Modules

Prerequisites

Contents

Students will learn about specific state-of-the-art problems and methodological approaches for large-scale computation in machine learning. The topics range from parallel computation on individual machines, to implicit parallelism frameworks on compute clusters, and algorithms and data structures supporting efficient exact or approximate computation with massive data sets in and out of core.

Learning outcomes

After successfully completing the module, students will be familiar with state-of-the-art problems and methodological approaches in the intersection of large-scale computing and machine learning; that is, computing at scale for machine learning and machine learning for large-scale computation. They will have the ability to familiarize themselves with current research in the area from original research literature, to participate in a technical discussion within the context of international science, and present scientific content in written and oral form.

Assessment

• Seminar presentation, 30-45 min depending on the subject (40%) • Report on the topic of the seminar presentation, 10 pages (40%) • Active participation (20%) A student passed the module, if he/she achieves 75% of the total.

Remarks

• Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Seminars or Laboratories“ • Study programme Informatik M.Sc.: Compulsory elective module in complex "Seminare oder Praktika" (level 400)