Introduction to machine learning

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SUMMARY

This discussion focuses on introductory resources for machine learning (ML), emphasizing the need to understand the advantages and disadvantages of various ML methods without delving into implementation details. Participants recommend revisiting foundational statistics texts, specifically Hogg and Craig's "Introduction to Mathematical Statistics" (4th edition), Bevington's "Data Reduction and Error Analysis," and Taylor's "An Introduction to Error Analysis." A highly recommended free resource is the "Elements of Statistical Learning" by Hastie, available at Stanford's website.

PREREQUISITES
  • Basic understanding of machine learning concepts
  • Familiarity with statistical methods
  • Knowledge of error analysis techniques
  • Access to online academic resources
NEXT STEPS
  • Read "Elements of Statistical Learning" by Hastie
  • Review "Introduction to Mathematical Statistics" (4th edition) by Hogg and Craig
  • Study "Data Reduction and Error Analysis" by Bevington
  • Explore "An Introduction to Error Analysis" by Taylor
USEFUL FOR

This discussion is beneficial for beginners in machine learning, statisticians looking to refresh their knowledge, and anyone interested in understanding the foundational concepts of ML methods and their statistical underpinnings.

Frabjous
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Can anyone provide a good introductory reference to machine learning. Right now, I am interested in understanding the advantages and disadvantages of the various ML methods. I am not currently interested in detailed descriptions of their implementation.

I am probably going to have to brush up on my statistics. So I am also looking for a reference for this. Back in the day, I studied

Hogg,Craig Introduction to Mathematical Statistics (4th edition)
Bevington Data Reduction and Error Analysis
Taylor An Introduction to Error Analysis

although it is questionable how much I remember.
 
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