(MLMI 2025) 2025 The 8th International Conference on Machine Learning and Machine Intelligence
artificial intelligenceComputer Science and Technologies
Conference Date
Jul 25-Jul 27, 2025
Submission Deadline
Feb 20, 2025
E-mail
mlmi_contact@163.com
Telephone
+86-18302820449
Full name: 2025 The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025)
Abbreviation: MLMI 2025
Kyoto, Japan | July 25-27, 2025
The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025) will be held in Kyoto, Japan during July 25-27, 2025. MLMI 2025 is organized by Ritsumeikan University, Japan.
Insightful presentations, engaging discussions, vibrant networking – MLMI 2025 has it all. With leading academics on the scientific committee of the event, the program is guaranteed to address the most relevant topics in the field of machine learning and machine intelligence. It's an opportunity to source feedback on your research, to get published in conference proceedings, and to explore the beautiful city Osaka, Japan.
Publication:
Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published in the Conference Proceedings of MLMI 2025. (*All the previous Conference Proceedings have been archived in ACM Digital Library and have been indexed by Ei Compendex & Scopus.)
Topics:
Topics of interest for submission include, but are not limited to:
- Artificial Neural Networks
- Genetic Algorithms
- Association Rule Learning
- Inductive Logic Programming
- Automata, Logic and Games
- Intelligent Systems
- Bayesian Networks
- Lambda Calculus and Types
- Clustering
- Logic and Proof
- Commercial Software
- Machine Learning
- Commercial Software with Open-Source Editions
- Models of Computation
- Complex Systems
- Object-Oriented Programming
- Computational Complexity
- Open-Source Software
- Computational Learning Theory
- Pattern Recognition
- Computational Linguistics
- Reinforcement Learning
- Computer Animation
- Representation Learning
- Computer Science
- Similarity and Metric Learning
- Computer System
- Sparse Dictionary Learning
- Concurrent Algorithms and Data Structures
- Supervised Learning
- Data Mining
- Support Vector Machines
- Decision Tree Learning
- Unsupervised Learning
- Deep Learning
For more topics, please visit: http://www.mlmi.net/cfp.html
Submission Guideline:
♢ English is the official language. Paper should be prepared in English.
♢ Abstract submission is for presentation only without publication.
♢ Full paper submission is for both presentation and publication. (No less than 8 pages)
♢ Submission Methods:
- By online submission system: http://confsys.iconf.org/submission/mlmi2025
- Or Submit to mlmi_contact@163.com as attachment
For more details, please visit: http://www.mlmi.net/submission.html
Ritsumeikan University, Japan:
The history of Ritsumeikan dates back to 1869 when Prince Kinmochi Saionji, an eminent international statesman of modern Japan, founded “Ritsumeikan” as a private academy on the site of the Kyoto Imperial Place. Ritsumeikan University offers a wide range of courses in advanced studies at its Kinugasa Campus in Kyoto, Biwako-Kusatsu Campus (BKC) in Shiga, and Osaka Ibaraki Campus in Osaka (OIC). Today, Ritsumeikan has become one of the most prestigious private universities in Japan, consistently earning one of the highest rankings among Japanese private universities in the renowned QS world university rankings. With its distinct strength in international orientation, it has been selected as part of the ongoing Top Global University Project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) since 2014. Ritsumeikan University is also being highly appraised by society as the world-class educational institution which attracts many students both in Japan and overseas, consisting of 16 colleges and 22 graduate schools.
Contact Us:
Conference Secretary: Miss Joie Wu
Email: mlmi_contact@163.com
Tel: +86-18302820449
Website: http://mlmi.net/
Office Hour: Monday-Friday, 9:30-18:00 (GMT+8)