Lectures
Compute Graphics (infCG-02a, Winter Term)
Abstract
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Computer Graphics enables the generation of visualizations that can be used in a variety of domains and applications, such as movies and games, manufacturing, architecture, and in research. Due to recently introduced concepts, such as the Metaverse and Digital Twins, 3D computer graphics has gained a considerable amount of momentum. Key goals of Computer Graphics are to develop algorithms for rendering images, for modeling the 3D geometry of objects and scenes, and for generating simulations and animations.
Learning Objectives
This is an introductory course to computer graphics that focuses on the technical concepts for the real-time generation of images. The beginning of the course will address the transformation of geometry to pixels by introducing the render pipeline along with the underlying mathematical concepts, including triangles, normals, interpolation, texture mapping, etc. Additionally, the course will address more fundamental concepts of computer graphics, such as light transport, illumination, shadowing, camera models and various concepts for simplifying complex mathematical formalisms. We will discuss different geometric representations and their simplification based on level of detail algorithms as well as the organization of large collections of objects based on acceleration data structures. Finally, we will discuss advanced image-based techniques, such as anti-aliasing and deferred rendering as well as different color spaces. The objective of this course is to develop a real-time renderer for objects and scenes. Code for an initial framework for camera, shaders, and simple geometry processing will be provided. Source code examples will be discussed in the classes.
Course Content
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Render Pipeline (from geometry to pixels)
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Geometric transformations
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Camera models
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Models for local and global illumination
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Shading, shadowing, and texture mapping
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Animation and Interpolation
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Geometric representations
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Level of detail algorithms and spatial organization
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Anti-aliasing and deferred rendering
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Color theory and color spaces
Further Requirements
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Mathematical basics of algebra and analysis.
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Ideally, some knowledge of C++.
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OpenGL and GLSL (shader language) will be introduced.
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All lecture slides and course material will be in English.
Exam
Written exam (100 min.). It is required to actively work on the exercises (homework) to be allowed to take the exam. The exam will be offered in the 2 examination time slots following the course.
Teaching and Learning Methods
Learning materials will be provided in the form of presentation slides. Primary lecture media is projected slide presentation. Occasionally complemented with drafts on board/white board. Concepts are introduced in the lectures with the help of examples and specific application tasks. In the exercise the knowledge is deepened and applied - guided by bi-weekly homework assignments.
Literature
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Real-time Rendering, Tomas Akenine-Möller, Eric Haines, Naty Hoffman, Angelo Pesce, Michael Iwanicki, Sebastien Hillaire, 4th Edition, Taylor & Francis
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Fundamentals of Computer Graphics: International Student Edition, Steve Marschner, Pete Shirley, A K Peters/CRC Press; 5th edition, 2021
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Abstract
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This lecture provides a foundational introduction to deep learning, with a focus on understanding the core concepts and practical applications of neural networks. The course begins with basic building blocks such as optimization, loss functions, and backpropagation before advancing to key architectures including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Emphasis is placed on understanding how these models are trained, how they generalize, and the role of regularization and data augmentation in improving performance. The course is designed to prepare students to implement and apply deep learning models effectively, and to critically evaluate their behavior and results across a range of tasks. The course is suitable for students of Bachelor programs, as well as those from other disciplines with an interest in AI.
Learning Objectives
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Students will be able to...
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explain the foundational principles of deep learning and artificial neural networks.
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describe and apply key training concepts including loss functions, optimizers, and backpropagation.
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implement and train feedforward neural networks.
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understand and apply techniques for regularization, data augmentation, and training stability.
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analyze and compare common activation functions and loss functions.
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critically evaluate model performance and generalization.
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apply CNN architectures to image-based tasks and understand design choices in state-of-the-art models.
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understand RNN and Transformers architectures.
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work independently and in teams on deep learning problems using Python-based tools.
Course Content
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Introduction to Deep Learning and Neural Networks
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Optimization and Backpropagation
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Loss Functions and Activation Functions
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Training Neural Networks: Best Practices
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Regularization Techniques and Data Augmentation
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Convolutional Neural Networks (CNNs) and CNN Architectures (e.g., AlexNet, ResNet)
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Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
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Introduction to Transformers in Deep Learning
Further Requirements
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Basic knowledge of linear algebra, statistics, and differential calculus.
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Familiarity with Python programming.
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All lecture slides and course materials will be provided in English.
Exam
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Written or oral exam at the end of the semester. Successful participation in the exercises (homework assignments) is required for admission to the final exam.
Teaching and Learning Methods
Lectures are presented using slide-based presentations, supported by occasional whiteboard explanations. The concepts introduced in the lecture will be accompanied by practical examples and applications. Weekly exercises and homework assignments will be used to reinforce and deepen understanding.
This course is applicable for students pursuing computer science, data science, and related fields. It also serves as a basis for deep learning and AI courses for other fields of science as well as preparation for more advanced AI courses (e.g., Autonomous Learning, Generative AI, Information Retrieval, etc.).
Literature
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Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 2016
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Neural Networks and Deep Learning, Michael Nielsen (online resource)
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Deep Learning with Python, François Chollet, Manning Publications
Generative AI (infGAI-01a, Summer Term)
Abstract
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This lecture is dedicated to developing an understanding of generative AI models with a focus on visual computing. The lecture will introduce the diverse applications of generative AI, ranging from generating new data samples and using generative architectures for data augmentation to highlighting how these technologies are revolutionizing multiple industries. By the end of the lecture, attendees will gain an understanding of generative AI, equipped with the knowledge to critically assess its applications. This lecture is designed to cater to a wide range of students, from BA and MSc to students of other courses.
Learning Objectives
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Students will be able to...
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explain basic concepts of machine learning (optimizers, losses, backpropagation, etc.)
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understand foundational principles of generative models, distinguishing them from traditional discriminative models in machine learning
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implement various generative architectures, including Autoencoders, Generative Adversarial Networks (GANs), Diffusion Models and Transformers
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describe their underlying mechanisms, strengths, and limitations
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understand the difference between predictive and generative modeling and advanced concepts, such as synthetic data generation and various data augmentation strategies
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discuss the technology's capabilities and limitations
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to work in teams and to independently work on ML tasks
Course Content
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Introduction to Deep Learning (Simple Architectures, Losses, Optimizers, Backpropagation)
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Concepts of Generative Modeling
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Convolutional Neural Networks
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Autoencoders and variational autoencoders
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Image-based predictive and discriminative network architectures (i.e., image-based tasks)
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Transformers for image-based task
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Generative Adversarial Networks
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Domain Adaptation and Style Transfer
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Diffusion Models
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Neural Radiance Fields (NeRFs)
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Synthetic Data Generation (with focus on images)
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Data Augmentation Strategies
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Transfer Learning and Finetune Training
Further Requirements
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Basic knowledge about statistics, linear algebra, and especially differential calculus.
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Familiarity with the programming language Phython.
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All lecture slides and course material will be in English.
Exam
Written exam (100 min.). It is required to actively work on the exercises (homework) to be allowed to take the exam. The exam will be offered in the 2 examination time slots following the course.
Teaching and Learning Methods
Learning materials will be provided in the form of presentation slides. Primary lecture media is projected slide presentation. Occasionally complemented with drafts on board/white board. Concepts are introduced in the lectures with the help of examples and specific application tasks. In the exercise the knowledge is deepened and applied - guided by weekly homework assignments.
Literature
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Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, 2016, MIT Press
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Generative AI - Teaching Machines to Paint, Write, Compose and Play, David Foster, 2019, O'Reily
