2017 International Conference on Machine learning and Data Science (MLDS)

Machine Learning

• Model Selection

• Evolutionary Parameter Estimation

• Graphs and Social Networks

• Non-parametric models for sparse networks

• Large scale machine learning

• Learning Paradigms

• Deep Learning 

• Recommender Systems

• Applications

• Evaluation of Learning Systems

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