기계 학습, Machine Learning 강좌 – 문일철 교수님

강의소스 강좌영상 Week 01. Introduction—————————————— 1. Motivation 2. MLE (Maximum likelihood Estimation) 3. MAP(Maximum a Posterior Estimation) – Bayes 4. Probability & Distribution Supervised Learning  Week 02. Fundamentals of ML————————————- 1. Rule-Based ML 2. Decision Tree      – Entropy & Information Gain        noise & inconsistencies 3. Read more…

ISLR :: 소개 및 강좌

Course:  https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about                  https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ Book:    www.StatLearning.com   http://www-bcf.usc.edu/~gareth/ISL/              http://www.springer.com/series/417 번역본: 가볍게 시작하는 통계학습   (컬러가 아닌게 함정) 참고:  http://www.alsharif.info/iom530   An Introduction to Statistical Learning, with Applications in R” (James, Witten, Hastie, Tibshirani – Springer 2013 W1: Introduction Read more…