN)
• Introduction to Sequence Learning (Formalisms, Metrics, Recapitulation of Relevant Machine Learning Techniques)
• Recurrent Neural Networks (Simple RNN Models, Backpropagation Through Time)
• Gated Recurrent Networks (Vanishing Gradient Problem in RNNs, Long Short-Term Memories, Gated Recurrent Units, Stacked RNNs)
• Important Techniques for RNNs (Teacher Forcing, Scheduled Sampling, h-Detach)
• Bidirectional RNNs and related concepts
• Hierarchical RNNs and Learning on Multiple Time Scales
• Online Learning and Learning without BPTT (Real-Time Recurrent Learning, e-Prop, Forward Propagation Through Time)
• Reservoir Computing (Echo State Networks, Deep ESNs)
• Spiking Neural Networks (Spiking Neuron Models, Learning in SNNs, Neuromorphic Computing, Recurrent SNNs)
• Temporal Convolution Networks (Causal Convolution, Temporal Dilation, TCN-ResNets)
• Introduction to Transformers (Sequence-to-Sequence Learning, Basics on Attention, Self-Attention and the Query-Key-Value Principle, Large Language Models)
• State Space Models (Structured State Space Sequence Models, Mamba)

Qualifikationsziele/Kompetenzen:

• Students get a fundamental understanding deep learning basics such as backpropagation, computational graphs, and auto-differentiation
• Students understand the implications of inductive biases
• Students get a comprehensive understanding of most relevant deep learning approaches
• Students learn to analyze the challenges in deep learning tasks and to identify well-suited approaches to solve them
• Students will understand the pros and cons of various deep learning models
• Students know how to analyze the models and results, to improve the model parameters, and to interpret the model predictions and their relevance
• Students get a comprehensive understanding of most relevant sequence learning approaches
• Students learn to analyze the challenges in sequence learning tasks and to identify well-suited approaches to solve them
• Students will understand the pros and cons of various sequence learning models
• Students can implement common and custom sequence learning models for time series analysis, classification, and forecasting
• Students know how to analyze the models and results, to improve the model parameters, and to interpret the model predictions and their relevance

Vergabe von Leistungspunkten und Benotung durch:

• Klausur oder mündliche Prüfung nach Maßgabe des Dozenten

Modulverantwortliche:

• Prof. Dr. Sebastian Otte

Lehrende:

• Institut für Robotik und Kognitive Systeme
• MitarbeiterInnen des Instituts
• Prof. Dr. Sebastian Otte

Literatur:

• Goodfellow, I., Bengio, Y., & Courville, A. : Deep Learning MIT Press (2016), ISBN 978-0262035613
• Nakajima, K., & Fischer, I. : Reservoir Computing: Theory, Physical Implementations, and Applications Springer Nature Singapore (2021), ISBN 978-9811316869
• Sun, R., & Giles, C. : Sequence Learning: Paradigms, Algorithms, and Applications Springer Berlin Heidelberg (2001), ISBN 978-3540415978
• Bishop, C. M. : Pattern Recognition and Machine Learning Springer (2006), ISBN 978-0387310732
• Sutton, R., & Barto, A. : Reinforcement Learning: An Introduction The MIT Press (2018), ISBN 978-0262039246
• François-Lavet, V., Henderson, P., Islam, R., Bellemare, M., & Pineau, J. : An Introduction to Deep Reinforcement Learning Now Publishers Inc (2018), ISBN 978-1680835380
• Recent publications on the related topics :

Sprache:

• Wird nur auf Englisch angeboten

Bemerkungen:

Letzte Änderungen:

07.02.2024