Search result: Catalogue data in Autumn Semester 2018
Computational Biology and Bioinformatics Master More informations at: Link | ||||||
Master Studies (Programme Regulations 2011) | ||||||
Advanced Courses and Methods of Computer Science | ||||||
Methods of Computer Science | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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252-0057-00L | Theoretical Computer Science | W | 7 credits | 4V + 2U | J. Hromkovic, H.‑J. Böckenhauer | |
Abstract | Concepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars? | |||||
Objective | Learning the basic concepts of computer science along their historical development | |||||
Content | This lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems. The main topics of the lecture are: - alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks - finite automata, regular and context-free grammars - Turing machines and computability - complexity theory and NP-completeness - design of algorithms for hard problems | |||||
Lecture notes | The lecture is covered in detail by the textbook "Theoretical Computer Science". | |||||
Literature | Basic literature: 1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014. 2. J. Hromkovic: Theoretical Computer Science. Springer 2004. Further reading: 3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997 4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006. 5. I. Wegener: Theoretische Informatik. Teubner. More exercises and examples in: 6. A. Asteroth, Ch. Baier: Theoretische Informatik | |||||
Prerequisites / Notice | During the semester, two non-obligatory test exams will be offered. | |||||
252-0535-00L | Advanced Machine Learning | W | 8 credits | 3V + 2U + 2A | J. M. Buhmann | |
Abstract | 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. | |||||
Objective | Students 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. | |||||
Content | The 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 notes | No lecture notes, but slides will be made available on the course webpage. | |||||
Literature | C. 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 / Notice | The 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. | |||||
401-0663-00L | Numerical Methods for CSE | W | 8 credits | 4V + 2U + 1P | R. Alaifari | |
Abstract | The course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++. | |||||
Objective | * Knowledge of the fundamental algorithms in numerical mathematics * Knowledge of the essential terms in numerical mathematics and the techniques used for the analysis of numerical algorithms * Ability to choose the appropriate numerical method for concrete problems * Ability to interpret numerical results * Ability to implement numerical algorithms afficiently | |||||
Content | 1. Direct Methods for linear systems of equations 2. Least Squares Techniques 3. Data Interpolation and Fitting 4. Filtering Algorithms 8. Approximation of Functions 9. Numerical Quadrature 10. Iterative Methods for non-linear systems of equations 11. Single Step Methods for ODEs 12. Stiff Integrators | |||||
Lecture notes | Lecture materials (PDF documents and codes) will be made available to the participants through the course web page: Link | |||||
Literature | U. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011. A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000. W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006. M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002 P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002 | |||||
Prerequisites / Notice | The course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves. |
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