I have taught many subjects. Each one changed how I understand the others.
I began teaching advanced combinatorial optimisation and innovation workshops in 2008, followed by artificial intelligence, software architecture, senior projects, data structures and algorithms, computational intelligence and distributed information systems. At KMUTT, this path expanded into computer networks, digital marketing, enterprise resource planning, big data analytics, and data science and engineering.
The classroom did not only ask me to explain what I already knew. It repeatedly showed me where my understanding was fragmented. Algorithms became more meaningful when connected to data structures. Machine learning became more honest when connected to evaluation and production. Networks, distributed systems and big data revealed that a correct idea at small scale may become a different problem at system scale. ERP and digital marketing reminded me that technology has value only when it can be understood within the work, decisions and people around it.
Teaching in universities, industry programmes, LearnAI, Super AI Engineer, AI project coaching and the International Olympiad in Informatics also taught me to explain the same principle at different depths. I am now revisiting archived teaching materials and rebuilding them as contemporary, readable course collections. Some are already available; others need more careful revision before they return.
I hope these pages become more than stored lecture notes. I hope you enjoy learning from them, questioning them, and using them to build something better.