This course provided in-depth knowledge of advanced topics in Tsetlin machines, offering both theoretical understanding and practical skills. I learned to analyze, implement, and evaluate the main Tsetlin machine architectures, including regression, convolution, auto-encoding, and composites.
Module 1: The Tsetlin Automaton and Games of Tsetlin Automata
The foundation module covered the Tsetlin automata and games of automata, which form the basis for Tsetlin machines. I studied individual Tsetlin automata and collectives of Tsetlin automata through game theory, understanding how these fundamental components work together.
Module 2: The Tsetlin Machine - Inference and Learning
This module introduced the vanilla Tsetlin machine, covering booleanization, clauses, voting, and learning with feedback Type I and II. I gained a deep understanding of how Tsetlin machines perform inference and learn from data.
Module 3: Selected Applications
I explored detailed applications in Natural Language Processing, Image Analysis, Board Games, and Edge Computing. This module covered how to booleanize input, design models, and interpret learning outcomes in various domains. The practical applications helped me understand how Tsetlin machines can be applied to real-world problems.
Module 4: Markov Chain Analysis of Learning Process
This module focused on Markov chain analysis in combination with game theory to study the convergence of Tsetlin machine learning. I learned mathematical techniques for analyzing and understanding the learning dynamics of Tsetlin machines.
Module 5: Advanced Architectures
The final module addressed the analysis, design, and application of more advanced architectures, including regression, convolution, auto-encoding, and composites. I learned how to work with these sophisticated architectures and apply them to complex problems.
By the end of the course, I was able to apply Tsetlin machine architectures within image analysis, natural language processing, and board games. This course has provided me with expertise in an interpretable and efficient alternative to neural networks, with applications spanning multiple domains of AI.