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