Search result: Catalogue data in Autumn Semester 2021

Computational Science and Engineering Master Information
Core Courses
In the ‘core courses’ subcategory, at least two course units must be successfully completed.
NumberTitleTypeECTSHoursLecturers
401-4671-00LAdvanced Numerical Methods for CSE
Offered for the last time in HS 2021
W9 credits4V + 2U + 1PS. Mishra
AbstractThis course will focus on teaching different advanced topics in numerical methods for science and engineering. The main aim would be introduce novel algorithms and discuss their implementation.
Objective--Presentation of state of the art numerical methods in computational fluid dynamics.
--Advanced implementation in C++
-- Introduction of the role of data in scientific computing, particularly in the context of uncertainty quantification (UQ).
ContentA selection of the following topics will be covered:

1. Advanced numerical methods in fluid dynamics:
-- Finite volume schemes
-- High-resolution schemes on both structured and unstructured grids

2. Uncertainty quantification in fluid dynamics
-- Modeling of uncertainty in terms of random fields.
-- Monte Carlo methods
-- Multi-level Monte Carlo methods.
-- Quasi-Monte Carlo methods.
Lecture notesLecture material will be created during the course and will be made available.
Prerequisites / Notice- Familiarity with basic numerical methods
(as taught in the course "Numerical Methods for CSE").
- Knowledge of numerical methods for differential equations (as covered in the course "Numerical Methods for Partial Differential Equations").
252-0535-00LAdvanced Machine Learning Information W10 credits3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
AbstractMachine 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.
ObjectiveStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
ContentThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Prerequisites / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
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