Hybrid Conferencee

International Conference on Statistical Techniques for Machine Learning and AI (ICSTMMLA - 26)

28th - 29th August 2026 | Chicago, USA
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Conference Brochure
Sample Full Paper
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Conference Notifications:

"Be sure to check this section regularly for all Research Plus International Conference updates. We’ll keep you informed about deadlines, event details, and more important notifications."

Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Chicago. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Chicago event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Chicago conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Chicago, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Chicago."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICSTMMLA aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Statistics,Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

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

01
Machine learning algorithms for statistical analysis
02
Statistical techniques in AI model evaluation
03
Feature selection methods in machine learning
04
Statistical learning theory applications
05
Data preprocessing for machine learning models
06
Ensemble methods in statistical learning
07
Deep learning and statistical inference
08
Bayesian statistics in AI applications
09
Statistical methods for big data analytics
10
Interpretability of machine learning models
11
Statistical challenges in AI deployment
12
Reinforcement learning and statistical methods
13
Statistical evaluation of AI systems
14
Transfer learning in statistical contexts
15
Statistical methods for time series analysis
16
Unsupervised learning and statistical techniques
17
Statistical issues in data privacy
18
Statistical frameworks for AI ethics
19
Applications of statistics in natural language processing
20
Statistical modeling of complex systems