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Tracks

Topics and Scope of the Conference
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