适合初学者与高手的大量机器学习资源集合

Machine Learning 101:

I. Introduction to Machine Learning

• http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm
• http://jeremykun.com/2012/08/04/machine-learning-introduction/
• http://www.omidrouhani.com/research/machinelearning/html/machinelearning.htm
• http://www.youtube.com/playlist?list=PLD63A284B7615313A (cal tech class)

II.  Linear Regression

• http://en.wikipedia.org/wiki/Linear_regression
• http://www.youtube.com/watch?v=ExVhaN36jBs
• http://en.wikipedia.org/wiki/Simple_linear_regression
• http://www.youtube.com/watch?v=ocGEhiLwDVc

 

 

III) Linear Algebra

• http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/Syllabus/
• https://www.khanacademy.org/math/linear-algebra
• online text
• http://joshua.smcvt.edu/linearalgebra/book.pdf
• - see http://joshua.smcvt.edu/linearalgebra/ for usage rights

V) Linear Regression with Multiple Variables
- Gradient Descent

• http://en.wikipedia.org/wiki/Gradient_descent
• http://www.youtube.com/watch?v=umAeJ7LMCfU (discusses above wiki article)
• http://www.youtube.com/watch?v=Dgn1ssi2p40

- Optimization

• http://www.stanford.edu/class/ee364a/videos/video01.html

 

IV) Octave Tutorial

• http://en.wikibooks.org/wiki/Octave_Programming_Tutorial

 

VI) Logistic Regression (LR)

• http://en.wikipedia.org/wiki/Logistic_regression
• http://alias-i.com/lingpipe/demos/tutorial/logistic-regression/read-me.html
• http://www.ats.ucla.edu/stat/sas/library/logistic.pdf
• http://www.youtube.com/watch?v=-Z2a_mzl9LM&feature=c4-overview&playnext=1&list=TLIxwITi7ngG0 (refers to LR as a classifier)

VII) Regularization

• http://en.wikipedia.org/wiki/Regularization_(mathematics)
• http://solon.cma.univie.ac.at/regul.html
• http://www.di.ens.fr/~fbach/ecml2010tutorial/ecml_tutorial_part1.pdf

overview using advanced math

• http://solon.cma.univie.ac.at/ms/regtutorial.pdf

 

VIII and IX) Neural Networks

• http://www.youtube.com/watch?v=KuPai0ogiHk
• http://www.youtube.com/watch?v=Ih5Mr93E-2c&list=PLD63A284B7615313A&index=10

- backpropagation

• http://www.youtube.com/watch?v=aVId8KMsdUU
• http://www.speech.sri.com/people/anand/771/html/node37.html
• http://blog.zabarauskas.com/backpropagation-tutorial/

 

XI) Machine Learning System Design

• http://people.cs.pitt.edu/~milos/courses/cs2750-Spring03/lectures/class2.pdf

 

Precision, recall, accuracy, …

• http://en.wikipedia.org/wiki/Precision_and_recall
• https://en.wikipedia.org/wiki/Accuracy_and_precision
• http://stats.stackexchange.com/questions/34193/how-to-choose-an-error-metric-when-evaluating-a-class…
• http://www.cs.cornell.edu/courses/cs578/2003fa/performance_measures.pdf

 

XII) Support Vector Machines

• http://www.cs.ucf.edu/courses/cap6412/fall2009/papers/Berwick2003.pdf
• http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
• http://www.youtube.com/watch?v=eHsErlPJWUU
• http://web.mit.edu/zoya/www/SVM.pdf

 

XIII) Clustering

• http://en.wikipedia.org/wiki/Cluster_analysis
• http://en.wikipedia.org/wiki/K-means_clustering
• http://www.youtube.com/watch?v=0MQEt10e4NM&feature=c4-overview&playnext=1&list=TLT3EED0Azl4Y

 

XIV) Dimensionality Reduction

• http://en.wikipedia.org/wiki/Dimensionality_reduction
• http://research.cs.tamu.edu/prism/lectures/iss/iss_l10.pdf
• http://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf
• http://www.youtube.com/watch?v=EHIZ7Pk1XVY
• http://www.youtube.com/watch?v=mz618Tesra4

 

XV) Anomaly Detection

• www.siam.org/meetings/sdm08/TS2.ppt
• http://en.wikipedia.org/wiki/Anomaly_detection

 

- Google Analytics http://www.google.com/analytics/
- anomaly detection with Google Analytics (example)

• http://www.youtube.com/watch?v=PulNjqfToAo

 

Must purchase this article (I did not purchase but appears to be good) http://www.sciencedirect.com/science/article/pii/S138912860700062X

- Gaussian distribution

• http://www.youtube.com/watch?v=4uiJoYVPmMw (no math)
• https://en.wikipedia.org/wiki/Normal_distribution
• http://www.r-tutor.com/elementary-statistics/probability-distributions/normal-distribution
• https://en.wikipedia.org/wiki/Multivariate_normal_distribution

 

XVI) Recommender Systems

• http://pages.cs.wisc.edu/~beechung/icml11-tutorial/
• http://ijcai-11.iiia.csic.es/files/proceedings/Tutorial%20IJCAI%202011%20Gesamt.pdf
• http://muricoca.github.io/crab/tutorial.html (using Python)

- Collaborative Filtering

• www.cs.cmu.edu/~wcohen/collab-filtering-tutorial.ppt

XVII) Large Scale Machine Learning

• http://i.stanford.edu/~ullman/pub/ch12.pdf
• http://www.sanjivk.com/EECS6898/ (introduction to class)
• (lectures) http://www.sanjivk.com/EECS6898/lectures.html
• http://techtalks.tv/talks/introduction-5/57923/

 

- stochastic gradient descent

• http://en.wikipedia.org/wiki/Stochastic_gradient_descent
• http://www.youtube.com/watch?v=HvLJUsEc6dw (visualization)
• http://work.caltech.edu/library/101.html
• http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-832-underactuated-robotics-…
  http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-832-underactuated-robotics-…

- parallelized stochastic gradient descent

• http://www.research.rutgers.edu/~lihong/pub/Zinkevich11Parallelized.pdf

 

- recursive partitioning:

• http://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf

 

Machine Learning 201:

• Advanced Machine Learning Course (CMU)
• Lecture 1: Machine Learning With Scikit-Learn
• Lecture 2: Machine Learning With Scikit-Learn
• Lecture 3: Machine Learning from the Boston Python User Group
• Andrew Ng’s Standford ML Class
• An Introduction to Machine Learning
• Andrew Ng’s Coursera Class Wiki
• Koller's PGM course on Coursera (requires solid prob. background)
• The Machine Learning Library
• JMLR
• CMU Google Slides
• NN Course

 

Deep Learning:

• Deep Learning - Very wide grasp resource about everything
• Juergen Schmidhuber's home page - Different perspectives of NNs with theoretical view as well
• Home Page of Geoffrey Hinton - And the Father of DL
• Neural Network FAQ, part 1 of 7: Introduction - General sense NN FAQ
• Page on lear.inrialpes.fr - INRIA Deep Learning Notes tutorial
• Page on nyu.edu:21991 - very detailed examples on real datasets
• Hinton's NN lectures at Coursera

 

Sparse Coding (new):

• ECCV10 tutorials
• CVPR10 tutorials

Some good articles on working with the command line:

• command line nuggets for data science (article focuses on unix but all will work in linux bash)
• intro to the command line
• 7 Command Line Tools for Data Scientists

 

Jacobian Iteration for Singular Value Decomposition:

• Basic Explanation
• Stream Algorithm for SVD

 

Fortran:

• Fortran for Beginners
• Fortran 77 Stanford Tutorial
• Professional Programmer’s Guide to Fortran 77
• BLAS
• Fortran 77 Intrinsic Functions

 

Mathematics, Statistical Theory and Probability Theory:

• Introduction to Probability
• Rice
• Chang Stochastic Processes
• Durrett Probability

 

Methods of Optimization:

• Gradient Descent
• Basic Steepest Decent
• Newton’s Method in Optimization
• CRAN Optimization and Mathematical Programming Task View
• MIT OCW Optimization Methods
• Boyd Optimization
• Boyd Solutions Manual
• Convex Optimization in R

 

Theoretical Computer Science:

• Foundations of Computer Science
• Complexity Theory a Modern Approach

 

Some Really Random Stuff:

• A Little Stats Cheat Sheet. Pretty basic stuff but it is a nice quick reference.
• Proof wiki list of symbols with LaTex code!!
• LaTex greeks, very useful.
• LaTeX fonts

 

R:

• R One pagers
• R Time Series
• R Statistical And Machine Learning Task View

 

Python:

• Pylearn2 Deeplearning Library
• IPython Notebooks on Various Topics