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Questions and Answers

랜덤 포레스트의 경우 각각의 개별 트리가 서로 동일하지 않도록 어떻게 구성합니까?

동일한 훈련 세트에서 알고리즘을 여러 번 훈련합니다. 그러면 자연스럽게 다른 나무가 생길 것입니다.

B개의 트리를 훈련하는 경우, 각 트리가 서로 다른 세트로 훈련되도록 훈련 세트의 1/B에 대해 각각 훈련시킵니다.

복원(with replacement)을 통해 훈련 데이터 샘플링 (correct)

복원없이(without replacement) 훈련 데이터 샘플링

LSTM의 주요 구성 요소는 무엇입니까?

망각 게이트

입력 게이트

출력 게이트

세 가지 모두 (correct)

GRU는 LSTM의 복잡한 구조를 포함하고 있다.

False

LSTM 모델의 cell state는 어떤 역할을 합니까?

정보를 쉽게 흐르게 하여 기억하거나 잊을 수 있도록 함 Signup and view all the answers

LSTM과 GRU 모두 _____ 문제를 해결하기 위해 RNN 기반 모델입니다.

기울기 폭발/소실 Signup and view all the answers

다음의 RNN 모델과 그 장단점을 연결하세요:

Vanilla RNN = 비선형 관계, 복잡한 모델 해석 LSTM = 강력한 성능, 높은 복잡성 GRU = 간단한 구조, 장기 의존성 캡처에 덜 효과적 ARMA = 선형 회귀 기반 접근, 낮은 모델 확장성 Signup and view all the answers

기계 학습 분류기를 사용하여 데이터 과학자는 정확도 몇 %를 얻었나요?

96.2% Signup and view all the answers

역사적 결함 비율은 얼마인가요?

3.8% Signup and view all the answers

모델을 배포한 후 결함이 있는 제품을 탐지하는 데 개선이 있었습니까?

False Signup and view all the answers

다수 클래스는 ________ 클래스입니다.

부정 Signup and view all the answers

소수 클래스는 ________ 클래스입니다.

긍정 Signup and view all the answers

SVM은 무엇을 찾는 데 주력합니까?

하이퍼플레인 Signup and view all the answers

소프트 마진 SVM은 주어진 결정 경계를 교차할 때 어떤 것을 부여합니까?

패널티 Signup and view all the answers

SHAP의 기초는 무엇입니까?

Shapley 값 Signup and view all the answers

SHAP의 목표는 무엇인가요?

로컬 및 글로벌 해석 제공 Signup and view all the answers

ARMA 모델에서 p가 의미하는 것은 무엇인가요?

자기 회귀 차수 Signup and view all the answers

이상 탐지의 무 감독 ML 기반 해결책은 무엇인가요?

iForest Signup and view all the answers

데이터 기반 의사결정의 정의는 무엇인가?

데이터를 사용하여 의사결정 과정에 정보를 제공하고 행동 방침을 검증하는 과정이다. Signup and view all the answers

다음 중 데이터 분석의 프로세스에 포함되지 않는 것은 무엇인가?

데이터 훈련 Signup and view all the answers

스마트 제조는 고급 데이터 분석과 인공지능의 통합을 포함한다.

True Signup and view all the answers

RUL(잔여 유용 수명) 추정에 대한 정의는 무엇인가?

기계나 장비가 고장나기 전에 얼마나 오래 사용할 수 있는지를 평가하는 것. Signup and view all the answers

결함 예측에서 MLP는 무엇의 약자입니까?

다층 퍼셉트론(Multilayer Perceptron) Signup and view all the answers

예측 분석의 한 가지 유형은 ______ 모델링이다.

예측 Signup and view all the answers

데이터 불균형 문제의 예로 적절하지 않은 것은 무엇인가?

모든 클래스가 균형을 이루는 경우 Signup and view all the answers

단일주기 이탈 감지기는 데이터를 항상 일관되게 분류합니다.

False Signup and view all the answers

다양한 데이터를 조합하여 처리하는 기법은 ______ 머신러닝이다.

다중모달 Signup and view all the answers

렐루 함수란 무엇인가요?

Rectified Linear Unit의 약자이며, 비선형 활성화 함수로 널리 사용된다. Signup and view all the answers

렐루 함수는 입력이 ______일 경우 0을 출력합니다.

음수 Signup and view all the answers

렐루 함수는 모든 입력에 대해 선형성을 유지한다.

False Signup and view all the answers

렐루 함수의 정의는 무엇인가요?

ReLU(z) = max(0, z) Signup and view all the answers

렐루 함수를 사용하면 어떤 결과를 얻을 수 있나요?

복잡한 비선형 함수 생성 가능 Signup and view all the answers

렐루 함수와 리니어 함수는 동일한 출력을 제공합니다.

False Signup and view all the answers

간단한 뉴럴 네트워크 모델의 구성은 어떻게 되나요?

하나의 히든 레이어에 세 개의 렐루 노드와 하나의 리니어 출력 노드로 구성된다. Signup and view all the answers

인터랙티브 실험에서 조절 가능한 요소는 무엇인가요?

모든 것 Signup and view all the answers

렐루 함수가 신경망에서 어떤 역할을 하나요?

신경망에서 비선형성을 구현하는 데 기여한다. Signup and view all the answers

Study Notes

Decision Making in Manufacturing

Decision making is a process of selecting a course of action from various alternatives.
Managers hold the authority to make crucial decisions and bear the consequences.
Effective management is linked to making sound decisions and achieving successful outcomes.

Data-Driven Decision Making (DDDM)

DDDM involves using data to guide decision-making and validate actions before implementation.
It contrasts evidence-based choices against intuition and experience; however, it may lack creativity and intuition.
Steps involved in DDDM include:
- Prescriptive Analytics: Identifying optimal decisions for desired results.
- Predictive Modeling: Forecasting future outcomes based on past data trends.
- Forecasting: Assessing whether observed trends will continue.
- Statistical Analysis: Investigating causes behind problems.

Data Analytics Process

Data analytics involves various stages, including data acquisition, feature engineering, model evaluation, and deployment.
Incorporates UI/UX and system integration support for effective deployment of analytical models.

Smart Manufacturing

Smart manufacturing combines advanced data analytics, AI, and ICT for enhanced efficiency and productivity.
Utilizes various sensors:
- Temperature, Pressure, Proximity, Force, Flow, Motion, Optical/Infrared, and Noise sensors among others.

Remaining Useful Life (RUL) Estimation

RUL estimation focuses on predicting the lifespan of components based on health indicators.
Two primary approaches:
- Physics-driven: Based on physical models of degradation.
- Data-driven: Utilizes historical data for predictions.
Overcomes challenges like data scarcity through physics-informed data generation.

Fault Prediction

Image-based techniques, such as CNNs, are used for defect detection in manufacturing.
Address issues like data imbalance through sampling methods and data augmentation.
Employs anomaly detection methods in scenarios with insufficient labeled data.

Yield Prediction

Wafer yield prediction utilizes both time-series and non-time-series data to assess output efficiency.
Recognizes the importance of analyzing both sequential and non-sequential data for accurate predictions.

Key Considerations in Manufacturing Data Analytics

Clearly define business questions, such as RUL estimation, fault/yield prediction.
Identify appropriate models (Supervised, Semi-supervised, Unsupervised) for the data and questions.
Address potential issues like lack of failure data, data imbalance, absence of labeled data, and model explainability.

Machine Learning Applications in Manufacturing

Anomaly detection using Isolation Forests and fuzzy support vector machines for imbalanced data classifications.
Time-series models like ARMA, RNN, LSTM, and GRU for forecasting trends in manufacturing data.

Anomaly Detection

Identification of data points that deviate from standard patterns, indicating potential issues in manufacturing processes.
Techniques involve semi-supervised or unsupervised learning approaches to detect anomalies effectively.### Anomaly Detection in Machine Learning
Isolation Forest (iForest):
- Uses binary decision trees for anomaly detection by isolating individual instances.
- Effective for both anomaly detection and outlier elimination.
- Anomalies are defined by fewer instances and distinct attribute values.
Decision Trees:
- A supervised learning method applicable to both regression and classification.
- Structured as a tree, using binary questions to narrow down possibilities.
- Predictive model involves root, intermediate, and terminal nodes to categorize data.
Random Forest:
- An ensemble model that improves decision tree accuracy through bagging and random subspace methods.
- Utilizes bootstrap sampling to create various models from subsets of the training data.
- Result aggregation implemented via majority voting during classification tasks.

Data Imbalance in Classification

Definition of Data Imbalance:
- Occurs when one class has significantly more instances than another, often encountered in machine learning tasks.
- Common scenario involves classification problems where a minority class is overshadowed by the majority class.
Impact of Data Imbalance:
- Using a classifier on imbalanced data can yield high accuracy without true predictive ability, as seen in defect inspections where expected defect rates were 3.8%, yet accuracy reached 96.2%, failing to detect real defects.
Solutions to Data Imbalance:
- Sampling techniques are the simplest approach, including oversampling the minority class or undersampling the majority class.
- Entropy-based Fuzzy Support Vector Machine (EFSVM) adjusts models for better handling of imbalanced classes.

Support Vector Machines (SVM)

Core Objective of SVM:
- To find a hyperplane that maximizes the margin between two classes, enhancing their separation.
- Hard-margin SVM does not permit instances within the margin, while soft-margin SVM allows them, minimizing unrealistic constraints.
Soft-margin SVM:
- Includes a penalty term for instances that cross decision boundaries, enabling greater flexibility.
- Optimizes the margin using hyperparameter control to determine the width of the margin based on instance conditions.
Optimization Goals:
- The formulation involves a cost function that balances margin size and penalties for misclassifications, with constraints differing from hard-margin SVM to allow margin adjustments.

Conclusion

Techniques like Isolation Forest, Random Forest, and SVM are instrumental in addressing various challenges in anomaly detection and classification, particularly in imbalanced datasets, underscoring the significance of tailored approaches in machine learning applications.### Support Vector Machine (SVM)
Non-crossover instances indicated by negative 𝜉𝑖, representing a non-negative condition.
Lagrange multiplier transforms optimization to the Lagrange primal problem (𝐿𝑝).
Original optimization problem constraints are non-negative; thus, the constraints of 𝐿𝑝 reflect this.
Karush–Kuhn–Tucker (KKT) condition establishes that the minimum of 𝐿𝑝 is reached when the partial derivatives with respect to unknowns are zero.
Lagrange primal problem can be transformed to a dual problem with replacement of equations.

Entropy-based Fuzzy Support Vector Machine (EFSVM)

EFSVM aims to decrease the significance of the negative (majority) class in the decision surface.
Entropy (𝐻𝑖) quantifies information of each instance, with the parameter 𝑘 defining the number of nearest neighbors via Euclidean distance.
Higher entropy values indicate instances closer to the margin with lower class certainty; an example employs k=7.
Negative sample entropy is influenced by the number of negative samples (𝑛−).
Define entropy-based fuzzy membership using minimum (𝐻min) and maximum (𝐻max) of 𝐻, separating negative samples into parameter-defined subsets with an increasing entropy order.
The importance of negative class instances is adjusted using an entropy-based fuzzy membership (𝐹𝑀𝑙) and a parameter 𝛽 for reducing importance, keeping the classes indifferent at 𝛽 → 0.

Quadratic Optimization

Quadratic optimization problems for EFSVM incorporate kernels.
The dual problem formulation is aligned for both EFSVM and soft-margin SVM with kernel applications.

Model Explainability

Business decisions require insights to “WHY” questions addressing outputs of regression, classification, and anomaly detection models.
Multiple linear regression assesses feature significance using Student’s t-test to evaluate coefficients against the null hypothesis.
SHapley Additive exPlanations (SHAP) offers clarity by analyzing features' contributions using Shapley values, allowing for local and global interpretations of model predictions.

Time-Series Analysis

Essential components in manufacturing analytics include understanding business questions (e.g., RUL estimation, fault prediction) and determining appropriate machine learning models.
Addressing key issues such as data imbalance, lack of labeled data, and model explainability is vital for accurate analyses.

Autoregressive Moving Average (ARMA)

ARMA modeling integrates a moving average process with linear difference equations.
An ARMA(𝑝, 𝑞) model contains 𝑝 lags in the autoregressive part and 𝑞 lags in the moving average part.
Covariance stationarity ensures that a time series remains stable over time concerning its mean and variance.

Recurrent Neural Network (RNN)

RNNs are designed to detect patterns within sequential data (time series, text, audio) through recurrent connections, facilitating memory retention of past inputs.
This structure allows RNNs to capture temporal dependencies, distinguishing them from traditional feedforward neural networks.

ReLU Function Overview

ReLU stands for "Rectified Linear Unit" and serves as a widely-used nonlinear activation function in neural networks.
Function definition: ( \text{ReLU}(z) = \max(0, z) ) outputs 0 for negative inputs and returns the input value for positive inputs.

Characteristics of the ReLU Function

Negative inputs always yield an output of 0.
Positive inputs result in the same value as the input, resembling a linear function when combined.
Compared to other activation functions, like sigmoid, which changes significantly only near zero, or linear functions, which provide identical outputs to inputs.

Nonlinear Function Modeling

The ReLU function allows for the combination of various nonlinear functions.
Complex nonlinear functions can be created through combinations of multiple ReLU nodes.
Retains linearity in certain segments while exhibiting a piecewise linear form.

Neural Network Practice

A simple neural network model consists of one hidden layer with three ReLU nodes and one linear output node.
The final output is derived by summing the outputs from the ReLU nodes.

Interactive Experimentation

Users can define a target output function and adjust the weights and biases of each ReLU node to fit the desired target shape.
By fine-tuning appropriate weights (W) and biases (B), users can shape complex output functions.

Summary and Insights

The ReLU function plays a vital role in introducing nonlinearity into neural networks.
Understanding specific nonlinear functions provides greater flexibility in designing neural network models.
Utilizing a combination of activation functions is essential for creating a variety of nonlinear shapes.

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Questions and Answers

랜덤 포레스트의 경우 각각의 개별 트리가 서로 동일하지 않도록 어떻게 구성합니까?

LSTM의 주요 구성 요소는 무엇입니까?

GRU는 LSTM의 복잡한 구조를 포함하고 있다.

LSTM 모델의 cell state는 어떤 역할을 합니까?

LSTM과 GRU 모두 _____ 문제를 해결하기 위해 RNN 기반 모델입니다.

다음의 RNN 모델과 그 장단점을 연결하세요:

기계 학습 분류기를 사용하여 데이터 과학자는 정확도 몇 %를 얻었나요?

역사적 결함 비율은 얼마인가요?

모델을 배포한 후 결함이 있는 제품을 탐지하는 데 개선이 있었습니까?

다수 클래스는 ________ 클래스입니다.

소수 클래스는 ________ 클래스입니다.

SVM은 무엇을 찾는 데 주력합니까?

소프트 마진 SVM은 주어진 결정 경계를 교차할 때 어떤 것을 부여합니까?

SHAP의 기초는 무엇입니까?

SHAP의 목표는 무엇인가요?

ARMA 모델에서 p가 의미하는 것은 무엇인가요?

이상 탐지의 무 감독 ML 기반 해결책은 무엇인가요?

데이터 기반 의사결정의 정의는 무엇인가?

다음 중 데이터 분석의 프로세스에 포함되지 않는 것은 무엇인가?

스마트 제조는 고급 데이터 분석과 인공지능의 통합을 포함한다.

RUL(잔여 유용 수명) 추정에 대한 정의는 무엇인가?

결함 예측에서 MLP는 무엇의 약자입니까?

예측 분석의 한 가지 유형은 ______ 모델링이다.

데이터 불균형 문제의 예로 적절하지 않은 것은 무엇인가?

단일주기 이탈 감지기는 데이터를 항상 일관되게 분류합니다.

다양한 데이터를 조합하여 처리하는 기법은 ______ 머신러닝이다.

렐루 함수란 무엇인가요?

렐루 함수는 입력이 ______일 경우 0을 출력합니다.

렐루 함수는 모든 입력에 대해 선형성을 유지한다.

렐루 함수의 정의는 무엇인가요?

렐루 함수를 사용하면 어떤 결과를 얻을 수 있나요?

렐루 함수와 리니어 함수는 동일한 출력을 제공합니다.

간단한 뉴럴 네트워크 모델의 구성은 어떻게 되나요?

인터랙티브 실험에서 조절 가능한 요소는 무엇인가요?

렐루 함수가 신경망에서 어떤 역할을 하나요?

Study Notes

Decision Making in Manufacturing

Data-Driven Decision Making (DDDM)

Data Analytics Process

Smart Manufacturing

Remaining Useful Life (RUL) Estimation

Fault Prediction

Yield Prediction

Key Considerations in Manufacturing Data Analytics

Machine Learning Applications in Manufacturing

Anomaly Detection

Data Imbalance in Classification

Support Vector Machines (SVM)

Conclusion

Entropy-based Fuzzy Support Vector Machine (EFSVM)

Quadratic Optimization

Model Explainability

Time-Series Analysis

Autoregressive Moving Average (ARMA)

Recurrent Neural Network (RNN)

ReLU Function Overview

Characteristics of the ReLU Function

Nonlinear Function Modeling

Neural Network Practice

Interactive Experimentation

Summary and Insights

Studying That Suits You

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