Module CS4295-KP04

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

4

Course of studies, specific fields and terms:

• Master Computer Science 2019, optional subject, Elective
• Master Psychology 2016, optional subject, Elective
• Master Biophysics 2023, optional subject, Elective
• Master Media Informatics 2020, optional subject, Elective
• Master MES 2020, optional subject, Elective
• Master Entrepreneurship in Digital Technologies 2020, optional subject, specific
• Master Psychology - Cognitive Systems 2027, optional subject, psychology
• Master Psychology - Cognitive Systems 2022, optional subject, psychology

Classes and lectures:

• CS4295-Ü: Deep Learning (exercise, 2 SWS)
• CS4295-V: Deep Learning (lecture, 2 SWS)

Workload:

• 75 hours private studies
• 45 hours in-classroom work

Contents of teaching:

• 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)

Qualification-goals/Competencies:

• 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

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr. Sebastian Otte

Teacher:

• Institute for Robotics and Cognitive Systems
• MitarbeiterInnen des Instituts
• Prof. Dr. Sebastian Otte

Literature:

• Goodfellow, I., Bengio, Y., & Courville, A. (2016) : Deep Learning MIT Press. ISBN 978-0262035613
• Prince, S. J. D. (2023) : Understanding Deep Learning The MIT Press. ISBN 978-0262048644
• Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020) : Mathematics for Machine Learning Cambridge University Press, 2020. ISBN 978-1108470049
• Bishop, C. M. (2006) : Pattern Recognition and Machine Learning Springer. ISBN 978-0387310732
• Recent publications on the related topics :

Language:

• offered only in English

Notes:

Last Updated:

15.07.2025