International Conference on

Advances in Machine Learning and Data Science

Theme: Modern Technologies and Challenges in Machine Learning and Data Science

Event Date & Time

Event Location

Baltimore, USA

18 years of lifescience communication

Performers / Professionals From Around The Globe

Conference Speaker

Pilar Rey del Castillo

Instituto de Estudios Fiscales
Spain

Conference Speaker

Abdulmohsen Algarni

King Khalid University
Saudi Arabia

Conference Speaker

Luís Sousa

University of Porto
Portugal

Conference Speaker

Ogunjobi Olivia Abiola

Dangote Group
Nigeria

Conference Speaker

Ahmed N. AL-Masri

American University in the Emirates
UAE

Conference Speaker

Wan Sik Nam

Korea University
South Korea

Conference Speaker

Dominik Slezak

University of Warsaw
Poland

Conference Speaker

Pengchu Zhang

Sandia National Laboratories
USA

Conference Speaker

E. Cabral Balreira

Trinity University
USA

Conference Speaker

Mohamed M. Mostafa

Gulf University for Science and Technology
Kuwait

Conference Speaker

David Sung

KyungHee University School of Management Graduate School
South Korea

Conference Speaker

SookYoung Son

Hyundai Heavy Industries
South Korea

Tracks & Key Topics

Data Science 2019

Data Science 2019 Conference Information

EUROSCICON Ltd invites all the participants across the globe to attend International Conference on Advances in Machine Learning and Data Science during December 09-10, 2019 at Baltimore, USA, with the theme of “Modern Technologies and Challenges in Machine Learning and Data Science “.

EuroScicon Ltd is the longest running independent life science events company with a predominantly academic client base. Our multi-professional and multispecialty approach creates a unique experience that cannot be found with a specialist society or commercially. EuroScicon Organizing in the way we operate to continuously improve our ability to unleash valuable recent research work in this field. In addition to immense networking opportunities, global prominence of the research and the researcher is what we bestow our Speakers and Committee Members for better outreach of Research work.

Data Science 2019 Conference is an Machine Learning and Data Science expo which is comprised of 28 scientific sessions which have designed to discussion on the current topics and issues of Machine Learning, Data Mining, Big Data, Artificial Intelligence, Deep Learning and Data Science field. Data Science 2019 is one of the few global data conferences, which covers all the fields of engineering

Data Science 2019 conference will make the perfect platform for global networking as it brings together renowned speakers, researchers, business persons across the globe to a most exciting and memorable scientific event filled with much enlightening interactive sessions, world class exhibition & poster presentations.

Data Science 2019 welcomes you all to stay engaged, proactive and help us to shape the future of Data Science Industry.

Target Audience:

Engineers who are specialized on the specific fields like Machine Learning, Data Science and Artificial Intelligence
Data Analytics Professionals
Software Publishing Houses specializing in Machine learning and Data Science software’s
Machine Learning, Data Science, Artificial Intelligence, Cloud Computing, Business Intelligence Associations and Societies
Machine Learning and Data Engineering Institutes
Business Entrepreneurs
Scientists/Researchers
Professors/Students
President/Vice president
Chairs/Directors
Data Scientists
Experts and Delegates

Sessions/Tracks

Track 01. Machine Learning Technology

Machine learning (ML) is the analysis of algorithms and statistical models that computer systems use to successfully perform a specific task without use of explicit instructions, relying on patterns and inference instead. Machine learning is an application of artificial intelligence (AI) that basis on the development of computer programs that can access data and use it learn for themselves.

Track 02. Machine Learning Models and Techniques

Numerous processes, techniques and methods can be applied to one or more types of ML algorithms to improve their performance. The training process of an ML model involves providing a learning algorithm with training data to learn from. We can use the Machine Learning models to get predictions on new data for which we do not know the target. 

Track 03. Pattern Recognition and Machine Learning

Pattern Recognition is a set of problem while Machine Learning is a set of solution. Pattern recognition is end related to Artificial Intelligence and Machine Learning. Pattern Recognition is one of the engineering applications of Machine Learning. Machine Learning deals with the building and analysis of the systems that can learn from data, rather than follow only clear programmed instructions whereas Pattern recognition is the recognition of patterns and regularities in data.

Track 04. Deep Learning

Deep structured learning or hierarchical learning is a class of ML methods based on learning data representations, as opposed to task-specific algorithms. Deep Learning can be supervised, semi-supervised or unsupervised. Modern deep learning models are based on an artificial neural network, specifically, Convolutional Neural Networks (CNN), although they can also include propositional formulas or latent variables organized layer-wise in deep generative models.

Track 05. Machine Learning in Big Data and Data Mining

Big data is the type of data that may be supplied into the analytical system so that a Machine Learning model could learn and improve the accuracy of its predictions. ML is based on algorithms that can learn from data without depending on rules-based programming. Data Mining builds more about what is really happening in some data and is still little much towards math than programming, but uses both. Machine Learning uses Data Mining techniques and other learning algorithms to construct models of what is happening at the back of some data so that it can forecast future outcomes.

Track 06. Machine and Deep Learning Applications

The increasingly growing number of applications of the machine and deep learning in various industries allows us to glimpse at a future where data, analysis, and innovation work hand-in-hand to offer its assistance without them ever realizing it. I will be quite common to find ML-based applications embedded with real-time data available from different source systems in multiple countries, thereby increasing the efficacy of new approach options which were unavailable before.

Track 07. Data Science

Data science is a concept of whole statistics, data analysis, machine learning related methods and algorithms in order to understand, analyse and to extract knowledge and insights with data. Data science is the same as big data and data mining which uses most effective hardware, programming systems and efficient algorithms to solve problems.

Track 08. Information and Database System

Databases are organized collections of data typically collected by schemas, tables, queries, reports and views, generally stored and accessed electronically from a computer system. Databases are typically organized to process data to provide quick information retrieval using formal design and modelling techniques.

Track 09. Big Data and Data Mining Applications in Science, Engineering, Healthcare, Medicine and Nursing Research

Data mining is the process of discovering patterns to extract information with an intelligent method from a data set and transform the information into a comprehensible structure for further use. Data mining is the detailed examination step of the "knowledge discovery in databases" process. These applications relate Data mining structures in genuine cash related business territory examination, Application of data mining in positioning, Data mining and Web Application, Engineering data mining, Data Mining in security, Social Data Mining, Neural Networks and Data Mining, Medical Data Mining, Data Mining in Healthcare. According to one rough calculation, one-third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications.

Track 10. Big Data and Data Mining Challenges and Opportunities

Most of the data is directly generated in digital format; we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. There are different phases in the Big Data analysis process and some common challenges that underlie many, and sometimes all, of these phases.

Track 11. Big data and Data Mining Tools and Software

Information Mining gadgets and programming ventures join Big Data Security and Privacy, Data Mining and Predictive Analytics in Machine Learning, Software Systems and Boundary to Database Systems.

Track 12. Big Data Technologies

Big Data is the name given to huge amounts of data. As the data comes in from a variety of sources, it could be too diverse and too massive for conventional technologies to handle. This makes it very important to have the skills and infrastructure to handle it intelligently. There are many of the big data solutions that are particularly popular right now fit for the use

Track 13. Forecasting from Big Data

Big Data is a revolutionary phenomenon has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. Big data play a critical role in reshaping the key aspects of forecasting by identifying and reviewing the problems, potential, better predictions, challenges and most importantly the related applications.

Track 14. Big Data Search and Mining

In the course of recent times, there has been an immense increase in the measure of information being put away in databases and the number of database applications in business and the investigative space. This blast in the measure of electronically put away information was accelerated by the achievement of the social model for putting away information and the improvement and developing of information recovery and control innovations.

Track 15. Data Mining Foundations

Data Mining is the process of posing queries to large amounts of data sources and extracting patterns and trends using statistical and machine learning techniques. It combines various technologies including database management, statistics and machine learning. Data mining has applications in numerous disciplines including medical, financial, defence and intelligence.

Track 16. Data Mining Methods and Algorithms

Data mining structures and calculations an interdisciplinary subfield of programming building is the computational arrangement of finding case in information sets including techniques like Big Data Search and Mining, Data Mining Analytics, High execution information mining figuring's, Methodologies on sweeping scale information mining, Methodologies on expansive scale information mining, Big Data and Analytics, Novel Theoretical Models for Big Data.

Track 17. Data Privacy and Ethics

In our e-world, information protection and cyber security have gotten to be respective terms. In this business, we have a commitment to secure our customer's information, which has been acquired as per their permission exclusively for their utilization. That is an all-important point if not promptly obvious. There's been a ton of speak of late about Google's new protection approaches, and the discussion rapidly spreads to other Internet beasts like Facebook and how they likewise handle and treat our own data.

Track 18. Data Warehousing

In computing, a Data Warehouse (DW or DWH), also known as an Enterprise Data Warehouse (EDW), is a system used for reporting and data analysis and is considered a central component of business intelligence. Data Warehouse or Enterprise Data Warehouse is central repositories of integrated data from one or more disparate sources.

Track 19. Artificial Intelligence and Technology

Automated thinking is the data performed by machines or software demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. AI examination is amazingly particular and focus, and is essentially isolated into subfields that a great part of the time hatred to chat with each other. It solidifies Artificial Creative Ability, Artificial Neural structures, Adaptive Systems, Cybernetics, Ontologies and Knowledge sharing.

Track 20. Business intelligence and Analytics

Business intelligence comprises the program and technologies used by enterprises for the data analysis of business information. Business intelligence technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies incorporate reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

Track 21. Knowledge Processing

Knowledge Management Technologies are information technologies that can be used to facilitate knowledge management. Knowledge Management Technologies are intrinsically no different from information technologies, but they can focus on knowledge management rather than information processing. The Knowledge Processes represent a different ways of making knowledge. They are forms of action, or things you do in order to know. There are some activity types which illustrate each of the Knowledge Processes. Many of the activities can be used across more than one Knowledge Process.

Track 22. Cloud Computing

Cloud computing is the delivery of computing services—servers, storage, databases, networking, software, analytics, and more—over the Internet (“the cloud”).  Cloud computing relies on sharing of resources to achieve coordination and economies of scale, similar to a public utility. Companies offering these computing services are called cloud providers and typically charge for cloud computing services based on usage.

Track 23. Internet of Things (IOT)

The internet of things(IoT) is the network of physical devices interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers(UIDs) and the ability to connect, collect and exchange data or transfer data over a network without requiring human-to-human or human-to-computer interaction.

Track 24. Visualization Techniques

Information representation is seen by numerous orders as a present likeness visual correspondence. It is not held by any one field, yet rather discovers translation crosswise over numerous. It covers the arrangement and investigation of the visual representation of information, indicating "data that has been dreamy in some schematic structure, including attributes or variables for the units of data".

Track 25. Clustering

Cluster analysis or clustering is the task of organizing a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

Track 26. Complexity and Algorithms

The uncertainty of a calculation indicates the aggregate time required by the system to rush to finish. The many-sided quality of calculations is most generally communicated using the enormous O documentation. Many-sided quality is most usually assessed by tallying the number of basic capacities performed by the calculation. What's more, since the calculation's execution may change with various sorts of info information, subsequently for a calculation we normally use the most pessimistic scenario multifaceted nature of a calculation since that is the extended time taken for any information size.

Track 27. Block Chain System

A block chain, originally block chain, is a increasing list of records, called blocks, which are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. By design, a block chain is resistant to modification of the data.

Track 28. Fuzzy Logic System

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.

Market Analysis

As the world is becoming more digital and connected, big data and business analytics are creating new possibilities for data collection, storage and intelligence process and analysis. With the huge amount of data generation, storage and capture, big data and business analytics have emerged as an important technology to study and solve data related difficulties for companies that have a huge amount of data stored and used within the organization.

The increasing interest and investment in artificial intelligence, in turn, is leading to the origination of new tools for collecting and analysing data and new enterprise roles and responsibilities which includes big data and business analytics solutions.

The Global Big Data market is estimated at $23.56 billion in 2015 and is expecting to reach $118.52 billion by 2022 growing at a CAGR of 26.0% from 2015 to 2022. Hasty growth in consumer data, superior information security, enhanced business efficiencies are some of the key factors fuelling market growth. The data mining tools market is expected to grow from USD 591.2 Million in 2018 to USD 1,039.1 Million by 2023, at a Compound Annual Growth Rate of 11.9% during the forecast period, owing to the remarkable increase in data volume and increased awareness among enterprises to support the benefits of available data assets.

Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, reaching a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B.

Learn More

USA Data Engineering Universities:

Stanford University | Massachusetts Institute of Technology | University of California | California Institute of Technology | University of Michigan | Georgia Institute of Technology | University of Illinois | Carnegie Mellon University | Cornell University | Purdue University | North-western University | Princeton University | University of Texas | University of California | Johns Hopkins University | Pennsylvania State University | Texas A&M University | University of California | University of Minnesota | University of Wisconsin | Harvard University | University of Southern California | Virginia Tech, Blacksburg | Columbia University | Ohio State University | Rensselaer Polytechnic Institute | University of Pennsylvania | Duke University | Rice University | University of California | University of Florida | University of Washington | University of Colorado | North Carolina State University | University of California | Yale University | Case Western Reserve University | Iowa State University |  Michigan State University | University of Delaware | University of Virginia, Charlottesville | Vanderbilt University | Lehigh University | Rutgers, The State University of New Jersey | University of Notre Dame | Boston University | North-eastern University | Washington University | Arizona State University | Clemson University | Colorado School of Mines |  Dartmouth College | Drexel University | Michigan Technological University | University of Arizona | University of Connecticut | University of Illinois | University of Pittsburgh | Oregon State University | University at Buffalo | University of Utah | Washington State University | Missouri University of Science & Technology | Stony Brook University | Tufts University | University of Iowa | University of Massachusetts | University of Tennessee | Brigham Young University | Colorado State University | Syracuse University | University of California | University of Cincinnati | University of Rochester | Worcester Polytechnic Institute | Auburn University | Clarkson University | George Washington University | Illinois Institute of Technology | New York University | Southern Methodist University | Stevens Institute of Technology | University of Houston | University of Kentucky ,Lexington | University of Nebraska | Florida A&M University | Kansas State University | Louisiana State University | New Jersey Institute of Technology | Oklahoma State University | University of Kansas | University of Missouri | University of North Carolina

Europe Data Engineering Universities:

University of Oxford | University of Cambridge | Imperial College London | ETH Zurich | London School of Economics and Political Science | University of Edinburgh | LMU Munich | King’s College London | Technical University of Munich | Heidelberg University | KU Leuven | University of Manchester | Delft University of Technology | University of Amsterdam | Humboldt University of Berlin | Leiden University | Erasmus University Rotterdam | Sorbonne University | Utrecht University | University of Freiburg | University of Bristol | University of Groningen | University of Warwick  | RWTH Aachen University | Uppsala University | University of Zurich | University of Glasgow | Lund University | University of Helsinki | University of Basel | Free University of Berlin | University of Sheffield | University of Bern | University of Bonn | Durham University | University of Birmingham | University of Copenhagen | University of Southampton | University of York | Trinity College Dublin | University of Oslo | Aarhus University | University of Mannheim | Maastricht University | Queen Mary University of London | Technical University of Berlin | University of Geneva | University of Hamburg | Karlsruhe Institute of Technology | University of Exeter | Ghent University | University of Vienna | Autonomous University of Barcelona | University of Cologne | Lancaster University | University of Nottingham | Ulm University | TU Dresden | University of Leeds | Stockholm University | University of Aberdeen | University of Sussex | Technical University of Denmark | University of St Andrews | Eindhoven University of Technology | University of Leicester | Newcastle University | University of Erlangen-Nuremberg | University of Lausanne | University of Bologna | Aalto University | University of Liverpool | Cardiff University | KTH Royal Institute of Technology | University of Konstanz | University of Duisburg-Essen | University of East Anglia | Aalborg University | University of Bergen | University of Antwerp | University of Barcelona | University of Bath

Asia Data Engineering Universities:

National University of Singapore | Tsinghua University | Peking University | University of Hong Kong | Hong Kong University of Science and Technology | Nan yang Technological University | Chinese University of Hong Kong | University of Tokyo | Seoul National University | Korea Advanced Institute of Science and Technology (KAIST) | Kyoto University | Pohang University of Science and Technology | City University of Hong Kong | University of Science and Technology of China | Nanjing University | Zhejiang University | Hong Kong Polytechnic University | Shanghai Jiao Tong University | Ulsan National Institute of Science and Technology | Korea University | Tel Aviv University | National Taiwan University | Hebrew University of Jerusalem | Osaka University | Indian Institute of Science | Tohoku University | Tokyo Institute of Technology | Nagoya University | National Taiwan University of Science and Technology | Indian Institute of Technology Bombay | Wuhan University | University of Malaya | University of Macau | Kyushu University | Qatar University | Hokkaido University | Hong Kong Baptist University | National Cheng Kung University | King Saud University | Indian Institute of Technology | Tokyo Medical and Dental University | Harbin Institute of Technology | University of Tsukuba | Tianjin University | Jordan University of Science and Technology | Southeast University | United Arab Emirates University | China Medical University, Taiwan | Soochow University | National Taiwan Normal University | American University of Beirut | Central China Normal University | Shandong University | University of Ulsan | Xiamen University | Indian Institute of Technology Kanpur | South China University of Technology | Fujita Health University | Sharif University of Technology | Taipei Medical University | Indian Institute of Technology Delhi | East China Normal University | East China University of Science and Technology | Istanbul Technical University | King Fahd University of Petroleum and Minerals | Isfahan University of Technology | Pusan National University | University of Haifa

USA Machine Learning and Data Science Companies

Insitro|Golden | Prisma Labs | Sight Machine | Afresh Technologies | Intellimize | Zippia, Inc. | uBiome | Fyusion | Rasa | Amazon | Apple | Ayasdi | Digital Reasoning | Darktrace | Dataiku | Facebook | Feedzai | Google | IBM Watson | Luminoso | N-iX | QBurst | Qualcomm | Skytree | Uber | SUMATOSOFT SCIENCESOFT | PSL CORP. | CORE VALUE INC. | THIRDEYE DATA | ALTOROS | OXAGILE | INTELLIAS | 3Q DIGITALC | BEYONDATA | CASERTA | FAYRIX | SOFTWEB SOLUTIONS | PRAGMATIC WORKS | BEYOND THE ARC

Europe Machine Learning and Data Science Companies

Webtrekk | IP Group Plc | Adverity | MUSO | Stockrepublic | Attest | Parkiro | Kalkulo | Keen Eye Technologies | Waterdata | BYAnalytics | ParStream | Q5 AG | Sumerian | Shopitize | atbrox | Datamarket | DataSift | 3scale | Kasabi | Zedge | PeerIndex | Aequicens | roaring.io | CourtQuant | feelyt | INSIDER | IFDAQ | Cascade.bi | Old St Labs | SiriusInsight.AI | Datatecnics | Aiden.ai | Auquan | Causalens | CBAS | Dogtooth | Grakn | GTN | Hazy | Immense Simulations | Kortical | Kheiron Medical | Latent Logic | Lifebit | Mapillary | nPlan

Asia Machine Learning and Data Science Companies.

Agerris | Elenium Automation | NeeWee | Doubtnut | Mihup Communications | Aiwhale | Medo.ai | AIZEN Global | Mainline | SigTuple | SenseTime | Mobvi | LeapMind | iCarbonX | Appier | UBTECH Robotics | Tusimple | ViSenze | CloudMinds | Preferred Networks | Cortica | Deep Instinct | Logz.io | Zebra | Active Intelligence | oBike | Anodot | Intuition Robotics | SigTuple | DAYLI

Journals of Machine Learning and Data Science

Artificial Intelligence | Big Data | Big Data Research | Computational Statistics & Data Analysis by International Association for Statistical Computing (IASC) | Foundations and Trends in Machine Learning | International Journal of Business Intelligence and Data Mining | International Journal of Data Science and Analytics | Journal of Big Data | Machine Learning | Sigkdd Explorations | Data Elixir | O’Reilly Data Newsletter | Data Science Weekly

Media Partners/Collaborator

A huge thanks to all our amazing partners. We couldn’t have a conference without you!

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Sponsors/Exhibitors

A huge thanks to all our amazing partners. We couldn’t have a conference without you!