Some of useful machine learning resources from beginner to intermediate.
Some of useful machine learning resources from beginner to intermediate.
For updated list keep follow my BLOG A Blog From a Human-engineer-being - Eren Golge's Blog
Please suggest links to add !!
Machine Learning 101:
I. Introduction to Machine Learning
http://homepages.inf.ed.ac.uk/rb...
Machine Learning — Introduction
Omid's Machine Learning tutorial
Machine Learning Course - CS 156 (cal tech class)
II. Linear Regression
Linear regression
III) Linear Algebra
Syllabus | Linear Algebra | Mathematics | MIT OpenCourseWare
Linear algebra
online text
Page on smcvt.edu
- see Free Linear Algebra textbook for usage rights
V) Linear Regression with Multiple Variables
- Gradient Descent
Gradient descent
(discusses above wiki article)
- Optimization
EE364a: Lecture Videos
IV) Octave Tutorial
Octave Programming Tutorial
VI) Logistic Regression (LR)
Logistic regression
Logistic Regression Tutorial
Page on ucla.edu
(refers to LR as a classifier)
VII) Regularization
Regularization (mathematics)
Regularization
Page on di.ens.fr
overview using advanced math
Page on univie.ac.at
VIII and IX) Neural Networks
- backpropagation
The Backpropagation Algorithm
Manfred Zabarauskas' Blog
XI) Machine Learning System Design
Page on pitt.edu
Precision, recall, accuracy, …
Precision and recall
Accuracy and precision
How to choose an error metric when evaluating a classifier?
Page on cornell.edu
XII) Support Vector Machines
Page on ucf.edu
Page on columbia.edu
ma
Page on mit.edu
XIII) Clustering
Cluster analysis
k-means clustering
XIV) Dimensionality Reduction
Dimensionality reduction
Page on tamu.edu
Page on math.uwaterloo.ca
XV) Anomaly Detection
Page on siam.org
Anomaly detection
- Google Analytics Google Analytics
- anomaly detection with Google Analytics (example)
Must purchase this article (I did not purchase but appears to be good) Existing solutions and latest technological trends
- Gaussian distribution
(no math)
Normal distribution
Normal Distribution
Multivariate normal distribution
XVI) Recommender Systems
Machine Learning for Large Scale Recommender Systems
Page on iiia.csic.es
2. Getting started: an introduction to recommender systems with Crab (using Python)
- Collaborative Filtering
www.cs.cmu.edu/~wcohen/collab-filtering-tutorial.ppt
XVII) Large Scale Machine Learning
Page on stanford.edu
Large-Scale Machine Learning (introduction to class)
(lectures) http://www.sanjivk.com/EECS6898/...
Introduction | TechTalks.tv
- stochastic gradient descent
Stochastic gradient descent
(visualization)
Stochastic Gradient Descent (Abu-Mostafa)
Page on mit.edu
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-832-underactuated-robotics-…
- parallelized stochastic gradient descent
Page on rutgers.edu
- recursive partitioning:
Page on r-project.org
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
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
Matlab code for solving L1-regularization problems
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
