Ender Konukoglu: Catalogue data in Autumn Semester 2024

Name	Prof. Dr. Ender Konukoglu
Field	Biomedical Image Computing
Address	Biomedizinische Bildverarbeitung ETH Zürich, ETF C 107 Sternwartstrasse 7 8092 Zürich SWITZERLAND
Telephone	+41 44 633 88 16
E-mail	kender@vision.ee.ethz.ch
Department	Information Technology and Electrical Engineering
Relationship	Associate Professor

Number	Title	ECTS	Hours	Lecturers
227-0447-00L	Image Analysis and Computer Vision	6 credits	3V + 1U	E. Konukoglu, E. Erdil, F. Yu
Abstract	Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.
Learning objective	Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions
Prerequisites / Notice	Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English.
227-0919-00L	Knowledge-Based Image Interpretation	0 credits	2S	E. Konukoglu, F. Yu
Abstract	With the lecture series on special topics of Knowledge based image interpretation we sporadically offer special talks.
Learning objective	To become acquainted with selected, recent results in image analysis and interpretation.
247-0111-00L	Data Science: From Analytics to Learning	4 credits	3V	O. Akkus Ispir, E. Konukoglu
Abstract	In this module, basic paradigms and techniques in working with data will be discussed, especially towards data security, managing data decentrally, and learning from data.
Learning objective	Participants will understand some of the concepts in detail and see the mathematics behind them.
Content	This module covers the essential concepts and tools of data science. The main purpose is to provide you the basic knowledge and intuition to use data and understand how it is used. You'll explore the data landscape, understand key data science techniques, and learn how to apply them. The key topics of this module are the types of data, sources, and collection methods, data lifecycle, data-driven decision making, exploratory data analysis, experimental testing, regression models, and machine learning. Each topic will be enriched with collaborative discussions and hands-on exercise, enabling you to develop a practical understanding of how data science is leveraged across various industries.
247-0112-00L	Computer Vision Basics	2 credits	2V	E. Konukoglu
Abstract	This module will cover basic theoretical knowledge on visual recognition systems of the last two decades, mostly focusing on the most recent advancements in deep learning and convolutional neural networks.
Learning objective	Participants understand basic concepts of visual regonition and human-computer interaction systems.
Content	The content starts with an introduction to neural networks and then focuses on how they are used for computer vision tasks. The theoretical knowledge will be supported with a practical session that will allow participants to gain hands-on experience with most commonly used tools and deepen their understanding of the key concepts with examples.