Anthill Inside 2018

On the current state of academic research, practice and development regarding Deep Learning and Artificial Intelligence.

Machine Learning and Statistical Methods for Time Series Analysis

Submitted by Aravind Putrevu (@aravindputrevu) on Monday, 16 April 2018

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Technical level

Intermediate

Section

Full talk

Status

Submitted

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Total votes:  +1

Abstract

In this talk, I will present a deep algorithmic dive into the new machine learning technologies available in the Elastic Stack and how they can be applied to real datasets.

Specifically, I will focus on some of the unsupervised machine learning techniques Elastic uses, and the challenges and constraints which exist in order to provide operationally useful insight when applying these technologies to real time series data.

Outline

  • Introduction
  • Machine Learning Overview
  • Time Series Anomaly Detection
  • Demo

Speaker bio

Aravind is a loquacious person, who has something to talk about everything. He is passionate about evangelising technology, meeting developers and helping in solving their problems. He is a backend developer and has six years of backend development experience.

Previously, He worked at McAfee Antivirus as a Sr. Software Engineer in Cloud Security Domain. He has profound interest in Search, ML, Security and IoT tech. In his free time, he plays around with his pet RasPi or Arduino.

Links

Comments

  • 1
    Hari C M (@haricm) Reviewer 9 months ago

    Aravind, you have to share slides and preview video for us to evaluate this talk

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