Introduction to Outlier Detection Methods (Part 2) - Wolfram Livecoding Session скачать в хорошем качестве

Introduction to Outlier Detection Methods (Part 2) - Wolfram Livecoding Session

Andreas Lauschke, a senior mathematical programmer, live-demos key Wolfram Language features useful in data science. In this seventh session, the introduction to outlier detection methods continues, and the basics of continuous probability theory are recapped. Then learn about the built-in function FindAnomalies (new in Version 12), including a mathematical introduction to better understand its inner workings. FindAnomalies is applied to various distributions. See the outputs, including plots, of FindAnomalies and learn how to interpret them. Examples include the function applied to data from the 2016 US presidential election, as a visualization tool for technical trading systems and on major/minor chord detection of sound samples from a church organ. Caveats about the use of FindAnomalies are discussed as well. Notebook materials are available at: https://www.notebookarchive.org/id/20... You can directly interact with Andreas through Wolfram Community: https://wolfr.am/GDrFGeWF More information about Wolfram's approach to data science can be found at: https://www.wolfram.com/featureset/da... Watch the full series of data science videos by Andreas Lauschke: Session #1: https://wolfr.am/HlmE40MT Session #2: https://wolfr.am/HlmMydgB Session #3: https://wolfr.am/HlmMzmFX Session #4: https://wolfr.am/HlmMAxJT Session #5: https://wolfr.am/HlmMBJb8 Session #6: https://wolfr.am/HlmMCUjR Andreas would like to add the following corrections: 09:55 Should have said "non-negative" but said "negative" 01:07:22 , 01:07:55 and 01:08:05 Should have said "FindAnomalies" but said "FindMinimum"