AI Engineer로서 알아야 할 기본적인 DS지식들을 스스로 간단히(직관적으로) 리뷰하는 공간입니다.

ML Basic

Metric(분류, 회귀)

데이터 스케일링(정규화)

Local Minima와 Global Minima

차원의 저주

LDA(Latent Dirichlet allocation)

SVM(support vector machine)

나이브 베이즈(naive bayes)

차원 축소 기법

Markov Chain

경사하강법

Optimizer

Norm(L1 & L2) & Regularization

Batch Norm

선형회귀(&회귀 분석의 4가지 가정)

로지스틱 회귀

앙상블 기법

Decision Tree

Random Forest

AdaBoost

Gradient Boosting

Embedding

CNN

RNN

LSTM & GRU

Transformer

RecSys

Association Rule

Content-based Recommendation

Collaborative Filtering

유사도 측정

Latent factor model

Matrix Factorization

Matrix Factorization for Implicit Feedback

Bayesian Personalized Ranking

Item2Vec

ANN

Neural Collaborative Filtering

GNN

Context-aware Recommendation

Multi-Armed Bandit

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Reference

• https://boostdevs.gitbook.io/ai-tech-interview/
• https://github.com/SeongBeomLEE/Machine-Learning-Engineer-Interview
• https://brunch.co.kr/@gimmesilver#articles
• https://wooono.tistory.com/m/category/AI/Machine Learning
• https://angeloyeo.github.io/2019/07/27/PCA.html
• https://www.youtube.com/c/혁펜하임