Track 1

Machine learning

Machine learning (ML) is seen as a subset of artificial intelligence. It is the scientific study of algorithms and applied mathematics that computers use to effectively perform a particular task while not victimization express directions, counting on patterns and logical thinking instead. In order to make predictions or decisions without being explicitly programmed, machine learning algorithms build a mathematical model of sample data, known as "training data" to perform the task. These algorithms are used in the requests of email riddling, finding of network burglars, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which emphases on making forecasts using computers. The study of mathematical optimization brings methods, theory and tender domains to the field of machine learning. Data mining and deep learning are the fields within machine learning and focus on exploratory data analysis through unsupervised learning. In its presentation across business problems, machine learning is also referred to as predictive analytics.

Related Societies: Association for Computing Machinery (ACM), USA | British Automation and Robot Association (BARA) UK | Association Franchise pour Artificial Intelligence, France | Canadian Artificial Intelligence Canada | Japan Robot Association (JARA) Japan | International Federation of Robotics(IFR), Germany.