Modules

Prerequisites

Contents

The focus will be on the following areas: • A review of memory-compute co-location and its impact on big data computations. • Solving Machine Learning (ML) work loads using explicit parallelism, specifically multi-threaded computation on an individual machine. • Introduction of implicit parallelism programming models as implemented for example in MapReduce, Spark and Ray and their application in ML. • Probabilistic algorithms such as sketching algorithms (incl. CountMinSketch, Hy-perLogLog) or Bloom filters. • Implementing ML methods using index data structures such as suffix or kd-trees.suffix or kd-trees.

Learning outcomes

After successfully completing the module, students have an overview on how to solve large-scale computational problems in data science and machine learning. They know parallel approaches from multi-threaded computation on individual machines to implicit parallelism frameworks on compute clusters. They are familiar with algorithms and data structures supporting efficient exact or approximate (e.g. sketching) computation with massive data sets in and out of core. They are able to implement the algorithms. They can assess which methods can be used in a given situation.

Assessment

Prerequisite: • 3 out of 4 successfully completed quizzes (45 minutes each) in the exercises Final Module Examination: • Written examination, 120 min. OR • Oral examination, 30-45 min. The nature of the examination will be announced in the first lecture.

Remarks

• Study programme Informatik M.Sc.: Compulsory elective module in complex „Praktische Informatik”(level 400) • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Learning and Reasoning” • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Data Science Applications”