2024 2nd International Conference on Machine Learning and Intelligent ScienceMarch 8-10, 2024 | Macau S.A.R, China |
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.
Submission Deadline | October 10, 2023 |
Acceptance Notification | November 05, 2023 |
Registration Deadline | November 20, 2023 |
Conference Dates | March 8-10, 2024 |
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.
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