The emerging paradigm of Tsetlin machines makes a fundamental shift from arithmetic-based to logic-based machine learning. At the core, finite state machines, based on learning automata, learn patterns using logical clauses, and these constitute a global description of the task learned. In this way, the Tsetlin machine introduces the concept of logical interpretable learning, where both the learned model and the process of learning are easy to follow and explain. As a result, it reduces the expertise needed to apply ML techniques efficiently in various domains. The paradigm has enabled competitive accuracy, scalability, memory footprint, inference speed, and energy consumption across diverse tasks, including classification, regression, natural language processing(NLP), and speech understanding.

List of Topics

• Tsetlin Machines
• Learning Automata
• Novel AI Algorithms
• Explainable and Interpretable AI
• Energy-efficient AI Systems Design
• New AI Applications including signal and image processing
• Intelligent Data Preprocessing

Committees

General Co-Chairs

• Rishad Shafik, Newcastle University, UK
• Lei Jiao, University of Agder, Norway

Program Co-Chairs

• Rebekka Olsson Omlandseter, University of Agder, Norway
• Darshana Abeyrathna, DNV, Norway

Financial Chair

• Anne Lene Fjeldavlie, University of Agder, Norway

Publicity Chair

• Pål Grandal, University of Agder, Norway

Publication

All accepted papers in ISTM'25 will be made available in IEEE Xplore

Venue

The conference will be held at Sapienza University of Rome

Contact

All questions about submissions should be emailed to 

• Pål Grandal (pal.grandal@uia.no)
• Lei Jiao (lei.jiao@uia.no)
• Rebekka Olsson Omlandseter (rebekka.o.omslandseter@uia.no)
• Darshana Abeyrathna (a.darshana1991@gmail.com)

Sponsors

• University of Agder
• Newcastle University
• University of Pittsburgh
• Literal Labs
• NORA.ai

Technical Co-Sponsor

• IEEE Computer Society
• IEEE
• TCDE