Algorithmic Techniques for Modern Data Models |
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Algorithmic Techniques for Modern Data Models
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Overall Course Objectives
To know, apply, analyze, and design algorithms in modern data models: 路 Probabilistic, time-dependent and approximate summary techniques, such as algorithms in the online, dynamic, or streaming model of computation, algorithms for data sketching, and data sampling algorithms. 路 Distributed and massively parallel computation techniques, such as algorithms in MapReduce, BSP, and multicore models, algorithms for communication models, and external memory and cache-oblivious algorithms. 路 Compressed computation techniques, such as approximate nearest neighbors in high-dimensional spaces, clustering algorithms, and compressed indexing and searching. See course description in Danish Learning Objectives Describe an algorithm or a data structure in a comprehensible manner, i.e., accurately, concise, and unambiguous.Prove correctness of algorithms and data structures in different data models, such as the streaming, parallel, and external memory models. Analyze, evaluate, and compare the performance of algorithms and data structures in different data models, such as the streaming, parallel, and external memory models. Analyse, evaluate, and compare the suitability of different data models in a given setting.Apply and extend relevant algorithmic techniques (e.g. sketching, map-reduce, compressed computation) in modern data models.Design algorithms that solve a given problem in a given modern data model.Systematically identify and analyse problems and make informed choices for solving the problems based on the analysis. Argue clearly for the choices made when solving a problem.Express oneself in writing at a scientific level.Course Content State-of-the-art algorithmic techniques for modern data models, such as probabilistic, time-dependent and approximate summary techniques, distributed and massively parallel computation techniques, and compressed computation techniques. Recommended prerequisites 02110, Basic courses in algorithms and data structures (comparable to 02105/02326 + 02110). Mathematical maturity. Teaching Method Lectures and exercises. Faculty Inge Li G酶rtz Contact or read more about Inge Li
Eva Rotenberg
Contact or read more about Eva
Philip Bille
Contact or read more about Philip
See course in the course database. |
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