2024 2nd International Conference on Machine Learning and Intelligent Science

March 8-10, 2024 | Macau S.A.R, China

Welcome to MLIS

2024 International Conference on Machine Learning and Intelligent Science (MLIS 2024) will be held in Macau S.A.R, China, as workshop of ICKD during March 8-10, 2024. It aims to provide a forum for researchers, practitioners, and professionals from both the industry and the academia to share their newest research findings and results.

The conference calls for high-quality, original research papers in the theory and practice of machine learning and intelligent science. The conference also solicits proposals focusing on frontier research, new ideas and paradigms in machine learning and intelligent technologies.

It's acceptable for those participants who can't travel to attend conference onsite to present their papers online with reduced registration fee.

Important Dates

Submission Deadline October 10, 2023
Acceptance Notification November 05, 2023
Registration Deadline November 20, 2023
Conference Dates March 8-10, 2024

Submission Method

Please submit your full paper via iConference submission system using the following link: Zmeeting Electronic Submission System; ( .pdf).  Please submit only abstract if you plan on just presenting your paper without publication


Papers submitted to MLIS will be reviewed by both the conference committees and IJML editorial board, and accepted papers will be published in the International Journal of Machine Learning , which will be indexed by Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.

Paper Template

If you're NOT looking to have your papers published in the above journal, it's acceptable to submit your abstracts to the conference just for oral presentation without publication.

Scope of Conference

Authors are invited to submit full papers describing original research work in areas including, but not limited to:

Unsupervised Learning
Meaningful Compression
Computational Theories of Learning
Big Data Visualization
Structure Discovery
Feature Elicitation
Recommender Systems
Targetted Marketing
Multitasking and Transfer Learning
Customer Segmentation
Deep learning