Track 01: Machine Learning

The application of Artificial Intelligence (AI) that helps systems improving ability to learn and improve automatically without being explicitly programmed is called Machine Learning. It focuses on the development of computer programs which can access data and used to learn themselves. Machine learning is a subset of Artificial Intelligence and Deep Learning is Machine learning but applied to large data sets. Machine Learning (ML) is involved in most of the AI works because intelligent behavior needs considerable knowledge and ultimately learning is the easiest way to get the knowledge

There is a wide range of uses of Machine Learning, Few are listed below:

• Can identify trends and patterns very easily
• Human intervention is not required
• Used in handling multi-dimensional data
• And also many more wide applications like Medical diagnosis, Statistical Arbitrage, Learning, associations, Classification, Prediction, Extraction, and also for Image recognition and face recognition.

Machine Learning Conference is focused on spreading more knowledge to the new era of Intelligence

Track 02: Deep Learning

The class of Machine Learning algorithms that uses various layers to extract higher level features from the raw inputs progressively is called as Deep Learning. Let’s talk about an example whereas in Image processing, lower layers may identify edges, and higher layers may identify the concepts relevant to humans this includes digits, letters or faces.  One of the subsets of Machine Learning in AI is Deep Learning which has networks capable of learning unsupervised from data that is unlabeled or unstructured. Deep Learning is also called as deep neural learning or deep neural network.

Track 03: Artificial Intelligence

The simulation of human intelligence processes by machines especially computer systems is called Artificial intelligence. This includes learning, reasoning, and self-correction.  It is also explained as the area of computer science which deals with the creation of machines which are intelligent enough to work and react like humans. Some of the activities where artificial intelligence is involved include Speech recognition and face recognition. Types of Artificial Intelligence include: Reactive machines, Limited memory, Theory of mind, Self-awareness.

Track 04: Artificial Neural Networks

Artificial Neural Networks or connectionist systems are one of the main tools used in machine Learning, The systems that are inspired by, but not identical to biological neural networks that constitute animal brains is called as Artificial neural networks (ANN), These systems learn to perform tasks by taking examples without being programmed with task-specific rules. The tasks which the linear programs cannot perform can be performed by artificial neural network, ANN can handle the missing Data and they need not to be reprogrammed because they can learn. 

Track 05: Healthcare & Medical Sciences

There are many uses of machine learning in the field of Healthcare and Medical Sciences. This works effectively in the presence of huge data which can be used as synchronizing the information and using it in improving healthcare treatments and infrastructure.  It has capacity to help so many people, to save money as well as lives. It has great potential for healthcare which will be used for discovery, diagnosis, decision making.

Track 06: Robotic Process Automation (RPA)

The use of software with Machine Learning capabilities and Artificial Intelligence which is used to handle huge-volume and repeatable tasks which humans used perform earlier. For an example: tasks like queries, calculations and maintenance of records and transaction. These have strong technical similarities to graphical user interface testing tools which can automate interactions with the Graphical User Interface (GUI),  As per the recent reports release in 2019 the CAGR of Robotic Process Automation (RPA) market in India at 20% annually.

Track 07: Affective Computing

This is also called as Facial expression Detection or Artificial Emotional Intelligence, this is used to measure, simulate, and react to human emotions. Emotional recognition is identifying human emotions from facial and as well as verbal expressions. This is something humans do automatically but computational methodologies have been developed.

Track 08: Virtual Reality (VR) and Gaming

The most freely known utilization of machine learning in games is likely the use of deep learning operators that rival proficient human players in complex technique games. There has been a huge utilization of machine learning on games such as Atari/ALE, Doom, Minecraft, Starcraft, and car racing. The reason Game developers look to use artificial intelligence in game development is because there are essentially two problems in game development that machine learning can address in various ways they are playing the game against human players and helping build the game dynamically for players.

Track 09: Computer Vision

Computer vision is a field that deals with how computers can be made        to gain high level understanding from images (digital) or videos, it tries to automate tasks that the human visual system can do from the point of engineering, this deal with the automatic extraction, analysis and understanding of useful information from single image or sequence of images. This is a field of artificial intelligence that trains computers to interpret and understand the visual world is called Computer vision, this uses digital images from cameras, videos and deep learning models, and machines which can accurately identify and classify objects and react to that.

Track 10: Big Data Analytics

The complex process of checking large          and varied data sets (or) big data to uncover information is called Big data analytics. This can examine huge data like hidden patterns, unknown correlations, market trends, and customer preferences this will help many organizations make informed business decisions. There are various uses of big data analytics which includes the hospitality industry, healthcare companies, public service agencies and also retail business. The software tools used such as data mining, Hadoop, text mining. Big data is taken from text  , audio, video, and images. Big data is analyzed by organizations

The application of artificial intelligence which enables software applications to be more precise in predicting results without being definitively programmed is called Machine Learning. According to the global market machine learning market was shown an estimated value at around USD 1.59 billion in 2017 and approximately it is expected to reach USD 20.84 billion in 2024 and growing at a CAGR of 44.06% between 2017 and 2024. Mas per the artificial intelligence experts idea by 2050 all the intellectual tasks that are performed by humans can be accomplished by artificial intelligence technology. Machine learning technology can be applied in financial services, healthcare, government, transportation, oil and gas, bio informatics, computational anatomy, marketing and sales, manufacturing, and more.

Machine Learning 2019

On account of all our brilliant speakers, Conference participants and Exhibitors for making this occasion an incredible achievement. We trust you made some good memories there and all of you will keep great recollections of our meeting

6th World Machine Learning and Deep Learning Congress was held during October 24-25, 2019 at Helsinki, Finland. The meeting was set apart with the participation of Editorial Board Members of upheld Journal of Advances in Robotics and Automation, Scientists, young and splendid specialists, business delegates and gifted understudy communities representing more than of 25 nations, who made this gathering productive and fruitful.