As one method in the wide field of Artificial Intelligence, Machine Learning is a data analytics technique based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Growing volumes of available data as well as cheaper and more powerful computational processing allow a deeper understanding of manufacturing processes, which can lead for example to an earlier identification of risks or a better fulfillment of customer demands. Through the application of Machine Learning, it will be possible to gain even bigger insights to the captured data, since it enables a generation of models that can analyze bigger, more complex data and deliver faster, more accurate results. Today, Machine Learning applications can be found in various areas: From financial services for credit scoring and algorithmic trading over biology for tumor detection or drug testing to manufacturing industry for predictive maintenance through image and pattern recognition.

In order to learn more about Machine Learning to get familiar with a methodology how to implement Machine Learning projects and to get insights from case studies in industry, the European Chamber Nanjing chapter co-organized with GAMI to hold a 1.5 days training together with their partners wbk, amtc and Solomon on July 21 and July 23, 2020. This will include an online and an onsite session, to share with you knowledge and know-how on Machine Learning.

Part 1 – Online Session on July 21, 2020

Agenda:

13:30 – 13:40 Introduction

13:40 – 14:10 Keynote - What is Machine Learning?

14:10 – 14:40 The Machine Learning Implementation Model - The CRISP-DM Process

14:40 – 15:20 Machine Learning Algorithms

15:20 – 15:35 Coffee Break

15:35 – 16:35 Practical Implementation Example

16:35 – 17:15 Examples from the Industry

17:15 – 17:30     Wrap-Up 

Besides the online training (part I), we highly recommend you to also join the onsite training (part II) on July.23 at Suzhou GAMI. Details about the site training, please click here. 

Payment:

1. Bank transfer:

RMB Bank Account
A/C Name: 中国欧盟商会
A/C No.: 7110210182500027543
China CITIC Bank 中信银行京城大厦支行
Capital Mansion Sub Branch
No. 6, Xinyuan Nan Road
Chaoyang District, 100004 Beijing
北京市朝阳区新源南路6号京城大厦
S.W.I.F.T. Code: CIBKCNBJ100

2. Pay onsite through cash or Wechat.

Terms & Conditions:

This event has limited seating and requires registration and payment in advance. No walk-ins accepted.

Cancellation Policy:

If you cannot attend this training, please cancel your registration at least 3-working days before. If you fail to notify us of your cancellation in a timely fashion, you will be charged for event costs.

To cancel, please contact Ms. Annie Zhou at azhou@europeanchamber.com.cn.

Disclaimer免责声明:
Participants must maintain proper social distancing norms and abide by the Nanjing Municipal Government ’s Epidemic Prevention and Control Rules. If you do not follow the relevant regulations, the European Union Chamber of Commerce in China accepts no responsibility for any of your physical discomfort caused during and after the event. Thank you for your cooperation.

参与活动时，参会人应与他人保持适当距离（防疫考虑）并遵守南京市政府颁布的疫情预防控制要求（例如佩戴口罩等）。如您违反要求，中国欧盟商会对于您在活动中或者活动后的身体不适，不承担任何责任。 感谢您的理解和配合。

Speakers

Mr. Tobias Schlagenhauf

Research Associate @ wbk –Institute of Production Science

Mr. Tobias Schlagenhauf

Research Associate @ wbk –Institute of Production Science

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Research
Deep Learning models for Failure Detection
Condition Monitoring of Ball Screw Drives
Industrial Speech Recognition
Machine learning for Product, Process and Plant automatization
Condition monitoring for Machine Components

Project Experience
Machine Learning in the Context of Laser Beam Welding
AI driven Supported Maintenance
Artificial Intelligence in the straightening Process
Intelligent System for Detection of Surface Defects
Machine Learning for Anomaly Detection

Mr. Markus Netzer

Research Associate @ wbk –Institute of Production Science

Mr. Markus Netzer

Research Associate @ wbk –Institute of Production Science

View speaker details

Research
Machine Learning for Product-, Process-and Plant automatization
Condition Monitoring for Machine Components
Detection of Anomalies in automated operating Procedure
Pattern recognition in time series

Project Experience
AI based Anomaly Detection in machine tools
Condition Monitoring for Ball Screw Drives
Sensors integration for intelligent components and machines
Intelligent process control for additive manufacturing

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