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176 видео
(Docceptor 머신러닝 1-1) 본 강의에서 다룰 머신러닝의 간단 소개
(Docceptor Machine Learning 1-2) Machine Learning Types and Applications Following the Introduction
(Docceptor 머신러닝 2-1) 머신러닝에 필요한 확률 기초 - 랜덤 변수, 확률 분포, 조건부 확률, Bayes rule
(Docceptor Machine Learning 2-2) Probability Basics Required for Machine Learning - Conditional P...
(Docceptor Machine Learning 3) Maximum a posteriori vs. Maximum likelihood estimation basics
(Docceptor Machine Learning 4-1) Linear Regression Basics
(Docceptor 머신러닝 4-2) Linear regression에 확률 적용
(Docceptor 머신러닝 4-3) 머신러닝을 위한 Python setup 및 Linear regression 실습
(Docceptor Machine Learning 5-1) Simple Logistic Regression (MLE, Cost Function) and Python Practice
(Docceptor 머신러닝 5-2) Logistic regression의 고차원 데이터 및 multi-class 처리
(Docceptor 머신러닝 6) k-NN classification 및 python practice
(Docceptor 머신러닝 7-1) k-Means Clustering 및 EM algorithm 기초
Digital System and Binary numbers
