On-location / Digital Conference

International Conference on Statistical Techniques for Machine Learning and AI (ICSTMMLA-27)

27th - 28th Mar 2027,Las Vegas, USA

In Association With:

Call for Paper


Important Dates


Early Bird Registration

25th Feb 2027

Paper Submission Deadline

2nd March 2027

Registration Deadline

12th March 2027

Conference Date

27th - 28th Mar 2027

Conference Updates:

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  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in Las Vegas ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in Las Vegas conference.
  • Peer Review Process:
    The peer review process will begin soon for Las Vegas conference.
  • Networking with Global Experts:
    Join global experts at our conference in Las Vegas.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICSTMMLA bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Statistical Techniques for Machine Learning and AI, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning algorithms for statistical analysis
  • Statistical techniques in AI model evaluation
  • Feature selection methods in machine learning
  • Statistical learning theory applications
  • Data preprocessing for machine learning models
  • Ensemble methods in statistical learning
  • Deep learning and statistical inference
  • Bayesian statistics in AI applications
  • Statistical methods for big data analytics
  • Interpretability of machine learning models
  • Statistical challenges in AI deployment
  • Reinforcement learning and statistical methods
  • Statistical evaluation of AI systems
  • Transfer learning in statistical contexts
  • Statistical methods for time series analysis
  • Unsupervised learning and statistical techniques
  • Statistical issues in data privacy
  • Statistical frameworks for AI ethics
  • Applications of statistics in natural language processing
  • Statistical modeling of complex systems

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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