Machine Learning 101

Machine Learning 은, 데이터를 통해 패턴을 학습하여 일부의 데이터만으로 예측하는 알고리즘의 집합

Kind of Machine Learning Decided by Data

• Supervised Learning
• Unsupervised Learning

• Reinforcement Learning

  

Machine Learning Pipe Line

Supervised Learning

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Regression

• Linear and Non-linear Regression
  • Partial Least Squares (PLS)
    • 작성 예정
• Gradient Descent
  • Learning Rate
  • LRS (Learning Rate Scheduler)
  • Optima (Local minima problem)
  • Deep Learning - Loss Funcition
• Bias and Variance Trade-off
  • Loss Funcition
• Over and Under-fitting
• Weight Regularization

Classification

• Logistic and Soft-max Regression
• Support Vector Machine (SVM)
• Dicison Tree
• Linear Discriminant Analysis (LDA, Supervised)

Ensemble (Complex, Super/Unsupervised)

• Bagging
• Boosting

Regression vs. Classification

Regression (회귀)

• Input (Feature) : Real number (실수형), Discrete value(범주형) etc..,
• Output (Predict) : Real number (실수형, 이산값)
• Model shape : normal function shape (eg. $y = w_{1}x + w_{0}$)

Classification (분류)

• Input (Feature) : Real number (실수형), Discrete value(범주형) etc..
• Output (Predict) : Discrete value (범주형)
• Essential Function for last node
  • Binary classification (이진 분류) : Sigmoid function
  • Multiple Classification (다중 분류) : Soft-max function

Unsupervised Learning

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Dimension Reduction (차원 축소; Data pre-processing)

Kind of Feature Extraction in DR

• Singular Value Decompostion (SVD)
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• t-SNE
• UMAP

Clustering

Parametric

• Gaussian Mixture Model

Non-Parametric

• K-Means
• Mean Shift
• DBSCAN

Hierarchial Clustering

Parameter vs. Hyper-parameter

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Parameter (weight)

• learnable parameter within model
• ex) $w_{0}, w_{1}, … w_{D}$

Weight Regularization (L1 / L2 Regul’n)

Weight Regularization

• L1 Lasso Regularization, L2 Ridge Regularization, Elastic Net

Hyper-parameter

• adjust by administer
• ex) Learning rate, Disposition size(배치 크기)

Data structure

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• Feature (attribute) : information = X
• Label : results = y (predict : y_hat)

Data for Machine Learning

All Relative Documents

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Linear and Non-linear Regression

Gradient Descent

Learning Rate

LRS (Learning Rate Scheduler)

Optima (Local minima problem)

Bias and Variance Trade-off

Logistic and Soft-max Regression

Support Vector Machine (SVM)

Dicison Tree

Linear Discriminant Analysis (LDA)

Ensemble (Complex)

Bagging

Boosting

Dimension Reduction

Singular Value Decompostion (SVD)

Principal Component Analysis (PCA)

Linear Discriminant Analysis (LDA)

t-SNE

UMAP

Clustering

Gaussian Mixture Model

K-Means

Mean Shift

DBSCAN

Hierarchial Clustering

Weight Regularization

Over and Under-fitting

Data for Machine Learning