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Dm ml study_roadmap

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Data Mining & Machine Learning Study Loadmap

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Dm ml study_roadmap

  1. 1. Study Roadmap & Resources for Data Mining & Machine Learning Ver. 0.12 Pilsung Kang Seoul National University of Science andTechnology pskang at seoultech dot ac dot kr 2014. 12. 22
  2. 2. Study Roadmap 부제: 무슨 공부를 어디서부터 시작해야 하나? 숨은 제목: 책들의 난이도를 분류해보자
  3. 3. Prerequisite Beginner Intermediate Advanced (for MS) Advanced (for Ph.D) DM & ML Theoretical Foundations Statistical Learning R Python
  4. 4. Resources: Books 부제: 책들의 정보를 알아보자 숨은 제목: 번역본은 있나? PDF로 다운은? 강좌자료는?
  5. 5. Books: Prerequisite • 원서: Introduction to Mathematical Statistics (7th Ed.), R.V. Hogg, J. Mckean, A.T. Craig, Pearson, 2012. • 번역본: 수리통계학 개론, 박태영 옮김, 경문사, 2012. • 원서: R Cookbook, P. Teetor, O’Reilly Cookbooks, 2011. • 번역본: R Cookbook, 이제원 옮김, 인사이트, 2012. • 원서: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython, W. McKinney, O’Reilly Media, 2012. • 번역본: 파이썬 라이브러리를 활용한 데이터 분석, 김영근 옮김, 한빛미디어, 2013.
  6. 6. Books: Beginner • 원서: Data Mining for Business Intelligence: Concepts, Techniques, and Applications in MS Office Excel with XLMiner (2nd Ed.), G. Shmueli, N.R. Patel, P.C. Bruce, Wiley, 2010. • Book website: http://www.dataminingbook.com/ • 번역본: 비즈니스 인텔리전스를 위한 데이터마이닝, 조재희 외 옮김, 이앤비플러스, 2012. • 원서: An Introduction to Statistical Learning with Applications in R, G. James, D. witten, T. Hastie, R. Tibshirani • Book website: http://www-bcf.usc.edu/~gareth/ISL/ (can download the book PDF) • Lecture videos: http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/ • 국내도서: R을 이용한 데이터마이닝, 박창이 외 지음, 교우사, 2011. • 원서: Building Machine Learning Systems with Python, W. Richert, L.P. Coelho, Packt Publishing, 2013.
  7. 7. Books: Intermediate • 원서: Data Mining: Concepts and Techniques (3rd Ed.), J. Han, M. Kamber, J. Pei, Morgan Kaufmann, 2011. • Book website: http://web.engr.illinois.edu/~hanj/bk3/bk3_slidesindex.htm • 번역본: 데이터마이닝 기념과 기법 (2판): 강창완 외 옮김, Morgan Kaufmann, 2007. • 원서: Introduction to Data Mining, P.-N. Tan, M. Steinbach, V. Kumar, Addison-Wesley, 2005. • Book website: http://www-users.cs.umn.edu/~kumar/dmbook/index.php • 번역본: 데이터마이닝: 용환승 외 옮김, 인피니티북스, 2007. • 원서: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd Ed.), Springer, 2011. • Book website: http://statweb.stanford.edu/~tibs/ElemStatLearn/ (can download book PDF) • 원서: Machine Learning with R, Brett Lantz, Packt Publishing, 2013. • 원서: Machine Learning in Action, P. Harrington, Manning Publications, 2012. • 번역본: 머신러닝 인 액션: 기계 학습 알고리즘으로 데이터 마이닝하기, 김영진 옮김, 제이펍, 2013.
  8. 8. Books: Advanced (for MS Students) • 원서: Pattern Classification (2nd Ed.), R.O. Duda, P.E. Hart, D.G. Stork, Wiley, 2001. • Book pdf: http://cns-classes.bu.edu/cn550/Readings/duda-etal-00.pdf • 번역본: 패턴인식 (2판), 유현중 옮김, 아이티씨, 2006. • 원서: Bayesian Artificial Intelligence (2nd Ed.), K.B. Korb, A.E. Nicholson, CRC Press, 2010. • Book website: http://www.csse.monash.edu.au/bai/book/about.php
  9. 9. Books: Advanced (for Ph.D Students) • 원서: Pattern Recognition and Machine Learning, C.M. Bishop, Springer 2007. • Book website: http://research.microsoft.com/en-us/um/people/cmbishop/PRML/ • 원서: Machine Learning: A Probabilistic Perspective, K.P. Murphy, MIT Press, 2012. • Book website: http://www.cs.ubc.ca/~murphyk/MLbook/ • 원서: Probabilistic Graphical Models: Principles and Techniques, D. Koller, N. Friedman, MIT Press, 2009 • Book website: http://pgm.stanford.edu/ • Coursera Lecture: https://www.coursera.org/course/pgm • 원서: Building Probabilistic Graphical Models with Python, K.R. Karkera, Packt Publishing, 2014. • Book website: http://statweb.stanford.edu/~tibs/ElemStatLearn/ (can download book PDF)
  10. 10. Resources: R & Python 부제: 이론을 알았다면 활용을 해보자 숨은 제목: 키보드보다 마우스가 편한 사람들도 할수있다
  11. 11. Interactive Learning Sites for R 1. Datacamp: www.datacamp.com 2. Code school: https://www.codeschool.com/courses/try-r 3. swrl: http://www.swirlstats.com/ Other websites:  http://www.cyclismo.org/tutorial/R/index.html: R 기초 문법에 충실  http://www.rdatamining.com/ Data Mining 방법론 예제 위주  http://caret.r-forge.r-project.org/ 기본적인 DM/ML 알고리즘 대부분을 제공  http://www.r-tutor.com/ R을 이용한 GPU computing 자료가 유용
  12. 12. Interactive Learning Sites for Python 1. Codecademy: http://www.codecademy.com/learn 2. Bento: https://www.bento.io/ 3. Online Python Tutor http://www.pythontutor.com/ Other websites:  https://developers.google.com/edu/python/ Google에서 제공하는 Python 기초 강좌  http://doc.pyschools.com/html/index.html Python quick reference guide  http://www.pythonforbeginners.com/basics/python-websites-tutorials 다양한 tutorial 링크 제공  http://scikit-learn.org/stable/ 기본적인 DM/ML 알고리즘 대부분을 제공
  13. 13. Resources: Online Learning 부제: 책만 봐선 이해가 안되는 사람을 위하여 숨은 제목: 근데 한국어 강좌는 별로 없다
  14. 14. Online Learning Sites 1. Coursera: https://www.coursera.org/ 지금 필요한 건 (1) 시간 (2) Listening 능력 2. edX: https://www.edx.org/
  15. 15. Online Learning Sites 3. iTunes University: iPhone/iPad/Mac 이 없다면 PC로도 가능함 4. Youtube: 필요한 주제에 맞는 강좌를 골라듣자. Example: Hugo Larochelle의 Neural Network 강좌 Website: http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html Hugo Larochelle • Assistant Professor at CS of Universite de Sherbrooke • Ph.D Supervisor: Yoshua Bengio • Post-Doc Supervisor: Geoffrey Hinton • Deep learning에 대해 내공이 상당하겠는데??
  16. 16. Online Learning Sites 5. Metacademy: http://www.metacademy.org/roadmaps/

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