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