A Large set of Machine Learning Resources for Beginners to Mavens

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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

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

Credits goes to

Resources

I added some of my places to that list as well.

 

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