Difference between revisions of "Big Data Parallel Programming (7.5 credits)"
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| + | Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements. | ||
Latest revision as of 16:37, 25 January 2022
Course Code: DT8013
Short description: Advanced topics in Data Mining
Course Level: Advanced
Course page: http://tinyurl.com/DT8013
Teacher from ISlab involved in Big Data Parallel Programming (7.5 credits): Sławomir Nowaczyk
Examiner of Big Data Parallel Programming (7.5 credits): Sławomir Nowaczyk
Processing huge amounts of data is at the core of data mining, deep learning and real-time autonomous decision making. All these are in turn at the core of modern artificial intelligence applications. Data can reside more or less permanently in the cloud and accessed via distributed file systems and / or be streamed in real time from multiple sensors at very high rates. Access to data as well as processing is done using very well engineered frameworks where both storage and processing are distributed and processing is done in parallel. The purpose of this course is to introduce you to this infrastructure including parallel programming for the implementation of these frameworks. This should enable you to judge how to choose a framework for your applications, identify pros and cons, suggest improvements and even implement improvements.
