The ongoing data deluge driven by the increasing digitalization of science, society and industry, leads to a significant increase in demand for data storage, processing and analytics within several industrial domains. Sciences and industry are overwhelmed by the need to store large amounts of transactional and machine-generated data resulting from the customer, service and manufacturing processes. Examples of machine-generated data are server logs as well as sensor data that is generated in finer granularities and frequencies. Further, datasets are often enriched with web and open data from social media, blogs or other open data sources. The Internet of Things (IoT) will further blur the boundaries between the physical and the digital world causing an even further increase in the digital footprint of the world. In this course, we will learn about data applications and their requirements. Further, we will discuss the core infrastructure necessary to handle the large data volumes and analytical problems. As part of the exercises students will utilize different frameworks, e.g., MapReduce, Spark and Tensorflow/Keras, to implement different algorithms.
This class will cover the following topics:The lecture is aimed at master's and bachelor's degree students in the computer science and data science programs.
While LMU is closed, most teaching happens currently online. As teachers, we ask you to be forgiving if things should not work perfectly right away, and we hope for your constructive participation. In this situation, we would also like to explicitly point out some rules, which would be self-evident in real life:
If you violate one of these rules, you can expect to be expelled from the respective course, and we reserve the right for further action. With all others, we are looking forward to the joint experiment of an "online semester".
Die Vorlesung ist zweistündig und besitzt eine Übungen (6 ECTS).
The final grade of the event is determined based on a project work and an oral examination. In order to be admitted, the exercise must be passed. For the lecture to be successful, a grade of at least 4 must be achieved.
Time / Dates : March, 27, 29 - April 1, and April 6, 10, 2021
Location: Zoom (Invite will be send to all participants)
Enrollment: The places will be allocated via UniWorX: Uni2Work-Application.
We ask you to describe your previous knowledge in your application and to motivate your participation.
Introduction, HPC, Hadoop Distributed Execution Engines: Spark and SQL, Introduction Machine Learning Deep Learning (Computer Vision) NLP and Scalable ML Benchmarks, MLOps, Responsible AI Exercise Solutions
For questions or inquiries please contact Andre Luckow.