This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
This weeks guest post comes from KDD (KnowledgeDiscovery and Data Mining). Honestly, KDD has been promoting data science way before data science was even cool. KDD 2020 is a dual-track conference, offering distinct programming in research and applied data science. 1989 to be exact. The details are below.
ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, KnowledgeDiscovery and MachineLearning for 26 th Annual Conference in San Diego.
Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). Machinelearning provides the technical basis for data mining. He possesses great interest in machinelearning, astronomy and history.
by OMKAR MURALIDHARAN Many machinelearning applications have some kind of regression at their core, so understanding large-scale regression systems is important. But most common machinelearning methods don’t give posteriors, and many don’t have explicit probability models. For more on ad CTR estimation, refer to [2].
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content