Hosting a Practical Know-How Course on AI and Machine Learning Applications in Industrial Equipment
The IR Engineer Education Research Institute will host an online seminar on May 26, 2026, detailing the practical implementation of AI and machine learning for industrial equipment. The course tackles real-world site challenges, including data collection and generative AI applications.
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- 📰 Published: March 30, 2026 at 19:00
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The IR Engineer Education Research Institute will hold an online seminar that explains everything from the principles of neural networks and reinforcement learning to the essentials of time-series data collection, conversion into training data, and IoT system construction from an implementation perspective.
In facility domains such as manufacturing floors and commercial buildings, the utilization of AI for system control and anomaly detection is expanding. However, during on-site implementation, engineers often face 'realistic challenges' that are difficult to solve with theory alone, such as constraint conditions specific to each piece of equipment, information security, bias in collected data, and securing the necessary quantity and quality of data for training. Based on these challenges, this seminar provides 'field-ready' implementation know-how from both theoretical and practical standpoints.
## Seminar Overview
* Seminar Name: Practical Know-How for Applying AI and Machine Learning to Industrial Equipment
* Format: Online (Zoom LIVE streaming / Archive streaming)
* Dates:
[LIVE Streaming] Tuesday, May 26, 2026, 10:00 - 16:00
[Archive Streaming] May 28 - June 11, 2026
* Capacity: 20 people
* Tuition Fee: 49,500 yen / person (tax included)
* Instructor: Chuzo Ninagawa (Representative Director, N Research Institute Co., Ltd.)
## Course Details
In this seminar, after an overview of industrial applications of machine learning, participants will learn step-by-step about modeling controlled objects, predicting equipment maintenance, reinforcement learning for facility management, and tips for collecting training data and system design. In particular, it focuses on 'data collection' and 'preparing data for use in training'—which often become bottlenecks on-site—and covers concepts like ideal collection distribution and countermeasures when data is scarce (the SMOTE method).
Furthermore, it will explain the internal principles of Transformers, the foundation of generative AI which is gaining attention as a cutting-edge technology, and its industrial application examples (research cases) such as time-series trend prediction and event occurrence forecasting.
## Seminar Program
1. Overview of industrial applications of machine learning (Research example videos / field application / actual samples)
2. Modeling controlled objects (NN basics / step response / black-box models of multivariable control)
3. Equipment maintenance prediction (LSTM / sudden event prediction / accuracy evaluation metrics)
4. Reinforcement learning for facility management (Q-learning / transfer learning / optimal economic operation / shortening learning periods)
5. Actual practices of training data collection (Quantity and quality / ideal distribution / SMOTE / tools vs. self-made)
6. Tips for realistic system design (Initial strategy / target selection / team building / limitations of tools)
7. Advanced industrial applications of generative AI technology (Overview of Transformers / time-series forecasting / event prediction)
8. Summary and Q&A
### Target Audience
* Those who want to learn specific implementation methods and case studies of machine learning in industrial settings
* Engineers/team leaders involved in development, design, and production management related to system control
* Personnel from equipment/device manufacturers, infrastructure/industrial system manufacturers, civil engineering/construction, and related companies
* Those who are already proceeding with implementation but are struggling with on-site challenges (data, operations, design)
In facility domains such as manufacturing floors and commercial buildings, the utilization of AI for system control and anomaly detection is expanding. However, during on-site implementation, engineers often face 'realistic challenges' that are difficult to solve with theory alone, such as constraint conditions specific to each piece of equipment, information security, bias in collected data, and securing the necessary quantity and quality of data for training. Based on these challenges, this seminar provides 'field-ready' implementation know-how from both theoretical and practical standpoints.
## Seminar Overview
* Seminar Name: Practical Know-How for Applying AI and Machine Learning to Industrial Equipment
* Format: Online (Zoom LIVE streaming / Archive streaming)
* Dates:
[LIVE Streaming] Tuesday, May 26, 2026, 10:00 - 16:00
[Archive Streaming] May 28 - June 11, 2026
* Capacity: 20 people
* Tuition Fee: 49,500 yen / person (tax included)
* Instructor: Chuzo Ninagawa (Representative Director, N Research Institute Co., Ltd.)
## Course Details
In this seminar, after an overview of industrial applications of machine learning, participants will learn step-by-step about modeling controlled objects, predicting equipment maintenance, reinforcement learning for facility management, and tips for collecting training data and system design. In particular, it focuses on 'data collection' and 'preparing data for use in training'—which often become bottlenecks on-site—and covers concepts like ideal collection distribution and countermeasures when data is scarce (the SMOTE method).
Furthermore, it will explain the internal principles of Transformers, the foundation of generative AI which is gaining attention as a cutting-edge technology, and its industrial application examples (research cases) such as time-series trend prediction and event occurrence forecasting.
## Seminar Program
1. Overview of industrial applications of machine learning (Research example videos / field application / actual samples)
2. Modeling controlled objects (NN basics / step response / black-box models of multivariable control)
3. Equipment maintenance prediction (LSTM / sudden event prediction / accuracy evaluation metrics)
4. Reinforcement learning for facility management (Q-learning / transfer learning / optimal economic operation / shortening learning periods)
5. Actual practices of training data collection (Quantity and quality / ideal distribution / SMOTE / tools vs. self-made)
6. Tips for realistic system design (Initial strategy / target selection / team building / limitations of tools)
7. Advanced industrial applications of generative AI technology (Overview of Transformers / time-series forecasting / event prediction)
8. Summary and Q&A
### Target Audience
* Those who want to learn specific implementation methods and case studies of machine learning in industrial settings
* Engineers/team leaders involved in development, design, and production management related to system control
* Personnel from equipment/device manufacturers, infrastructure/industrial system manufacturers, civil engineering/construction, and related companies
* Those who are already proceeding with implementation but are struggling with on-site challenges (data, operations, design)