Call for Abstract
Scientific Program
7th World Machine Learning and Deep Learning Congress, will be organized around the theme “New way of Communicating your wishes to a Computer”
Machine Learning 2020 is comprised of 10 tracks and 53 sessions designed to offer comprehensive sessions that address current issues in Machine Learning 2020.
Submit your abstract to any of the mentioned tracks. All related abstracts are accepted.
Register now for the conference by choosing an appropriate package suitable to you.
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 1-1Neural Networks
- Track 1-2Reinforcement Learning
- Track 1-3Predictive Learning
- Track 1-4Unsupervised Learning
- Track 1-5Quantum Machine Learning
- Track 1-6Adversarial Machine Learning
- Track 1-7Computational Learning Theory
- Track 1-8Convolutional neural network
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 2-1Deep learning frameworks
- Track 2-2Business Management
- Track 2-3Automated Reasoning
- Track 2-4Robotics
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 3-1Artificial Narrow Intelligence
- Track 3-2Artificial General Intelligence
- Track 3-3Artificial Super Intelligence
- Track 3-4Speech Recognition
- Track 3-5Voice Assistants
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 4-1Recurrent Neural Networks
- Track 4-2Biological models and applications
- Track 4-3Learning vector quantization
- Track 4-4Feedforward Neural Network
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 5-1Drug Discovery and Manufacturing
- Track 5-2Medical Imaging Diagnosis
- Track 5-3Personalized Medicine
- Track 5-4Smart Health Records
- Track 5-5Clinical Trial and Research
- Track 5-6Crowdsourced Data Collection
- Track 5-7Better Radiotherapy
- Track 5-8Outbreak Prediction
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 6-1Insurance
- Track 6-2Banking and Financial Services
- Track 6-3Knowbots
- Track 6-4Chatbots and virtual agents
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 7-1Cognitive science
- Track 7-2Emotion recognition systems
- Track 7-3Speech descriptors
- Track 7-4Emotion classification
- Track 7-5Emotion classification
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 8-1Modeling complex systems
- Track 8-2Game designing and Development
- Track 8-3Adaptive or intelligent behaviors in non-player characters
- Track 8-4Digital Creativity
- Track 8-5Real-Time Strategy
- Track 8-6Augmented Reality
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 9-1Self-driving cars
- Track 9-2Machine vision
- Track 9-3Algorithmic methods
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
- Track 10-1Behavioural Analytics
- Track 10-2Graph Analytics
- Track 10-3Journey Sciences
- Track 10-4Hyper Personalisation
- Track 10-5Agile Data Science
- Track 10-6The Experience Economy