We are pleased to welcome you to 2025 17th International Conference on Machine Learning and Computing, which will be held in Guangzhou, China during February 14-17, 2025. The International Conference on Machine Learning and Computing (ICMLC) is grand annual conference on machine learning algorithms, computational statistics, mathematical optimization, computer engineering, computer science and other related subject since 2009. It will provide opportunities for researchers in this field to share their ideas and reinforce collaboration. As such, the conference has achieved a remarkable number participants while also enable profuse exchanges among academic and industrial researchers over the years.
The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. ICMLC conference committees are pleased to invite authors with specialized knowledge and novel innovative thinking to meeting in Shenzhen, China.
ICMLC 2025 is co-sponsored by National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, and technical supported by Metropolitan State University of Denver, Southwest Jiaotong University, University of Strathclyde and University of Macau, University of Reading and so on.
 |
All submitted papers will be sent to 2-3 peer reviewers for reviewing. And accepted and presented papers will be published into ICMLC 2025 International Conference Proceedings Series by ACM (ISBN: 979-8-4007-0702-5), which will be indexed by Ei Compendex and Scopus index, like the previous conferences. |
● ICMLC 2024 Conference Proceedings (ISBN: 979-8-4007-0923-4) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2023 Conference Proceedings (ISBN: 978-1-4503-9841-1) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2022 Conference Proceedings (ISBN: 978-1-4503-9570-0) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2021 Conference Proceedings (ISBN: 978-1-4503-8931-0) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2020 Conference Proceedings (ISBN: 978-1-4503-7642-6) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2019 Conference Proceedings (ISBN: 978-1-4503-6600-7) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2018 Conference Proceedings (ISBN: 978-1-4503-6353-2) | ACM Digital Library | Ei Compendex & Scopus
● ICMLC 2017 Conference Proceedings (ISBN: 978-1-4503-4817-1) | ACM Digital Library | Ei Compendex & Scopus
Welcome related scholars, students to submit your full paper or abstract by Electronic Submission System or email: icmlc@vip.126.com.
Template Download：Paper Template-Word File Paper Template-LaTeX
Official Language：English Please prepare your paper according to paper template
Full paper or abstract submission deadline.
You will receive the final review results about your paper or abstract before or on that date.
Please submit your registration files including final papers before that date.
Delegate: attend the conference without paper publication nor presentation
For delegate registration, please click the below button to make the quick registration and return us the payment email within 3 working days. The registration will be successful until receive the confirmation from the conference secretary.
Monday-Friday:10am-5:30pm
Ms. Allison Fung
Email: icmlc@vip.126.com
Tel: +86-13258-11111-7
WeChat: asr2020217
(微信添加请备注ICMLC 2025, 以便通过)
Session 1: Impact of Data Quality Improvement on Machine Learning（Submit here）
Chairs: Jing Zhang (Southeast University, China) and Ming Wu (Hohai University, China)
Session 2: Learning-based System Design and Verification Technologies for Complex Embedded Systems（Submit here）
Chairs: Shuai Zhao (Sun Yat-sen University, China) and Wenle Wang (Jiangxi Normal University, China)
This is the previous conferences photo gallery. You could know more about the conference history.
Co-sponsored By |
|
 
|
|
Technically Supported By |
|
     
|
|
Published By |
|
|
|
Copyright © 2025 17th International Conference on Machine Learning and Computing