Joachim M. Buhmann: Catalogue data in Spring Semester 2009

Name	Prof. Dr. Joachim M. Buhmann
Field	Computer Science (Information Science and Engineering)
Address	Institut für Maschinelles Lernen ETH Zürich, OAT Y 13.2 Andreasstrasse 5 8092 Zürich SWITZERLAND
Telephone	+41 44 632 31 24
Fax	+41 44 632 15 62
E-mail	jbuhmann@inf.ethz.ch
URL	http://www.ml.inf.ethz.ch/
Department	Computer Science
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
251-0526-00L	Statistical Learning Theory	5 credits	2V + 1U	J. M. Buhmann
Abstract	The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
Objective	The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Content	# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
Lecture notes	no script; transparencies of the lectures will be made available.
Literature	Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Prerequisites / Notice	Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Advanced Topics in Machine Learning can be followed without the introductory course.
251-0838-00L	Computer Science II (D-MAVT)	4 credits	2V + 1U	J. M. Buhmann, I. Sbalzarini
Abstract	Students will be presented an overview of computer organization and design based on the assembly language MIPS. Additional topics from theoretical and practical computer science are fundamental algorithmic techniques like dynamic programming, randomized and approximation algorithms, information theory, computer networks and data bases.
Objective	Überblick und Verständnis für grundlegende Prinzipien der heutigen Rechner.
Content	1) Rechnerstrukturen (Operationsprinzip eines Rechners, Von-Neumann Rechner, einfacher Datenpfad) 2) Algorithmische Prinzipien: dynamische Programmierung (Dijkstra's Algorithmus, Bellman-Ford), randomisierte Algorithmen am Beispiel von randomisiertem Quicksort, Approximationsalgorithmen für Scheduling; 3) Entropie als Informationsmass; optimale Codierungslänge 4) kryptographische Protokolle 5) Einführung in Datenbanken
Literature	Wird in der Vorlesung bekanntgegeben.
Prerequisites / Notice	Voraussetzung: Besuch von Informatik I
252-5251-00L	Computational Science	2 credits	2S	P. Arbenz, J. M. Buhmann, P. Koumoutsakos, I. Sbalzarini
Abstract	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.
Objective	Studying and presenting fundamental works of Computational Science. Learning how to make a scientific presentation.
Content	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.
Lecture notes	none
Literature	Papers will be distributed in the first seminar in the first week of the semester
551-1316-00L	CIMST Interdisciplinary Summer School on Bio-Medical Imaging	3 credits	6G	R. Kroschewski, S. M. Ametamey, Y. Barral, P. Bösiger, J. M. Buhmann, R. E. Carazo Salas, G. Csúcs, D. W. Gerlich, A. Helenius, F. Helmchen, J. A. Helmuth, T. Ishikawa, P. Koumoutsakos, S. Kozerke, P. Meraldi, M. Peter, M. Rudin, V. Sandoghdar, I. Sbalzarini, R. Schibli, B. Schuler, C. Schulze-Briese, M. Stampanoni, G. Székely, R. A. Wepf
Abstract	The school (24.9. - 04.9.2009) 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.
Objective	The students know about the possibilities and limitations of a wide range of modern imaging methods and can propose suitable methods for a given imaging problem.
Content	Cutting edge techniques using a wide range of imaging mechanisms such as magnetic resonance, positron emission, infrared and optical microscopy, electron microscopy and x-ray imaging 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 imaging analysis and modeling will be discussed. On the first day, a basic introduction to the following topics will be provided: Sources, emitters, wavelength, imaging principles, resolution, adsorption, etc. The school aims to point out possibilities of the integration of different imaging methods.
Lecture notes	None
Prerequisites / Notice	We plan to admit about 50 Master or PhD students with background in either biology, chemistry, mathematics, physics, computer science or engineering (internally and from abroad). The school will be taught in English. Admission will be given via a selection process based on the curriculum vitae and a statement of purpose. For details of the program and the application procedure please consult http://www.cimst.ethz.ch/education/summer_school/09/application The application deadline is 1st June 2009. A decision will be given by 16th June 2009.