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Previous Speakers

Shabir Momin

Shabir Momin

ZengaTV Singapore

Anu Kukar

Anu Kukar

KPMG Australia

Tilila El Moujahid

Tilila El Moujahid

Microsoft United Arab Emirates

Erwin E. Sniedzins

Erwin E. Sniedzins

Mount Knowledge Inc. Canada

Samir El-Masri

Samir El-Masri

Digitalization.Cloud United Arab Emirates

Sylvester Juwe

Sylvester Juwe

British Gas UK

Harshavardhana Kikkeri

Harshavardhana Kikkeri

Kaaya Tech Inc USA

Jayatu Sen Chaudhury

Jayatu Sen Chaudhury

American Express India

Machine Learning 2019

About Conference


MEConferences team cordially invites all participants across the world to attend the 6th World Machine Learning and Deep Learning Congress (Machine Learning 2019) which is going to be held during October 14-15, 2018 in Helsinki, Finland. The main theme of the conference is “Making world a new place with technology". This conference aimed to expand its coverage in the areas of Artificial Intelligence, Machine Learning and Deep Learning where expert talks, young researcher’s presentations will be placed in every session of the meeting will be inspired to keep up your enthusiasm. We feel our expert Organizing Committee is our major asset, however, speakers are what make events stand out. 6th World Machine Learning and Deep Learning Congress is bringing the most innovative minds, practitioners, experts and thinkers to inspire and present to the delegates new innovative ways to work and innovate through their data. Your presence over the venue will add one more feather to the crown of Machine Learning 2019.

Machine Learning is a method of teaching computers how to perform complex tasks that cannot be easily described or processed by humans and to make predictions. It is a combination of Mathematical Optimization and Statics. On the other hand, Deep Learning is the subset of ML that focus even more narrowly like a neuron level to solve any problem. Machine Learning 2018 is comprised of the following sessions with 20 tracks designed to offer comprehensive sessions that address current applications, discoveries, and issues of Machine Learning and Deep Learning.

Who attends?

  • CIOs / GCIOs
  • CTOs / CDOs
  • President / Vice president
  • Chairs / Directors
  • Data Scientists / Developers
  • Startup Professionals
  • Scientists / Researchers
  • Professors

Industry Verticals:

  • Banking
  • Financial Services
  • Insurance
  • Telecommunications
  • Media
  • Transport
  • Healthcare
  • Pharmaceuticals
  • eCommerce & Retail
  • Oil & Gas
  • Energy
  • Infrastructure

And last but not the least……….

  • Anyone interested in Artificial Intelligence, Machine Learning & Deep Learning and thrives to make the future developed and better

Sessions / Tracks

Track: Artificial Intelligence

Artificial Intelligence is a technique which enables computers to mimic human behavior. In other words, it is the area of computer science that emphasizes the creation of intelligent machines that work and reacts like humans. With increasing world of AI, knowledge transfer is also very much necessary. For that Machine Learning Conferences has added this very important topic of Artificial Intelligence.

Types of Artificial Intelligence:

•           Narrow Artificial Intelligence - Narrow artificial intelligence is also known as weak AI. It is an artificial intelligence that mainly focuses on one narrow task. Narrow AI is defined in contrast to either strong AI or artificial general intelligence. All currently existing systems consider the artificial intelligence of any sort is weak AI at most. It is commonly used in sales predictions, weather forecasts & playing games. Computer vision & Natural Language Processing (NLP) is also a part of narrow AI. Google translation engine is a good example of narrow Artificial Intelligence

•           Artificial General Intelligence
•           Artificial Super Intelligence

 

Track: Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed and to make intelligent decisions. It also enables machines to grow and improve with experiences. It has various applications in science, engineering, finance, healthcare, and medicine.

Advantages of Machine Learning-

•           Useful where large-scale data is available
•           Large-scale deployments of Machine Learning beneficial in terms of improved speed and accuracy
•           Understands non-linearity in the data & generates a function mapping input to output (Supervised Learning)
•           Recommended for solving classification and regression problems
•           Ensures better profiling of customers to understand their needs
•           Helps serve customers better and reduce attrition

And many more………

This Machine Learning Conference is focused on adding more value & knowledge to the revolutionary era of Intelligence.

 

Track: Deep Learning

Deep Learning is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Machine Learning Conferences has added the topic of Deep Learning Conferences which will clear the doubts & will add more knowledge from the most innovative minds throughout the globe.

 

Track: Deep Learning Frameworks

Deep Learning is a subset of Machine Learning which deals with deep neural networks. It is based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Deep Learning is able to solve more complex problems and perform greater tasks. Deep Learning Framework is an essential supporting fundamental structure that helps to make the complexity of DL little bit easier.

 

Track: AI & Machine Learning in HealthCare & Medical Science

Machine learning works effectively in the presence of huge data. Medical science is yielding a large amount of data daily from research and development (R&D), physicians and clinics, patients, caregivers etc. These data can be used for synchronizing the information and using it to improve healthcare infrastructure and treatments. This has the potential to help so many people, to save lives and money. As per a research, big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers and regulators. Due to the presence of enormous data in Healthcare, Machine Learning Conferences are adding medical Science topic in their every meetup.

 

Track: Artificial Neural Networks (ANN)

A human brain has neurons that help in adaptability, learning ability & to solve any problem. Unlike the Human brain, computer scientists dreamt for computers to solve the perceptual problems that fast. And hence, the ANN model came into existence. Artificial Neural Networks is nothing but a biologically inspired computational model that consists of processing elements (neurons) and connections between them, as well as of training and recall algorithms. Artificial Neural Networks (ANN) Conference will help to build relationships with the most eminent persons in the field.

 

Track: Natural Language Processing (NLP) and Speech Recognition

Natural Language Processing (NLP) is a subset of artificial intelligence that focuses on system development that allows computers to communicate with people using everyday language. Natural language generation system converts information from computer database into readable human language and vice versa.

The field of NLP is divided into 2 categories:-

•           Natural Language Understanding (NLU)
•           Natural Language Generation (NLG)

 

Track: Pattern Recognition

Pattern Recognition is a classification of Machine Discovering that predominantly concentrates on the acknowledgment of the structure and regularities in detail; however, it is considered almost similar to machine learning. Pattern Recognition has its cause from engineering, and the term is known with regards to Computer vision. Pattern Recognition, for the most part, has a better enthusiasm to formalize, illuminate and picture the pattern and give the last outcome, while machine learning customarily concentrates on expanding the recognition rates before giving the last yield. Pattern Recognition algorithms normally mean to give a reasonable response to every single input and to perform in all probability coordinating of the data sources, taking into charge their statistical variety. There are various uses of Pattern Recognition.

 

Track: Facial Expression and Emotion Detection

The use of machines in the public has expanded widely in the most recent decades. These days, machines are utilized as a part of a wide range of businesses. As their introduction with people increment, the communication additionally needs to wind up smoother and more characteristic. Keeping in mind the end goal to accomplish this, machines must be given an ability that let them get it the encompassing condition. Exceptionally, the intentions of a person. At the point when machines are eluded, this term includes computers and robots. Deep Learning conference will talk in depth about facial expression & emotion detection.

 

Track: Computer Vision and Image Processing

Computer Vision is a sub-branch of Artificial Intelligence whose goal is to give computers the powerful facility for understanding their surrounding by seeing the things more than hearing or feeling, just like humans. It is used for processing, analyzing and understanding digital images to extract information from that. In other words, it transforms the visual images into a description of the words. Machine Learning Conference gives a platform for the researchers to come & talk on a common platform.

 

Track: Robotic Process Automation (RPA)

Robotic Automation lets organizations automate current tasks as if a real person was doing them across applications and systems. RPA is a cost cutter and a quality accelerator. Therefore, RPA will directly impact OPEX and customer experience, and benefit to the whole organization and this is why it becomes the main topic to be discussed in Machine Learning Conference.

 

Track: Virtual Reality and Augmented Reality

Virtual Reality is the technology for the presentation of complicated information, manipulations, and interactions of the person with them by the computer. It is a computer generated an interactive three-dimensional environment to simulate reality. It can show 3D and attach sounds and touch information increases extraordinarily data comprehensibility. It has entered the public awareness as a medical toy with equipment “Helmet-glove”, which was preferentially determined for a wide public.

Augmented Reality is a combination of a real scene viewed by a user and a virtual scene generated by a computer that augments the scene with additional information. It enhances the real life by superimposing virtual images and adds graphics, sounds & smell to the real world, as it exists. The user maintains a sense of presence in the real world, He/she can interact with the real world and is not cut off from the real world. Augmented Reality is most suitable for marketing campaigns, product activations and launches, print advertising and much more. It is also been used on the smartphones.

 

Track: Internet of Things (IoT)

The Internet of things (IoT) refers to an umbrella that covers the entire network of physical devices, home appliances, vehicles, and other items embedded with software, sensors, actuators, electronics, and connectivity, or we can say with an IP address (Internet Protocol), which enables these objects to connect and exchange data, which results in enhanced efficiency, accuracy and economic advantage in addition to reduced human involvement.

 

Track: Big Data, Data Science and Data Mining

Nowadays, a huge quantity of data is being produced daily. Machine Learning uses those data and provides a noticeable output that can add value to the organization and will help to increase ROI,

Big Data is informational indexes that are so voluminous and complex that conventional data handling application programming is lacking to manage them. Big Data challenges incorporate capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, and updating and data security. There are three dimensions to Big Data known as Volume, Variety, and Velocity.

Data Science manages both structured and unstructured data. It is a field that incorporates everything that is related to the purging, readiness and last investigation of data. Data science consolidates the programming, coherent thinking, arithmetic, and statistics. It catches information in the keenest ways and supports the capacity of taking a gander at things with an alternate point of view.

Data mining is essentially the way toward collecting information from gigantic databases that was already immeasurable and obscure and after that utilizing that information to settle on applicable business choices. To put it all the more essential, Data mining is an arrangement of different techniques that are utilized as a part of the procedure of learning disclosure for recognizing the connections and examples that were beforehand obscure. We can thusly term data mining as a juncture of different fields like artificial intelligence, data room virtual base management, pattern recognition, visualization of data, machine learning, and statistical studies and so on.

 

Track: Big Data Analytics

Big Data Analytics gives a handful of usable data after examining hidden patterns, correlations and other insights from a large amount of data. That as a result, leads to smarter business moves, higher profits, more efficient operations and finally happy customers. And Big Data Conference adds more value to it.

Big Data Analytics adds value to the organization in the following ways:

•           Cost reduction
•           Faster, Better decision making
•           New Products and Services

 

Track: Predictive Analytics

Predictive Analytics is the branch of advanced analytics which offers a clear view of the present and deeper insight into the future. It uses different techniques and algorithms from statistics and data mining, to analyze current and historical data to predict the outcome of future events and interactions. Big Data Conference, Artificial Intelligence Conference as well as Machine Learning summit keep Predictive Analytics as its main part because of its vast scope.

 

Track: Cloud Computing

Cloud Computing is a delivery model of computing services over the internet. It enables real-time development, deployment and delivery of a broad range of products, services, and solutions. It is built around a series of hardware and software that can be remotely accessed through any web browser. Generally, documents and programming are shared and dealt with by numerous clients and all information is remotely brought together as opposed to being put away on clients' hard drives. Machine Learning Conferences has included a special talk on cloud computing.

Market Analysis

Machine Learning 2019 welcomes attendees, presenters, and exhibitors from all over the world to Helsinki, Finland. We are delighted to invite you all to attend and register for the 6th World Machine Learning and Deep Learning Congress which is going to be held on January 28-29, 2019, Abu Dhabi, UAE.

The organizing committee is gearing up for an exciting and informative conference program including plenary lectures, symposia, workshops on a variety of topics, poster presentations and various programs for participants from all over the world. We invite you to join us at the Machine Learning 2019, where you will be sure to have a meaningful experience with scholars from around the world. All members of the Machine Learning Conference organizing committee look forward to meeting you in Helsinki, Finland.

Importance & Scope:

Previously Artificial Intelligence, Machine Learning & Deep Learning was used to construct software from training examples. Its method was also extended to support data mining and knowledge discoveries. Then ML & DL started doing perceptual tasks like deep learning for computer vision, speech recognition etc. After that its main work was automated decision making & anomaly detection (Cyber Security, Fraud Detection and Machine Diagnosis). But the Future of ML & DL is beyond imagination and it can control & work on a variety of topics like:

  • Detecting and Correcting for Bias
  • Risk-Sensitive Optimization
  • Explanations of Black Box Systems
  • Verification and Validation
  • Integrating ML Components into Larger Software Systems

 

The market of AI, Machine Learning & Deep Learning is growing exponentially worldwide. According to the research, Healthcare Artificial Intelligence Market size was over $750 million in 2016 and forecast to witness exceed $10 billion in 2024, with nearly 40% CAGR from 2017 to 2024. US Healthcare artificial intelligence market was valued over $320 million in 2016 and is estimated to witness more than 38% CAGR over the coming years. The global machine learning market is expected to grow from $ 1.41 Billion in 2017 to $ 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% and the Global Machine Learning as Service market is expected to grow from $ 480.94 million in 2015 to reach $ 5,394.87 million by 2022 with a CAGR of 41.2%.    

 

Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, Return on Investment (ROI), and to gain a competitive edge in business operations. Moreover, in the coming years, applications of machine learning in various industry verticals are expected to rise exponentially. Some of the verticals are:

·         BFSI

o   Applications of machine learning in BFSI

  •  Fraud and Risk Management
  •  Investment Prediction
  •  Sales and Marketing Campaign Management
  •  Customer Segmentation
  •  Digital Assistance
  •  Others (compliance management and credit underwriting)

·         Healthcare and Life Sciences

o   Applications of machine learning in healthcare and life sciences

  •  Disease Identification and Diagnosis
  •  Image Analytics
  •  Drug Discovery/Manufacturing
  •  Personalized Treatment
  •  Others (clinical trial research and epidemic outbreak prediction)

·         Retail

o   Applications of machine learning in retail

  •  Inventory Planning
  •  Upsell and Cross-Channel Marketing
  •  Segmentation and Targeting
  •  Recommendation engines
  •  Others (customer ROI and lifetime value, and customization management)

·         Telecommunication

o   Applications of machine learning in telecommunication

  •  Customer Analytics
  •  Network Optimization
  •  Network Security
  •  Others (digital assistance/contact centres analytics and marketing campaign analytics)

·         Government and Defence

o   Applications of machine learning in government and defence

  •  Threat Intelligence
  •  Autonomous Defence system
  •  Others (sustainability and operational analytics)

·         Manufacturing

o   Applications of machine learning in manufacturing

  •  Predictive Maintenance
  •  Demand Forecasting
  •  Revenue Estimation
  •  Supply Chain Management
  •  Others (root cause analysis and telematics)

·         Energy and Utilities

o   Applications of machine learning in energy and utilities

  •  Power/Energy Usage Analytics
  •  Seismic Data Processing
  •  Smart Grid Management
  •  Carbon Emission
  •  Others (customer specific pricing and renewable energy management)

·         Others (Education, Agriculture, Media and Entertainment, and Education)

Why Finland?

In recent time Finland has emerged high in the technology sector because of its high educational standards and enormous facilities for R&D of digital technologies. Finnish tech companies all build cutting-edge innovations around 5G, cybersecurity, and other game-changing industries of the future, from top of the world leading players to promising start-ups It has shown interest in developing the AI sector which will offer cybersecurity cooperation, unmanned aircrafts, satellite cooperation, and maritime safety issues, such as dissemination of situation awareness information.

Finland, a country of contrasts, the Midnight Sun in the summer, the Polar Night and the Northern Lights in the winter, is known for its scenic beauty of natural aura. Helsinki is the capital city of Finland. It is located in southern Finland on the shore of the Gulf of Finland, is a city of the vibrant seaside, beautiful islands, and great green parks. Helsinki is the administrative center of the country for politics, education, finance, culture, and research.

Why to Attend???

ML conference gathers communities to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets. Machine Learning 2018 invites attendees from around the world focused on learning about ML & DL. This would be one of the best opportunity to reach the largest assemblage of participants from the ML community. Conduct demonstrations, distribute information, meet with current and potential customers, make a splash with a new product line, and receive name recognition at this 2-day event. World’s renowned speakers, the most recent techniques, highlights, discoveries and the newest updates in ML & DL fields are hallmarks of this conference.

  • Extraordinary speakers
    Discover advances in ML and DL algorithms and methods from the world's leading speakers, researchers, and scholars. Learn from industry experts in speech & pattern recognition, neural networks, image analysis, and NLP. Explore how Machine Learning and Deep Learning will impact finance, healthcare, manufacturing, search & transportation.
  • Discover emerging trends
    The congress will showcase the opportunities of advancing trends in Machine Learning & Deep Learning and their impact along with successful applications in business. It will also focus on the challenges and areas of improvement related to the field of research and applications. Learn the latest technological advancements & industry trends from a global line-up of experts.
     
  • Expand your network
    A unique opportunity to connect with industry leaders, influential technologists, Machine Learning Professionals & founders leading the deep learning revolution. Learn from & interact with various industry innovators sharing best practices to advance the smart artificial intelligence revolution and become a part of it.

Industries Associated with Machine Learning:

World Wide:

  • GOOGLE – For developing a photographic memory
  • IBM – For embedding Watson where it’s needed most
  • BAIDU – For accelerating mobile search with artificial intelligence
  • SOUNDHOUND – For giving digital services the power of human speech
  • ZEBRA MEDICAL VISION – For using deep learning to predict and prevent diseases
  • PRISMA - For making masterpieces out of snapshots
  • IRIS AI - For speeding up scientific research by surfacing relevant data
  • PINTEREST - For serving up a universe of relevant pins to each and every user
  • TRADEMARKVISION - For helping start-ups make their mark without any legal confusion
  • DESCARTES LABS - For preventing food shortages by predicting crop yields

Universities Associated with Machine Learning:

World Wide:

  • Carnegie Mellon University
  • University of Michigan Ann Arbor
  • Cornell
  • Berkeley
  • Stanford
  • Columbia University
  • University of Washington
  • Georgia Tech
  • University of California San Diego
  • University of Massachusetts Amherst
  • John Hopkins University
  • University of Illinois Urbana Champaign
  • Penn State University
  • University of North Carolina Chapel Hill
  • California Institute of Technology
  • University of Wisconsin Madison

 Major Societies & Groups Worldwide-

  • The International Machine Learning Society (IMLS)
  • American Statistical Association
  • IEEE Computer Society
  • European Knowledge Discovery Network of Excellence (KDNet)
  • National Centre for Data Mining (NCDM)
  • Pacific Rim International Conferences on Artificial Intelligence
  • Canadian Artificial Intelligence Association
  • The European Coordinating Committee on AI (ECCAI)
  • Special Interest Group on Artificial Intelligence, Computer Society of India
  • Japanese Society of Artificial Intelligence
  • Sociedad Mexicana de Inteligencia Artificial
  • Russian Association for Artificial Intelligence
  • Computing Research Association
  • The Society for the Study of Artificial Intelligence AND Simulation of Behaviour
  • The International Neural Network Society

What will you learn???

1.       Data-Driven Innovation Stage:

Clear examples of Data Innovation and its impact on the business or society Data-Driven Innovation Stage (DI) is one of the 5 stages you can attend during the third annual Data Innovation Summit 2018 in Stockholm. In this presentation, you can explore the Data-Driven Innovation stage in detail, and learn why you and your team should attend the event and this stage. Once you have registered for the summit you can attend any of the five stages on the summit. Here we go!

 

2.        Business Analytics Stage:

Practical case studies on implementation of Advanced Analytics and Data Science strategy, capabilities and technology in an organization. 

Business Analytics (BA) Stage is one of the 5 stages you can attend during the third annual Data Innovation Summit 2018 in Stockholm. In this presentation, you can explore the Business Analytics stage in detail, and learn why you and your team should attend the event and this stage. Once you have registered for the summit you can attend any of the five stages on the summit. Here we go!

 

3.       Data Management Stage:

Strategy, Technology, and implementation of practical case studies on Data Governance, Privacy by Design, Data quality, Future Enabled Enterprise Architecture, data integration. Data mining, Modelling, feature Extraction.

 

4.       IOT Insight and Innovation Stage:

Practical and technical examples of IOT implementation, insight and Business ROI. Focus on IOT Data Analytics, Data Management, and Innovation.

 

5.       Machine Learning and Artificial Intelligence

Practical Examples of AI implementation, insight and Business ROI. Focus on Artificial Narrow Intelligence (ANI), Robotic Process Automation (RPA), chatbots, neural networks, and deep learning. 

Related Societies: 

USA: ML SocietyThe International Machine Learning SocietyThe Association For The Advancement Of Artificial IntelligenceThe Artificial Intelligence SocietyAI & SocietyThe Association for Uncertainty in Artificial IntelligenceSociety for Artificial IntelligenceConference on Uncertainty in Artificial IntelligenceArtificial Intelligence InternationalSlovenian Pattern Recognition SocietyInternational Association of Computer Science and Information TechnologyGlobal Cleantech Cluster Association; Czechoslovak Pattern Recognition Society; Bulgarian Association for Pattern Recognition; The Swiss Association for Pattern Recognition; The British Machine Vision Association and Society for Pattern Recognition;

Europe: European Association for Artificial IntelligenceConexus Deep Learning SocietyDeep Learning Society – Atlantic Rim Collaboratory;  the Hellenic Artificial Intelligence Societythe European Coordinating Committee for Artificial IntelligenceArtificial Intelligence InternationalThe Association for Uncertainty in Artificial IntelligenceSpecial Interest Group of the Brazilian Computer SocietyFrench Association for Pattern Recognition and InterpretationNational Committee of the Russian Academy of Sciences for Pattern Recognition and Image AnalysisMexican Association for Computer Vision, Neurocomputing and Robotics, Nederlandse Vereniging voor Patroonherkenning en Beeldverwerking; Italian Association for Pattern Recognition

Asia Pacific & Middle East: Indian Unit for Pattern Recognition and Artificial IntelligenceArtificial Intelligence Society of Hong KongPattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation; Artificial Intelligence Association of IndiaThe Society for the Study of Artificial Intelligence and Simulation of BehaviourAustralian Pattern Recognition SocietyHong Kong Society for Multimedia and Image ComputingPattern Recognition and Machine Intelligence Committee of the Chinese Association of Automation; The Macau Society for Pattern Recognition and Image ProcessingPakistani Pattern Recognition Society (PRRS); Computer Vision and Pattern Recognition Group of The Korean Institute of Information Scientists and Engineers

 

Related Conferences:

Past Conference Report

Machine Learning 2018

In the presence of Business Professionals, Academicians, Practitioners, and Students involved in the development of high-quality education in all aspects of technical skills, Conference Series 5th World Machine Learning and Deep Learning Congress was held during August 30-31, 2018 in Dubai, UAE.

ME Conferences Group played host to a diverse panel of key members of the Machine Learning 2018 community from research lab, industry, academia, and financial investment practices, discussing the future of Artificial Intelligence, Machine Learning, Deep Learning, Big Data, and RPA. This event was really aimed for examining where the technology is going in the future and purpose of the event was to provide an opportunity for cross-fertilization and development of ideas, in this field.

Focusing on Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things (IoT), The role of AI & Machine Learning in Medical Science, Robotic Process Automation (RPA), Artificial Neural Networks (ANN) & Chainer, Natural Language Processing (NLP) and Speech Recognition, Computer Vision and Image Processing, Pattern Recognition, Deep Learning Frameworks, Dimensionality Reduction, Model Selection and Boosting, Big Data, Data Science and Data Mining, Object Detection with Digits, Facial Expression and Emotion Detection, Cloud Computing, Predictive Analytics, Big Data Analytics, the two days of discussions enabled professionals to gain an insight into the current innovations and opened up networking opportunities.

Machine Learning 2018 Organizing Committee would like to thank the Moderators of the conference – Rohit Agarwal, Mobisy Technologies Pvt Ltd, India; Tanya Dixit, Qualcomm, India who contributed a lot for the smooth functioning of this event.

The conference was embarked with an opening ceremony followed by Keynote sessions and followed by a series of lectures delivered by Honourable Guests and members of the Keynote forum.

The highlights of the meeting were the eponymous lectures, delivered by:

  •        Shabir Momin, ZengaTV, Singapore
  •        Anu Kukar, KPMG, Australia
  •        Tanya Dixit, Qualcomm, India
  •        Tilila El Moujahid, Microsoft, UAE
  •        Jayatu Sen Chaudhury, American Express, India
  •        Sriharsha Allenki, Qualcomm, India
  •        Niladri Shekhar Dutta, Ericsson, UAE
  •        Manoj Mishra, Union Insurance, UAE
  •        Eman AbuKhousa, UAE University, UAE
  •        Najati Ali-Hasan, Anchor IT Consultation, UAE
  •        Abbas M Al-Bakry, University of Information Technology and Communications, Iraq
  •        Rohit Agarwal, Mobisy Technologies Pvt Ltd, India
  •        Erwin E. Sniedzins, Mount Knowledge Inc., Canada
  •        Samir El-Masri, Digitalization.Cloud, UAE
  •        Sylvester Juwe, British Gas, United Kingdom
  •        Harshavardhana Kikkeri, Kaaya Tech Inc, USA
  •        Santosh Godbole, SSN Solutions Limited, India
  •        Ahmed AlMaqabi, Almaqabi, Kingdom of Bahrain
  •        Gaurav Pawar, Mobisy Technologies Pvt Ltd, India
  •        Kai Khalid Miethig, Tariq Faqeeh Engineering, Bahrain
  •        Abed Benaichouche, Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

These talks were of great interest to the general technology and were enormously informative.

There were several poster presentations as well at the conference. The best poster award won by Mr. Rohit Agarwal & Mr. Gaurav Pawar for the title “An overview of deep learning based object detection techniques in the retail domain” and Dr. Nabil Belgasmi for the title “Multiobjective deep reinforcement learning approach for ATM cash replenishment planning”.

5th World Machine Learning and Deep Learning Congress was a great success with the support of international, multi-professional steering committee and coordinated by the Journal of Computer Science & Systems Biology; Advances in Robotics & Automation; Journal of Proteomics & Bioinformatics. We are happy to announce our 6th World Machine Learning and Deep Learning Congress, which will be held during October 14-15, 2018 in Helsinki, Finland


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To Collaborate Scientific Professionals around the World

Conference Date October 14-15, 2019

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Past Conference Report

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Keytopics

  • Algorithm
  • Algorithmic Probability
  • Approximation Error
  • Argumentation Framework
  • Argumentation Models
  • Artificial Intelligence
  • Artificial Neural Networks
  • Augmented Reality
  • Automation
  • Autonomous Car
  • Autonomous Robot
  • Behavior Informatics
  • Behavior Tree
  • Big Data
  • Bioinformatics
  • Brain Machine Interfaces
  • Chatbots
  • Cloud Computing
  • Cloud Robotics
  • Clustering
  • Cognitive Architecture
  • Cognitive Computing
  • Cognitive Science
  • Computational Intelligence
  • Computational Linguistics
  • Computational Mathematics
  • Computational Neuroscience
  • Computational Number Theory
  • Computational Problem
  • Computational Statistics
  • Computational Vision
  • Computer Science
  • Computer Vision
  • Computer-automated Design
  • Control Theory
  • Convolutional Neural Network
  • Cooperating Robots
  • Data Mining
  • Data Science
  • Data Set
  • Data Warehouse
  • Decision Model
  • Decision Theory
  • Deep Learning
  • Dimensionality Reduction
  • Game Theory
  • Google DeepMind
  • Graph Theory
  • Image Recognition
  • Inductive Logic Programming (ILP)
  • Inductive Reasoning
  • Integrated Systems
  • Intelligence Amplification
  • Intelligent Agent
  • Intelligent Robots
  • Kernel Method
  • KL-ONE
  • Logic Programming
  • Logical AI
  • Machine Learning
  • Machine Perception
  • Machine Vision
  • Marine Robotics
  • Mathematical Optimization
  • Mechatronics
  • Metabolic Network Modelling
  • Micro/Nano Robots
  • Multi-swarm Optimization
  • Natural Language Processing (NLP)
  • Networked Robots
  • Neural Networks
  • Neurocybernetics
  • Neurorobotics
  • Node
  • Nondeterministic Algorithm
  • Ontology Learning
  • OpenAI
  • Optical Character Recognition (OCR)
  • Optimization
  • Particle Swarm Optimization
  • Pathfinding
  • Pattern Recognition
  • Predictive Analytics
  • Probabilistic Programming Language
  • Process Control
  • Production System
  • Programming Language
  • Python
  • R Programming Language
  • Recognition
  • Recurrent Neural Network
  • Regression
  • Reinforcement Learning
  • Robotics
  • Semantics
  • Sensor Networks
  • Sequential Analysis
  • Simulated Annealing
  • Simulation And Animation
  • Situation Calculus
  • Software
  • Space Robotics And Automation
  • SPARQL
  • Speech Recognition
  • Speech To Text
  • Statical Data
  • Statistical Classification
  • Subject-matter Expert
  • Superintelligence
  • Supervised Learning
  • Support Vector Machines
  • Swarm Intelligence
  • Swarm Robots
  • Synthetic Intelligence
  • Systems Neuroscience
  • Technology
  • Tensor Network Theory
  • TensorFlow
  • Tentative Hypothesis
  • Theoretical Computer Science
  • Theory Of Computation
  • Unsupervised Learning
  • Validation Data Set
  • Vision
  • Visual Learning
  • Watson
  • Weak AI
  • Web Services