This document is the preface of the Machine Learning Workshop of the 8th International Conference on Computational Linguistics and Intelligent Systems (CoLInS 2024), April 12-13, 2024, held in Lviv, Ukraine. The main purpose of the Machine Learning Workshop is the recent research results discussion in all areas of Machine Learning Technology for Natural Language Processing and Intelligent Systems Development [1-6]. The conference is soliciting literature review, survey and research paper comments including, whilst not limited to, the following areas of interest: • Supervised learning; • Unsupervised learning; • Semi-supervised learning; • Reinforcement learning; • Decision trees; • Classification; • Regression; • Artificial neural networks; • Associative rules; • Deep learning; • Pattern recognition; • Cluster analysis; • Data mining. The language of the Machine Learning Workshop is English. The Machine Learning Workshop took the form of an oral presentation by peer-reviewed individual papers. The papers were distributed among 93 external reviewers from France, Germany, The Netherlands, the United States, the United Kingdom, India, Moldova, Czech Republic, Poland, Bulgaria, Portugal, Serbia, Slovenia, Finland and Ukraine. To make more correct decisions regarding the acceptance or rejection the papers got 3-7 reviews. The Machine Learning Workshop gathered participants from different countries including Germany, Poland, Ukraine, Kyrgyzstan, Egypt, Czech Republic, Switzerland, India, and France. This year Organizing Committee received 45 submissions, out of which 24 were accepted for presentation as regular papers. These papers and extended abstracts were published in Volume I
of the 8th International Conference on Computational Linguistics and Intelligent Systems (CoLInS 2024) proceedings.
The Machine Learning Workshop would not have been possible without the support of many people. First of all, we would like to thank all the authors who submitted papers to the Machine Learning Workshop and thus demonstrated their interest in the research problems within our scope. We are very grateful to the members of our Program Committee for providing timely and thorough reviews and, also, for being cooperative in doing additional review work. We would like to thank the Organizing Committee of the conference whose devotion and efficiency made this instance of the Machine Learning Workshop a very interesting and effective scientific forum. We would like to thank the Machine Learning Workshop Chairs, as well as the Program Committee and all Reviewers, for their diligence in selecting the papers and ensuring their high scientific quality.