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AI Development

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etc. (AI)

Manufacturing Innovation and AI Big Data for CEOs and Leaders

You can benchmark manufacturing innovation strategies for semiconductors and run machine learning and deep learning on semiconductor data without coding.

(4.0) 1 reviews

15 learners

  • 1159136
머신러닝
반도체
AI
Machine Learning(ML)
classification

What you will learn!

  • Semiconductor Big Data AI Cases

  • NO CODING(Orange 3) implementation

Lecture Summary📖

1) What kind of data will be collected in semiconductor and display manufacturing sites to drive innovation activities?
2) As a CEO and leader, is there any way to know whether a project that introduces artificial intelligence is progressing properly?
3) Is it possible to freely use machine learning or deep learning without coding?


Target audience👨‍💻

1) CEOs and executives who want to introduce artificial intelligence through manufacturing-related innovation activities.
2) Leaders of organizations seeking to introduce artificial intelligence in manufacturing
3) Those who want to implement artificial intelligence, machine learning, and deep learning without coding.


Expected effect💁‍♂️

1) You will be able to benchmark manufacturing innovation plans for semiconductor and display big data and come up with ideas on how to introduce them in your own domain.
2) You can freely experiment with machine learning and deep learning without coding and select the optimal artificial intelligence algorithm.
3) By acquiring the ability to derive insights through machine learning and deep learning algorithms using each domain's data, you can improve the field.

Curriculum📕

Lecture 1. Semiconductor Display Manufacturing Innovation and AI Big Data (Innovation Cases: Production, Yield, Quality)
Lecture 2 : Semiconductor Display Manufacturing Innovation and AI Big Data (Innovation Cases: Infrastructure, Environmental Safety, Energy)
Lesson 3 : Preliminary Preparation / Data Preprocessing and Visualization
Lecture 4 : Data Preprocessing and Visualization / Machine Learning (Classification: k-NN)
Lecture 5. Machine Learning (Classification: Logistic Regression, Tree, Random Forest, SVM)
Lecture 6. Machine Learning (Classification: Naïve Bayes, Neural Network)
Lecture 7. Machine Learning (Classification: Stacking, Adaboost / Regression: k-NN, Tree, Random Forest, Linear Regression, Linear Regression, Neural Network, Bias and Variance, Ensemble and Bagging, Boosting, XGBoost, Stacking)
Lecture 8. Machine Learning (Image Classification, Clustering: k-Means, Hierarchical Clustering) /
Deep learning (DNN, CNN)

10 Reasons Why AI Training for CEOs, Executives, and Leaders Is Important
1. If the CEO/executive doesn't do it first, no employee will.
2. Artificial intelligence is not something that can be left to employees and carried out on its own.
3. If CEOs/executives only have an abstract understanding of AI, they will not be able to make sound decisions.
4. AI experts are expensive, making it difficult to hire them arbitrarily. Even if they were hired, it's difficult to know what questions to ask and how to ask them during interviews, and even to determine whether their answers are correct or incorrect.
5. Training employees can be very cost-effective and time-saving, but the CEO/executive must first know how to train them.
6. Ideas for AI implementation should come from the CEO/executive who has the most comprehensive knowledge of the business system.
7. When CEOs and executives learn about AI themselves, they realize that implementing AI is not that difficult.
8. If the CEO or executive leading the project doesn't understand AI, the project could easily go astray. If they envision an AI at the level of AlphaGo, only to find that its performance falls short of expectations, they could immediately revert to their original methods.
9. Once you learn what AI really is, your expectations and ambitions will be lowered, and you will start thinking about ways to improve accuracy.
10. Ultimately, the CEO/executives make all the decisions about this, so if they don't know the details, they can't do anything.
(Source: Book , author Jang Dong-in, publisher Hanbit Media)

💾 Things to note before taking the class

This lecture is a re-edited version of an online seminar conducted via video conference. Please note!
Depending on your environment, the sound quality may feel uneven. Please check the preview lecture before attending!

  • All you need is a computer (desktop or laptop).

  • A higher spec computer may run faster, but there won't be a huge difference.

  • We use Orange, an open-source data mining toolkit. Please download and use it fromthe link .

  • The semiconductor dataset files for the practice are attached in Section 0-Unit 3.

Recommended for
these people

Who is this course right for?

  • CEOs and executives keen on AI adoption through manufacturing innovation.

  • Organizational leaders seeking to adopt manufacturing-related AI

  • Those who wish to implement Artificial Intelligence, Machine Learning, Deep Learning without coding

Hello
This is

After majoring in electronic engineering at Kwangwoon University, he served as the head of the manufacturing team and the head of the manufacturing center in the semiconductor division of Samsung Electronics for 27 years from 1984 to 2011, and the head of the infrastructure technology center in charge of environmental safety, facility, and systems. After completing his career in semiconductors as an executive director, he served as the vice president of Samsung Display (OLED) for five years from 2011 to 2016 as the head of the manufacturing center and the general manager of the complex in charge of production, environmental safety, facility, and systems. After graduating from the Graduate School of Science in Seoul and Business School Lausanne (BSL), he earned MBA, Ph.D., and DBA degrees in big data from the Graduate School of Science in Switzerland, and is currently serving as the head of the manufacturing intelligence project as a professor at the Swiss School of Management (SSM), a research professor at the Korea Institute for Industrial Policy Studies, and vice chairman of the artificial intelligence association.

상기 컨설턴트는 광운대에서 전자공학을 전공한 뒤 1984년부터 2011년까지 27년동안 삼성전자 반도체 부문에서 제조 팀장 및 제조 센터장을 거쳐 환경안전, Facility, 시스템을 총괄하는 인프라 기술 센터장을 지냈다. 전무로 반도체의 경력을 마친 뒤 2011년부터 2016년 까지는 5년간 삼성 디스플레이(OLED, LCD)에서 부사장으로서 제조 센터장 및 생산, 환경안전, 퍼실리티, 시스템을 책임지는 단지 총괄을 맡았다. 퇴임 후 서울 과학 종합 대학원 대학교와 스위스 BSL(Business School Lausanne)에서 빅데이터 MBA 와 Ph.D., DBA 학위를 취득한후 현재는 SSM(Swiss School of Management) 교수, 산업정책연구원 연구 교수, 사단 법인 인공지능협회 부회장으로 제조 지능화 사업단장 업무를 수행 중에 있다.

Curriculum

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9 lectures ∙ (7hr 11min)

Course Materials:

Lecture resources
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