Suchergebnis: Katalogdaten im Frühjahrssemester 2019
|Vertiefung in Computational Science|
|Kernfächer der Vertiefung in Computational Science|
|263-2300-00L||How To Write Fast Numerical Code |
Number of participants limited to 84.
Prerequisite: Master student, solid C programming skills.
Takes place the last time in this form.
|W||6 KP||3V + 2U||M. Püschel|
|Kurzbeschreibung||This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality such as matrix operations, transforms, and others. The focus is on optimizing for a single core. This includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.|
|Lernziel||Software performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.|
|Inhalt||The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture. |
This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.
Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
|Wahlfächer der Vertiefung in Computational Science|
|252-0526-00L||Statistical Learning Theory||W||7 KP||3V + 2U + 1A||J. M. Buhmann|
|Kurzbeschreibung||The course covers advanced methods of statistical learning :|
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.
|Lernziel||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.|
|Inhalt||# 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.|
# 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.
# Statistical physics models: approaches for large systems approximate optimization, which originate in the statistical physics (free energy minimization applied to spin glasses and other models); sampling methods based on these models
|Skript||A draft of a script will be provided; |
transparencies of the lectures will be made available.
|Literatur||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
|Voraussetzungen / Besonderes||Requirements: |
knowledge of the Machine Learning course
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 Statistical Learning Theory can be followed without the introductory course.
|261-5120-00L||Machine Learning for Health Care |
Number of participants limited to 78.
Previously called Computational Biomedicine II
|W||4 KP||3P||G. Rätsch|
|Kurzbeschreibung||The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems.|
|Lernziel||During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions.|
|Inhalt||The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: |
1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc.
2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them.
3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them.
4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges.
|Voraussetzungen / Besonderes||Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line|
Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II.
|Seminar in Computational Science|
|252-5704-00L||Advanced Methods in Computer Graphics |
Number of participants limited to 24.
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|W||2 KP||2S||M. Gross, O. Sorkine Hornung|
|Kurzbeschreibung||This seminar covers advanced topics in computer graphics with a focus on the latest research results. Topics include modeling, rendering, visualization,|
animation, physical simulation, computational photography, and others.
|Lernziel||The goal is to obtain an in-depth understanding of actual problems and |
research topics in the field of computer graphics as well as improve
presentation and critical analysis skills.
|261-5113-00L||Computational Challenges in Medical Genomics |
Number of participants limited to 20.
|W||2 KP||2S||A. Kahles, G. Rätsch|
|Kurzbeschreibung||This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion.|
|Lernziel||Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. |
In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class.
|Inhalt||The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.|
Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers.
|Voraussetzungen / Besonderes||Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine.|
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