Machine Learning Resources
- Tutorials
- Competitions
- Boosting
- Gradient Boosting
- Bootstrap
- Cascades
- Classifiers
- Convex Optimization
- Decision Tree
- Generative Models
- Markov Networks
- Markov Chains
- Matrix Computations
- Matrix Factorization
- Gaussian Processes
- Multilabel Learning
- Multi-Task Learning
- Nearest Neighbors
- Online Learning
- Visualization
- Tricks
- Deep What?
- Mathematics
- L-BFGS
- Code Stylometry
- Recommendation System
- Papers
- Resources
- Books
- Videos
- blogs
- Machine Learning Library
- Readings and Questions
Tutorials
Machine Learning for Developers
http://xyclade.github.io/MachineLearning/
Logistic Regression Vs Decision Trees Vs SVM
- Part I: http://www.edvancer.in/logistic-regression-vs-decision-trees-vs-svm-part1/
- Part II: http://www.edvancer.in/logistic-regression-vs-decision-trees-vs-svm-part2/
Machine learning: A practical introduction
- blog: http://www.infoworld.com/article/3010401/big-data/machine-learning-a-practical-introduction.html
Tutorials on Machine Learning (Tom Dietterich)
http://web.engr.oregonstate.edu/~tgd/projects/tutorials.html
Machine Learning Tutorials
- intro: “This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list.”
- github: https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md
A Visual Introduction to Machine Learning
Machine Learning – A gentle & structured introduction
- blog: http://blog.cambridgecoding.com/2016/02/14/machine-learning-a-gentle-structured-introduction/
- slides: http://pan.baidu.com/s/1hqVGAl2
A Comparison of Supervised Learning Algorithm
Competitions
Machine learning best practices we’ve learned from hundreds of competitions (Kaggle: Ben Hamner)
- youtube: https://www.youtube.com/watch?v=9Zag7uhjdYo&hd=1
- baidu-pan: http://pan.baidu.com/s/1pJLrICN
Boosting
“Quick Introduction to Boosting Algorithms in Machine Learning”
http://www.analyticsvidhya.com/blog/2015/11/quick-introduction-boosting-algorithms-machine-learning/
An Empirical Comparison of Three Boosting Algorithms on Real Data Sets with Artificial Class Noise(AdaBoost vs. LogitBoost vs. BrownBoost)
A (small) introduction to Boosting
Gradient Boosting
Complete Guide to Parameter Tuning in Gradient Boosting (GBM) in Python
Awesome XGBoost
- intro: This page contains a curated list of examples, tutorials, blogs about XGBoost usecases.
- github: https://github.com/dmlc/xgboost/blob/master/demo/README.md
XGBoost: A Scalable Tree Boosting System
Bootstrap
Coding, Visualizing, and Animating Bootstrap Resampling
http://minimaxir.com/2015/09/bootstrap-resample/
Cascades
Making faces with Haar cascades and mixed integer linear programming
- blog: http://matthewearl.github.io/2016/01/14/inverse-haar/
- github: https://github.com/matthewearl/inversehaar
Classifiers
Measuring Performance of Classifiers
Convex Optimization
Convex Optimization: Algorithms and Complexity
- arXiv: http://arxiv.org/abs/1405.4980
- blog: https://blogs.princeton.edu/imabandit/2015/11/30/convex-optimization-algorithms-and-complexity/
Decision Tree
Soft Decision Trees
- paper: http://www.cmpe.boun.edu.tr/~ethem/files/papers/icpr2012_softtree.pdf
- project page: http://www.cs.cornell.edu/~oirsoy/softtree.html
- github: https://github.com/oir/soft-tree
Canonical Correlation Forests
Generative Models
A note on the evaluation of generative models
Markov Networks
Markov Logic Networks
Markov Chains
Evolution, Dynamical Systems and Markov Chains
http://www.offconvex.org/2016/03/07/evolution-markov-chains/
Markov Chains: Explained Visually
Matrix Computations
Randomized Numerical Linear Algebra for Large Scale Data Analysis
http://researcher.watson.ibm.com/researcher/view_group.php?id=5131
Sketching-based Matrix Computations for Machine Learning
http://xdata-skylark.github.io/libskylark/
Matrix Factorization
Neural Network Matrix Factorization
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
k-Means Clustering Is Matrix Factorization
Gaussian Processes
The Gaussian Processes Web Site
Multilabel Learning
Neural Network Models for Multilabel Learning
An Empirical Evaluation of Supervised Learning in High Dimensions
Multi-Task Learning
Multi-Task Learning: Theory, Algorithms, and Applications (2012)
Nearest Neighbors
Annoy: Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
- github: https://github.com/spotify/annoy
Online Learning
Lecture Notes on Online Learning
Scale-Free Online Learning
Visualization
Visualising High-Dimensional Data
- blog: http://blog.applied.ai/visualising-high-dimensional-data/
- ipn(“t-SNE Demo”): https://s3-eu-west-1.amazonaws.com/appliedai.static/tsnedemo/htmlrenders/01_EndToEnd_DataViz.html
Tricks
Machine Learning Trick of the Day
- (1): Replica Trick: http://blog.shakirm.com/2015/07/machine-learning-trick-of-the-day-1-replica-trick/
- (2): Gaussian Integral Trick: http://blog.shakirm.com/2015/08/machine-learning-trick-of-the-day-2-gaussian-integral-trick/
- (3): Hutchinson’s Trick: http://blog.shakirm.com/2015/09/machine-learning-trick-of-the-day-3-hutchinsons-trick/
- (4): Reparameterisation Tricks: http://blog.shakirm.com/2015/10/machine-learning-trick-of-the-day-4-reparameterisation-tricks/
- (5): Log Derivative Trick: http://blog.shakirm.com/2015/11/machine-learning-trick-of-the-day-5-log-derivative-trick/
Deep What?
Deep Support Vector Machines
- video: http://videolectures.net/roks2013_wiering_vector/
- slides: http://www.esat.kuleuven.be/sista/ROKS2013/files/presentations/DSVM_ROKS_2013_WIERING.pdf
Deep Boosting(ICML 2014)
- paper: http://www.cs.princeton.edu/~usyed/CortesMohriSyedICML2014.pdf
- github: https://github.com/google/deepboost
Deep Neural Decision Forests(ICCV 2015. Microsoft Research. ICCV’15 Marr Prize)
- paper: http://research.microsoft.com/pubs/255952/ICCV15_DeepNDF_main.pdf
- supplement: http://research.microsoft.com/pubs/255952/ICCV15_DeepNDF_suppl.pdf
- notes: http://pan.baidu.com/s/1jGRWem6
Deep Kernel Learning
Questions on Deep Gaussian Processes
Greedy Deep Dictionary Learning
Mathematics
Some Notes on Applied Mathematics for Machine
An extended collection of matrix derivative results for forward and reverse mode algorithmic differentiation
L-BFGS
Code Stylometry
De-anonymizing Programmers via Code Stylometry
- keywords: source code authorship, random forests
- paper: http://www.princeton.edu/~aylinc/papers/caliskan-islam_deanonymizing.pdf
Recommendation System
Top-N Recommendation with Novel Rank Approximation
- arxiv: http://arxiv.org/abs/1602.07783
- github: https://github.com/sckangz/SDM16
Papers
An embarrassingly simple approach to zero-shot learning
- paper: http://jmlr.org/proceedings/papers/v37/romera-paredes15.html
- github: https://github.com/MLWave/extremely-simple-one-shot-learning
Debugging Machine Learning Tasks
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
- arxiv: http://arxiv.org/abs/1602.04938
- homepage: http://homes.cs.washington.edu/~marcotcr/blog/lime/
- github: https://github.com/marcotcr/lime
Resources
Machine Learning Surveys: A list of literature surveys, reviews, and tutorials on Machine Learning and related topics
machine learning classifier gallery
http://home.comcast.net/~tom.fawcett/public_html/ML-gallery/pages/
Machine Learning and Computer Vision Resources
http://zhengrui.github.io/zerryland/ML-CV-Resource.html
A Huge List of Machine Learning And Statistics Repositories
http://blog.josephmisiti.com/a-huge-list-of-machine-learning-repositories/
机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 1)
https://github.com/ty4z2008/Qix/blob/master/dl.md
The Spectator: Shakir’s Machine Learning Blog
Machine Learning Tutorials
https://github.com/ujjwalkarn/Machine-Learning-Tutorials/blob/master/README.md
Useful Inequalities
http://www.lkozma.net/inequalities_cheat_sheet/ineq.pdf
Math for Machine Learning
http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf
Cheat Sheet: Algorithms for Supervised- and Unsupervised Learning
Annalyzin: Analytics For Layman, with Tutorials & Experiments
https://annalyzin.wordpress.com/
Books
Machine Learning plus Intelligent Optimization: THE LION WAY, VERSION 2.0
- book: http://intelligent-optimization.org/LIONbook/
- slides: http://intelligent-optimization.org/LIONbook/LIONway-slides-chapter3.pdf
Level-Up Your Machine Learning
https://www.metacademy.org/roadmaps/cjrd/level-up-your-ml
An Introduction to the Science of Statistics: From Theory to Implementation (Preliminary Edition)
Videos
Video resources for machine learning
http://dustintran.com/blog/video-resources-for-machine-learning/
blogs
10 More lessons learned from building real-life Machine Learning systems — Part I
Machine Learning: classifier comparison using Plotly
Machine Learning Library
LambdaNet: Purely functional artificial neural network library implemented in Haskell
-github: https://github.com/jbarrow/LambdaNet
rustlearn: Machine learning crate for Rust
MILJS : Brand New JavaScript Libraries for Matrix Calculation and Machine Learning
- arXiv: http://arxiv.org/abs/1503.05743v1
- github: https://github.com/mil-tokyo
- homepage: http://mil-tokyo.github.io/
machineJS: Automated machine learning- just give it a data file!
Machine Learning for iOS: Tools and resources to create really smart iOS applications
Knet: a machine learning module implemented in Julia
DynaML: Scala Library/REPL for Machine Learning Research
- homepage: http://mandar2812.github.io/DynaML/
- github: https://github.com/mandar2812/DynaML/
Readings and Questions
(Quora): What are the top 10 data mining or machine learning algorithms?
(Quora): What are the must read papers on data mining and machine learning?
https://www.quora.com/What-are-the-must-read-papers-on-data-mining-and-machine-learning
(Quora): What would be your advice to a software engineer who wants to learn machine learning?
