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ML Basic

Concepts

Correlation Analysis (상관 분석)
Clustering Algorithm (군집화 알고리즘)
Curve Fitting (곡선 적합)
Decision Tree (의사결정나무)
Ensemble Learning (앙상블 학습)
Evaluation Metrics for Classification (평가 지표: 분류)
Evaluation Metrics for Regression (평가 지표: 회귀)
Least Square Method (최소제곱법)
Multi-collinearity (다중공선성)
Probability Distribution (확률분포)
R-squared (결정계수)
Random Forest (랜덤포레스트)
Time Series Similarity (시계열 유사도)
Simulated Annealing (시뮬레이티드 어닐링)
VIF (Variance Inflation Factor, 분산팽창인자)

Concepts (to be supplemented)

Hyperparameter Tuning (초매개변수 튜닝)
Isolation Forest
Matplotlib Tutorial
Visualization Methods
XAI

References

https://www.quora.com/What-are-the-best-books-about-data-science?q=which book data science
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패턴 인식과 머신러닝
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Data Science Course, Codecademy (link)
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R. J. Hyndman, G. Athanasopoulos, Forecasting: Principles and Practice, Otexts (link)
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시계열 분석 관련 추천 도서(무료 온라인 북): 강의 교재로 검토
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Time Series Data Mining Using the Matrix Profile: A Unifying View of Motif Discovery, Anomaly Detection, Segmentation, Classification, Clustering and Similarity Joins
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행렬 프로파일 기반의 모티프 분석, 시계열 분할 등의 분석 응용을 소개
slides_Matrix_Profile_Tutorial_Part1.pdf
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