== 예비 지식 ==
 * 확률과 통계
 * 선형대수학

== 분류(Classification) ==
 * [http://wiki.zeropage.org/wiki.php/Naive%20Bayesian%20Classifier Naive Bayesian Classifier]
 * [Artificial Neural Network]

== 강화학습(Reinforce Learning) ==
 * 강의
  * [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html UCL Course on RL]
   * [https://www.youtube.com/watch?v=2pWv7GOvuf0 Youtube]
 * 책
  * [http://incompleteideas.net/sutton/book/bookdraft2017june19.pdf Reinforcement Learning: An Introduction]
  * [https://dnddnjs.gitbooks.io/rl/content Fundamental of Reinforcement Learning]
 * Slide
  * [http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf Deep RL Tutorial - David Silver]
  * [https://www.slideshare.net/carpedm20/ss-63116251 텐서플로우 설치도 했고 튜토리얼도 봤고 기초 예제도 짜봤다면 TensorFlow KR Meetup 2016]
 * Articles
  * [http://ishuca.tistory.com/391 Simple Reinforcement Learning with Tensorflow 한국어 번역]
 * Resource
  * [https://gym.openai.com OpenAI Gym] : 실습 가능한 환경을 제공
 * Code 
  * [https://github.com/golbin/TensorFlow-Tutorials/tree/master/10%20-%20DQN tensorflow tutorial]
== 링크들 ==
 * http://wiki.zeropage.org/wiki.php/MachineLearning%EC%8A%A4%ED%84%B0%EB%94%94
 * http://www.reddit.com/r/MachineLearning/comments/20i0vi/meta_collection_of_links_for_beginners_faq
 * http://peekaboo-vision.blogspot.kr/2013/01/machine-learning-cheat-sheet-for-scikit.html
 * [https://github.com/NVIDIA/DIGITS Deep Learning GPU Training System]

== Links ==
 * https://www.coursera.org/course/neuralnets