Conference Information
ICMLDS 2018: International Conference on Machine Learning and Data Science
http://icmlds.org/
Submission Date:
2018-09-08 Extended
Notification Date:
2018-10-05
Conference Date:
2018-12-21
Location:
Hyderabad, India
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Conference Location
Call For Papers
The International Conference on Machine Learning and Data Science will focus on topics that are of interest to computer and computational scientists and engineers. MLDS-2018 will bring together researchers and practitioners from academia, industry and government to deliberate on the algorithms, systems, applied, and research aspects of Machine Learning and Data Science. The conference will be held in Hyderabad - Telangana, India, and will feature multiple eminent keynote speakers, and presentation of peer reviewed original research papers and exhibits.

Machine Learning

    Model Selection
        Learning using Ensemble and boosting strategies
        Active Machine Learning
        Manifold Learning
        Fuzzy Learning
        Kernel Based Learning
        Genetic Learning
        Hybrid models
    Evolutionary Parameter Estimation
        Fuzzy approaches to parameter estimation
        Genetic optimization
        Bayesian estimation approaches
        Boosting approaches to Transfer learning
        Heterogeneous information networks
        Recurrent Neural Networks
        Influence Maximization
        Co-evolution of time sequences
    Graphs and Social Networks
        Social group evolution – dynamic modelling
        Adaptive and dynamic shrinking
        Pattern summarization
        Graph embeddings
        Graph mining methods
        Structure preserving embedding
    Non-parametric models for sparse networks
        Forecasting
        Nested Multi-instance learning
    Large scale machine learning
        Large scale item categorization
        Machine learning over the Cloud
        Anomaly detection in streaming heterogeneous datasets
        Signal analysis 
    Learning Paradigms
        Clustering, Classification and regression methods
        Supervised, semi-supervised and unsupervised learning
        Algebra, calculus, matrix and tensor methods in context of machine learning
        Reinforcement Learning
        Optimization methods
        Parallel and distributed learning
    Deep Learning 
        Inference dependencies on multi-layered networks
        Recurrent Neural Networks and its applications
        Tensor Learning
        Higher-order tensors
        Graph wavelets
        Spectral graph theory
        Self-organizing networks 
        Multi-scale learning
        Unsupervised feature learning 
    Recommender Systems
        Automated response
        Conversational Recommender systems
        Collaborative deep learning
        Trust aware collaborative learning
        Cold-start recommendation systems
        Multi-contextual behaviours of users
    Applications
        Bioinformatics and biomedical informatics
        Healthcare and clinical decision support
        Collaborative filtering
        Computer vision
        Human activity recognition
        Information retrieval
        Cybersecurity
        Natural language processing
        Web search
    Evaluation of Learning Systems
        Computational learning theory
        Experimental evaluation
        Knowledge refinement and feedback control
        Scalability analysis
        Statistical learning theory
        Computational metrics

Data Science

    Algorithms
    Novel Theoretical Modelsp
    Novel Computational Models
    Data and Information Quality
    Data Integration and Fusion
    Cloud/Grid/Stream Computing
    High Performance/Parallel Computing
    Energy-efficient Computing
    Software Systems
    Search and Mining
    Data Acquisition, Integration, Cleaning
    Data Visualizations
    Semantic-based Data Mining
    Data Wrangling, Data Cleaning, Data Curation, Data Munching
    Data Analysis, , Statistical Insights
    Decision making from insights, Hidden patterns
    Data Science technologies, tools, frameworks, platforms and APIs
    Link and Graph Mining
    Efficiency, scalability, security, privacy and complexity issues in Data Science
    Labelling, Collecting, Surveying, Interviewing and other tools for Data Collection
    Applications in Mobility, Multimedia, Science, Technology, Engineering, Medicine, Healthcare, Finance, Business, Law, Transportation, Retailing, Telecommunication
Last updated by Dou Sun in 2018-09-01
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