Wise set of machine learning resources abou A Blog From Human-engineer-being

Note : I regularly update this list.

Machine Learning 101:

I. Introduction to Machine Learning

II. Linear Regression

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)

VII) Regularization

overview using advanced math

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

VIII and IX) Neural Networks

– backpropagation

XI) Machine Learning System Design

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

Precision, recall, accuracy, …

XII) Support Vector Machines

XIII) Clustering

XIV) Dimensionality Reduction

XV) 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

XVI) Recommender Systems

– 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

– 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

XVIII) Reinforcement Learning

Machine Learning 201:

Online Lectures:

Deep Learning:

Deep Learning – Very wide grasp resource about everything
Juergen Schmidhuber’s home page – Different perspectives of NNs with theoretical view as well
Lecun’s Lecture Materials
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
Neural Networks by Hugo Larochelle
Practical Deep Learning talk by Adam Gibson for DeepLearning4J
Deep Learning talk by Andrew Ng.
Very Good Introductory Material for the Basics of Deep Learning with a example code.
Topic-wise Deep Learning Bibliography by memkite (new)
IPAM deep learning and feature learning summer school (new)

Sparse Coding:

Useful for Kaggle

Kaggle Ensemble Guide

Some good articles on working with the command line:

Jacobian Iteration for Singular Value Decomposition:

Mathematics, Statistical Theory and Probability Theory:

Methods of Optimization:

Theoretical Computer Science:

Random but Important Things:

A Little Stats Cheat Sheet. Pretty basic stuff but it is a nice quick reference.
Onto Data Scaling and Standardization (new)
A great list of Machine Learning Tools (new)
A Cheat Sheet for Distance Measures
Proof wiki list of symbols with LaTex code!!
Large set of free ML related e-books (new)

R:

Python:

Fortran:

Miscellaneous Links:

Awesome Machine Learning – includes large set of ML related tools divided into programming languages.
Top 100 Video ML Lectures Talk

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

Machine Learns

A Large set of Machine Learning Resources for Beginners to Mavens

Note : I regularly update this list.

Machine Learning 101:

I. Introduction to Machine Learning

II. Linear Regression

III) Linear Algebra

V) Linear Regression with Multiple Variables
– Gradient Descent

IV) Octave Tutorial

VI) Logistic Regression (LR)

VII) Regularization

VIII and IX) Neural Networks

XI) Machine Learning System Design

XII) Support Vector Machines

XIII) Clustering

XIV) Dimensionality Reduction

XV) Anomaly Detection

XVI) Recommender Systems

XVII) Large Scale Machine Learning

XVIII) Reinforcement Learning

Machine Learning 201:

Deep Learning:

Sparse Coding:

Useful for Kaggle

Some good articles on working with the command line:

Jacobian Iteration for Singular Value Decomposition:

Mathematics, Statistical Theory and Probability Theory:

Methods of Optimization:

Theoretical Computer Science:

Random but Important Things:

R:

Python:

Fortran:

Miscellaneous Links:

A Large set of Machine Learning Resources for Beginners to Mavens

Note : I regularly update this list.

Machine Learning 101:

I. Introduction to Machine Learning

II. Linear Regression

III) Linear Algebra

V) Linear Regression with Multiple Variables – Gradient Descent

IV) Octave Tutorial

VI) Logistic Regression (LR)

VII) Regularization

VIII and IX) Neural Networks

XI) Machine Learning System Design

XII) Support Vector Machines

XIII) Clustering

XIV) Dimensionality Reduction

XV) Anomaly Detection

XVI) Recommender Systems

XVII) Large Scale Machine Learning

XVIII) Reinforcement Learning

Machine Learning 201:

Deep Learning:

Sparse Coding:

Useful for Kaggle

Some good articles on working with the command line:

Jacobian Iteration for Singular Value Decomposition:

Mathematics, Statistical Theory and Probability Theory:

Methods of Optimization:

Theoretical Computer Science:

Random but Important Things:

R:

Python:

Fortran:

Miscellaneous Links:

Related posts:

V) Linear Regression with Multiple Variables
– Gradient Descent