Ai Machine Learning Conferences 2018

Top Conferences for AI Machine Learning 2018
Top Conferences for AI Machine Learning 2018

Top Conferences for Machine Learning 2018

AI Online Program | 2018 Tech Conference

Machine Learning Summit | Transform AI

Find below all the top conferences for artificial intelligence (AI) and machine learning (ML) 2018 around the world.

Whether you're interested in machine learning conferences 2018 or Google machine learning crash course or even Sapo Leonardo machine learning, co ban and Apple machine learning journal are putting up hands on ai online program conferences/summit. Not to mention Facebook ai shut down.

One or two things on deeplearning ai as facebook ai creates own language, ai-tv, applied ai course or google ai walking.

You can learn with google ai and have google ai residency or ai sophia (or ai robot sophi) with expo.

Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed".

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics.

These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.

This is an opportunity to explore hands-on machine learning with scikit-learn and tensorflow or python machine learning by example. Which shall it be?

Why Python Machine Learning?
What the heck is it?

Why python machine learning you may ask? This allows you to explore difference between machine learning and deep learning or Udemy/Que Es/XKCD machine learning or overfitting in machine learning.

AI (artificial intelligence) vs Machine Learning"

Machine Learning Certification

With machine leraning certification, you could get a machine learning jobs in India.

Here are the upcoming/top/best/list of machine learning conferences:-

Month "MACHINE LEARNING 2018 DESCRIPTION"
APRILCompany: International Conference on Data Engineering Workshops (ICDE),
Date: Monday 16th-20th April,
Country: Paris/France,
URL: https://icde2018.org/
APRILCompany: Conference of the European Chapter of the Association for Computational Linguistics (EACL),
Date: Tuesday 3rd-7th April,
Country: Valencia/Spain,
URL: http://eacl2017.org/
APRILCompany: International Conference on Artificial Intelligence and Statistics (AISTATS),
Date: Monday 9th-11th April,
Country: Blanca/Spain,
URL: http://www.aistats.org
APRILCompany: International Symposium on Information Processing in Sensor Networks,
Date: Wednesday 11th-13 April,
Contry: Portugal,
URL: http://ipsn.acm.org/2018/cfp.html?v=1
APRILCompany: IAPR International Workshop on Document Analysis Systems,
Date: Tuesday 24th-24th April,
Contry: Vienna/Austria,
URL: https://das2018.caa.tuwien.ac.at/en/
APRILCompany: ESANN: European Symposium on Artificial Neural Networks,
Date: Wednesday 25th-27th April,
Contry: Belgium,
URL: https://www.elen.ucl.ac.be/esann/index.php?pg=welcome

Time is money when it comes to fighting fraud. Organized crime rings, fueled with billions of compromised data records, are systematically and methodically targeting the financial institutions (FIs) services value chain with sophisticated card fraud, application fraud, and retail account takeover (ATO) attacks.

The volume of these attacks continues to increase, since there is very little in the way of adverse consequences (i.e., jail time). To understand the potential of Machine Learning (ML), it's important to understand what the technology is and does. All too often, the term "ML" is used interchangeably with "artificial intelligence"; however, ML is actually a subset of artificial intelligence.

ML encompasses analytics techniques that can identify patterns of behavior through iterative optimization.

The use of ML analytics for fraud prevention is rapidly gaining traction in financial services. Fraud is moving too fast for the legacy approaches that rely on rules and annual model refreshes to be effective.

FIs need advanced analytics technology that can evolve rapidly and keep pace with the progression of fraud attacks so they can prevent losses while maintaining a positive customer experience.