Talk:Statistical learning theory

Online learning
I'm taking a free online course MOOC (Massive Open Online Course) from Stanford University on "Statistical Learning" — FYI.
 * Here is a link to see what is covered in the course: https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/

After taking the course, I'll look to help improve the Article here. TIA, Charles Edwin Shipp (talk) 01:03, 30 January 2014 (UTC)
 * Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2, starts Jan 21)
 * Week 2: Linear Regression (Chapter 3, starts Jan 25)
 * Week 3: Classification (Chapter 4, starts Feb 1)
 * Week 4: Resampling Methods (Chapter 5, starts Feb 8)
 * Week 5: Linear Model Selection and Regularization (Chapter 6, starts Feb 15)
 * Week 6: Moving Beyong Linearity (Chapter 7, starts Feb 22)
 * Week 7: Tree-based Methods (Chapter 8, starts Mar 1)
 * Week 8: Support Vector Machines (Chapter 9, starts Mar 8)
 * Week 9: Unsupervised Learning (Chapter 10, starts Mar 15)

Econophysics and other dirty applications
For some reason, User:I3roly considers that certains applications of SLT, e.g. in statistical finance, should not appear on this page. Would other editors kindly explain? Jala Daibajna (talk) 14:14, 4 May 2014 (UTC)

Baseball?
Is baseball really a notable enough example of an application of statistical learning theory that it deserves to be in the lead paragraph? -- Pingumeister(talk) 13:15, 21 September 2019 (UTC)
 * No, it does certainly not. Moreover, the cited paper "Gagan Sidhu, Brian Caffo. Exploiting pitcher decision-making using Reinforcement Learning. Annals of Applied Statistics" is about reinforcement learning which is not part of statistical learning, so it has nothing to do with this article. I have removed it from the lead, but an author keeps putting it back without explanations.  K œrte F a  {ταλκ}  09:40, 17 December 2019 (UTC)

Nice Introduction
Kudos to the person who wrote the introduction - it really is an "introduction" in every sense of the word! Most articles in "machine learning" seem to assume the reader is already familiar with the concepts, which makes them hard to read. And I imagine there's more interest on the topic given the success of ChatGPT and GitHub Copilot, so if more articles could be edited to start with a good introduction such as the one in this article, I believe it would greatly help bring machine learning to a wider audience! — Preceding unsigned comment added by Kenileb (talk • contribs) 22:03, 28 November 2023 (UTC)