Big Data with R by 송태민 & SGLee
* 참고도서 : 이상구, 이재화, 김경원, [빅북총서005] 선형대수학, BigBook, 2014.
* 참고도서 : 최용석, [빅북총서008] R과 함께하는 통계학의 이해, BigBook, 2014.
Mathematics for BigData
Lesson 1 Introduction
■ 참고 동영상 https://youtu.be/EURJnLppzKc
■ 참고 자료
SKKU Math for Big Data, Lecture 1, Introduction, https://youtu.be/EURJnLppzKc
Math for Big Data, Lecture 2, LU Decomposition, https://youtu.be/bzhTnoN3atk
Math for Big Data, Lecture 3, Schur Decomposition, https://youtu.be/F2kZON0oS_w
Math for Big Data, Lecture 4, Power Method, https://youtu.be/n4KD4aq_jxw
Math for Big Data, Lecture 5, QR Decomposition, https://youtu.be/gQ7gxTx5f9k
Math for Big Data, Lecture 6, Google's PageRank algorithm, https://youtu.be/tp6B7s43jAI
Math for Big Data, Lecture 7, Singular Value Decomposition(SVD), https://youtu.be/AxL4Q83IdAA
Math for Big Data, Lecture 8, Least Square Solutions, https://youtu.be/GwHh5lh5wEs
Math for Big Data, Lecture 9, Polar Decomposition, NMF, https://youtu.be/FqkMP9lBtaE
Math for Big Data, Lecture 10, Finding JCF using Dot Diagram, https://youtu.be/1E3wXN1oZyc
Math for Big Data, Lecture 11, Generalized eigenvectors and Matrix Function, https://youtu.be/lK4_Kp6P_N4
Math for Big Data, Lecture 12, Principal Componant Analysis 1 (PCA), https://youtu.be/0IKbslNH7xk
Math for Big Data, Lecture 13, Principal Componant Analysis 2 (PCA), https://youtu.be/j8PAt_Al180
Math for Big Data, Review 1, Intro. Calculus, Team 4, https://youtu.be/qALN6OAwNUo
Math for Big Data, Review 2, Intro. Linear Algebra, Team 3, https://youtu.be/xrFqBe8Rhs4
Math for Big Data, Review 3, Intro. Statistics, Team 2, https://youtu.be/sOx74EntB0I
Math for Big Data, Review 4, Intro. Engineering Math, Team 1, https://youtu.be/LRHN5swQW4E
Math for Big Data, Midterm PBL, S. Sun, https://youtu.be/CSdciSMPm-8
Math for Big Data, Midterm PBL, Naguib, https://youtu.be/k9_Ie8bMAY0
Math for Big Data, Midterm PBL, KEAhn, https://youtu.be/xFJmI1_uynk
Math for Big Data, Midterm PBL, Choo, https://youtu.be/TlC78z_LErQ
Math for Big Data, Midterm PBL, Naeem, https://youtu.be/8xo5UOP1tu8
Math for Big Data, Midterm PBL, Lkhagva, https://youtu.be/pPtO1rNdLs0
Math for Big Data, Midterm PBL, Sudip, https://youtu.be/5md49_RG74Q
Math for Big Data, Midterm PBL, Jeongwon Pyo, https://youtu.be/u5zDWtmx9P0
Math for Big Data, Midterm PBL, ESJang, https://youtu.be/cHYvWBuBrFA
Math for Big Data, Lecture 14, Graph and Matrix, https://youtu.be/Z89XvKXIYeg
Math for Big Data, Lecture 15, Laplacian Matrix and Big Data, https://youtu.be/4VuaOFRGm1g
Math for Big Data, Lecture 16, Intro. Big Data for Machine Learning 1, https://youtu.be/P24A1fkpX-Y
Math for Big Data, Lecture 17, Intro. Big Data for Machine Learning 2, https://youtu.be/bY3nfAHc6Qk
Math for Big Data, Lecture 18, (Team 4) Intro. Data Mining, Ahn& Choo, https://youtu.be/Dq2G8ReeEcY
Math for Big Data, Lecture 19, (Team 1) Pattern Classification 1, Naguib & Naeem, https://youtu.be/ieOUI6pc18A
Math for Big Data, Lecture 20, (Team 1) Pattern Classification 2, Naguib & Naeem, https://youtu.be/9kxyu0e-nfQ
Math for Big Data, Lecture 21, (Team 2) Statistical Learning, https://youtu.be/5dQO2Z3PgPU
Math for Big Data, Lecture 22, (Team 3) Cluster Analysis, https://youtu.be/LPyFO8jFHD8
Math for Big Data, Lecture 23, (Team 3) Project Draft 1, https://youtu.be/TZJrU7S1Q0o
Math for Big Data, Lecture 24, (Team 3) Project Presentation, Spectral Cluster Analysis by Shaowei-Lkhagva, https://youtu.be/476HgeBM8AE
Math for Big Data, Lecture 25, (AV) Project Presentation, Restricted Boltzmann Machine Training of Perceptron for Clustering by Naguib-Naeem https://youtu.be/QLKIgUCVLIY
Math for Big Data, Lecture 25, (Team 1) Project Presentation, Restricted Boltzmann Machine Training of Perceptron for Clustering by Naguib-Naeem https://youtu.be/vZ613MEWin4
Math for Big Data, Lecture 26, (Team 2) Project Presentation,Hand Gesture Recognition with Convolutional Neural Network by Pyo-Sudip-Jang https://youtu.be/FK-ANqohVlo
Math for Big Data, Lecture 27, (Team 4)
Math for Big Data, Lecture 28, Final PBL Presentation by Sudip, https://youtu.be/cOwWZcVb1AU
************************* After Note (후기) ********
[논문] ‘R을 활용한 ‘대화형 통계학 입문 실습실’ 개발과 활용',
'Interactive Statistics Laboratory using R and Sage',
J. Korea Soc. Math. Ed. Ser. E: Communications of Mathematical Education, Vol. 29, No. 4, Nov. 2015. 573-588.
[Lab] R을 활용한 기초 통계학 실습실
초보 http://matrix.skku.ac.kr/2015-R-Statistics/R-Sage-Statistics-Lab-1.htm
입문 : http://matrix.skku.ac.kr/2015-R-Statistics/R-Sage-Statistics-Lab-2.htm >
크롬 브라우저에서 위의 주소를 클릭만 하면 통계 공식, 프로그램 언어 하나도 외울 필요 없답니다. 코드 타이핑도 필요 없고~~ 하다보면 언어도 익숙해 지고 ... ^ ^
(아래 실습실에서 언어를 Sage 대신 R 로 바꾸고 실행 하시면 됩니다)
R download : http://healthstat.snu.ac.kr/CRAN/
설치
Download R 3.2.4 for Windows (62 megabytes, 32/64 bit)
If you want to double-check that the package you have downloaded exactly matches the package distributed by R, you can compare themd5sum of the .exe to the true fingerprint. You will need a version of md5sum for windows: both graphical and command line versions are available.
Please see the R FAQ for general information about R and the R Windows FAQ for Windows-specific information.
http://matrix.skku.ac.kr/E-Math/R-Practice-all.txt
위는 아래를 실습한 결과입니다 (by SGLee)