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Fundamentals of Tsetlin Machines

Prof. Ole-Christoffer Granmo
Data dell'evento: 
Venerdì, 21 June, 2024 - 16:30
Via Ariosto 25, Aula A2
The emerging paradigm of Tsetlin machines provides a fundamental shift from arithmetic-based to logic-based machine learning. At the core, finite state machines, so-called Tsetlin automata, learn patterns using logical clauses, and these constitute a global description of the task learnt. In this way, Tsetlin machines introduce the concept of logical interpretable learning, where both the learned model and the process of learning are easy to follow and explain. The combination of finite state machines and logical rules is particularly suited for cutting-edge hardware solutions, enabling nano-scale intelligence, ultra-low energy consumption, energy harvesting, and unrivaled inference speed. Tsetlin machines offer competitive accuracy, scalability, speed, and energy efficiency across diverse tasks, including classification, convolution, regression, natural language processing, and speech understanding. In this short course, I cover the basics and recent advances of Tsetlin machines.
June 21
     16:30-18:00 Lecture 1: The Tsetlin Automaton and Games of Tsetlin Automata
June 25
     13:30-15:00 Lecture 2: The Tsetlin Machine - Inference and Learning.
June 26
     13:30 - 15:00 Lecture 3: Selected Applications in Natural Language Processing, Image Analysis, Board Games, and Edge Computing 
June 27
      13:30 - 15:00 Lecture 4: Markov Chain Analysis of Learning Process
June 28
      13:30 - 15:00 Lecture 5: Advanced Architectures: Regression, Convolution, Auto-encoding, and Composite
Professor Ole-Christoffer Granmo is the Founding Director of the Centre for Artificial Intelligence Research (CAIR), University of Agder, Norway. He obtained his master’s degree in 1999 and his PhD degree in 2004, both from the University of Oslo, Norway. In 2018, he created the Tsetlin machine, for which he was awarded the AI research paper of the decade by the Norwegian Artificial Intelligence Consortium (NORA) in 2022. Dr. Granmo has authored over 180 refereed papers with eight paper awards in machine learning, encompassing learning automata, bandit algorithms, Tsetlin machines, Bayesian reasoning, reinforcement learning, and computational linguistics. He has further coordinated 7+ research projects and graduated 55+ master- and nine PhD students. Dr. Granmo is also a co-founder of NORA. Apart from his academic endeavors, he co-founded the company Anzyz Technologies AS.


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