Found 30 relevant results in 2.07s where lecturer="Joachim M. Buhmann"
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Machine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.
The school (1.9. - 12.9.2008) will discuss the recent progress and challenges in biological and medical imaging. Cutting edge techniques using a wide range of imaging mechanisms will be put in the context of selected biomedical problems. In particular, multimodal and multiscale imaging methods as well as supporting technologies such as computer aided image analysis and modeling will be discussed.
Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.
Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.
Class participants study and make a 40 minute presentation (in English) on fundamental papers of Computational Science. A preliminary discussion of the talk (structure, content, methodology) with the responsible professor is required. The talk has to be given in a way that the other seminar participants can understand it and learn from it. Participation throughout the semester is mandatory.
Informatics II (D-MAVT)
Informatik II (D-MAVT)
Students will be presented an overview of computer organization anddesign. Using the assembly language MIPS the organization levels fromlogic gates to the data path are described. Additional topics fromtheoretical and practical computer science are Turing machines,information theory, computer networks and data bases.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills.
Feature Extraction: Foundations and Applications
Feature extraction: foundations and applications
Feature extraction is an essential pre-processing step to pattern recognition and machine learning problems. Classical algorithms of feature construction and feature selection will be introduced, with applications in image processing, text processing, genomics and proteomics, and drug screening.
This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.
This class is a weekly reading group discussing research papers on causality inference from observational or experimental data. The selected papers aim at understanding machine learning techniques to infer causality, including causal graphs derived from "graphical models”.
This course will focus on inference with statistical models for image analysis. It discusses Markov random fields for image processing reasons and graphical models are for image understanding.
This short course on information theory will introduce fundamental concepts such as entropy, information, sufficiency, typicality, concentration and will present a range of topics from data coding, statistics, inference, decision-making and learning that relate in interesting ways to information theory.
Non-linear equations, Fundamentals of interpolation (points and functions), Nonlinear Least Squares, Optimization, Introduction to Symbolic computation.
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