Difference between revisions of "Machine Learning"
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Revision as of 03:56, 1 January 2019
More specific information is included in each subfield.
Subfields and Concepts
See Category:Machine Learning for some of its subfields.
- Supervised Learning
- Discriminative Model Vs Generative Model
- Parametric Model Vs Nonparametric Model
- Structured Learning
- Generalized Linear Model (GLM or GLIM)
- Support Vector Machine
- Supervised Dimensionality Reduction
- Adaptive Basis Function Model
- Decision Tree Learning
- Classification and Regression Tree (CART)
- ID3 Algorithm
- Artificial Neural Network
- Feedforward Neural Network
- Recurrent Neural Network
- Radial Basis Function (RBF) Network
- Kohonen Network
- Supervised Ensemble Learning
- Bayesian Averaging
- Bayes Optimal Classifier
- Decision Forest / Random Forest
- Decision Tree Learning
- Supervised Dictionary Learning
- Supervised Deep Learning
- Deep Belief Network
- Unsupervised Learning
- Clustering / Discrete Latent Variable Models
- Unsupervised Dimensionality Reduction / Continuous Latent Variable Models
- Manifold Learning (although supervised variants exist)
- Unsupervised Ensemble Learning
- Unsupervised Dictionary Learning
- Unsupervised Deep Learning
- Deep Autoencoder
- Deep Belief Network
- Semi-supervised Learning
- Active Learning
- Inductive Learning
- Supervised Learning
- Semi-supervised Learning
- Analytical Learning
- Transfer Learning / Inductive Transfer
- Learning to Learn / Meta Learning
- Multi-task Learning
- Reinforcement Learning
- Multi-Armed Bandit
- Finite Markov Decision Process
- Temporal-Difference Learning
- Adaptive Dynamic Programming
- Deep Reinforcement Learning
- Probabilistic Machine Learning
- Bayesian Machine Learning
- Statistical Learning Theory and Computational Learning Theory
- Evolutionary Computation
- Genetic Algorithms
- Lazy Learning / Instance-based Learning
- k-Nearest Neighbors (k-NN) Algorithm
- Case-based Reasoning (CBR)
- Eager Learning
- Foundations of Machine Learning by David S. Rosenberg
- Advanced Machine Learning Specialization - Coursera
- Machine Learning by Andrew Ng - Coursera
- Machine Learning by Pedro Domingos - Coursera
- Neural Networks for Machine Learning by Geoffrey Hinton - Coursera
- Practical Machine Learning by Jeff Leek - Coursera
- NYU Course on Big Data, Large Scale Machine Learning by John Langford and Yann LeCun
- Learning from Data by Yaser Abu-Mostafa
- Introduction to Machine Learning by Barnabas Poczos and Alex Smola
- Machine Learning by Nando de Freitas
- Pattern Recognition by Fred A. Hamprecht (2011 2012 )
- Machine Learning and Pattern Recognition by Charles Sutton (Lecture notes )
- Machine Learning by Joachim M. Buhmann
- Pattern Recognition by P.S.Sastry - NPTEL
- CS188 Intro to AI by Pieter Abbeel - Edx
- Introduction to Computational Thinking and Data Science
- PASCAL Lecture Series - VideoLectures.Net
- CO395: Machine Learning by Maja Pantic - very introductory course
- Prediction: Machine Learning and Statistics by Cynthia Rudin- very introductory course
- Machine Learning by Michael Littman
- SGN-2506: Introduction to Pattern Recognition by Jussi Tohka
- CSCI1950-F: Introduction to Machine Learning by Erik Sudderth
- CS 229: Machine Learning by Andrew Ng
- CSC 411: Machine Learning and Data Mining by Aaron Hertzmann
- CS 760: Machine Learning by David Page
- CSE446: Machine Learning
- Introduction To Machine Learning by David Sontag
- CSC321: Introduction to Neural Networks and Machine Learning by Tijmen Tieleman - this might be a bit advanced for beginners
- CSC2515: Introduction to Machine Learning by Geoffrey Hinton - this is very similar to the above
- Introduction to Pattern Recognition by Sargur Srihari
- Introduction to Machine Learning Course by Sargur Srihari - this might be a bit advanced for beginners
- Introduction to Machine Learning by Shai Shalev-Shwartz - this might be a bit advanced for beginners
- COS 511: Foundations of Machine Learning by Rob Schapire
- CS 2750 Machine Learning by Milos Hauskrecht
- Introductory Applied Machine Learning by Victor Lavrenko and Nigel Goddard
- Machine Learning and Pattern Recognition by Yann LeCun
- Machine Learning by Tom Mitchell
- Machine Learning by Tommi Jaakkola
- Machine Learning by Andrew Zisserman
- Pattern Recognition and Analysis by Rosalind W. Picard
- CSCE 666: Pattern Analysis Fall by Ricardo Gutierrez-Osuna
- Pattern Recognition by Richard Zanibbi
- Neural Networks and Pattern Recognition by Ömer Cengiz ÇELEBİ
- Machine Learning by Carl Edward Rasmussen and Zoubin Ghahramani
- Learning from Data by Amos Storkey
- Machine Learning: Pattern Recognition by Gwenn Englebienne
- Machine Learning by Tony Jebara
- Machine Learning I by Le Song
- Advanced data mining: theory and applications by Dmitry Efimov
- CS281: Advanced Machine Learning by Ryan Adams
- CSC2535: Advanced Machine Learning by Geoffrey Hinton
- Advanced Topics in Machine Learning by Andreas Krause
- Advanced Topics in Machine Learning (Kernel Methods) by Arthur Gretton - Gatsby
- Pattern Recognition by Ricardo Gutierrez-Osuna
- Introduction to Pattern Recognition by Jason Corso
- Pattern Recognition by Olga Veksler
- Pattern Recognition by Charles Robertson
- Pattern Recognition by Esa Alhoniemi
- Advanced Machine Learning by Mehryar Mohri
- Advanced Machine Learning by Tony Jebara
- Machine Learning II by Le Song
- Machine Learning by Byron Boots
- STA 663 Statistical Computing and Computation by Cliburn Chan and Janice McCarthy
- Statistical Machine Learning from Data by Samy Bengio
- STA 4273H: Statistical Machine Learning by Ruslan Salakhutdinov
- CS59000: Statistical Machine Learning by Alan Qi
- Statistical Machine Learning and Data Mining by Yee Whye Teh
- Identification, Estimation, and Learning by Harry Asada
- Unsupervised Learning by Zoubin Ghahramani
- Probabilistic and Unsupervised Learning by Maneesh Sahani - Gatsby
- Approximate Inference and Learning in Probabilistic Models by Maneesh Sahani - Gatsby
- Reinforcement Learning by David Silver
- Reinforcement Learning by Michael Herrmann
- Pattern Recognition by Xia Hong
- Pattern Recognition for Machine Vision by Bernd Heisele and Yuri Ivanov
- CS 5785: Modern Analytics by Serge J. Belongie
- Advanced Statistical Machine Learning by Stefanos Zafeiriou
- Machine Learning Practical by Steve Renals
- Statistical Machine Learning by Pier Francesco Palamara
- Computational Statistics by Martin Maechler and Peter Bühlmann
- Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
- Martinez, W. L., & Martinez, A. R. (2007). Computational statistics handbook with MATLAB. 2nd Ed. CRC press.
- Martinez, W. L., Martinez, A. R., Martinez, A., & Solka, J. (2010). Exploratory data analysis with MATLAB. 2nd Ed. CRC Press.
- Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of Machine Learning. Springer Science & Business Media.
- Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. 3rd Ed. Morgan Kaufmann.
- Conway, D., & White, J. (2012). Machine Learning for Hackers. O'Reilly Media.
- McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
- Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. Cambridge University Press.
- Brownlee, J. (2013). Clever Algorithms: Statistical Machine Learning Recipes. Jason Brownlee.
- Kutz, J. N. (2013). Data-driven modeling & scientific computation: methods for complex systems & big data. Oxford University Press.
- Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc.
- Battiti, R., & Brunato, M. (2014). The LION Way. Machine Learning Plus Intelligent Optimization. CreateSpace.
- Zumel, N., Mount, J., & Porzak, J. (2014). Practical data science with R. Manning.
- Nolan, D., & Lang, D. T. (2015). Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. CRC Press.
- Davidson-Pilon, C. (2015). Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional.
- Elston, S. F. (2015). Data Science in the Cloud with Microsoft Azure Machine Learning and R. O'Reilly Media, Inc.
- Kelleher, J. D., Mac Namee, B., & D'Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press.
- Lantz, B. (2015). Machine Learning with R. 2nd Ed. Packt Publishing Ltd.
- Yu-Wei, C. D. C. (2015). Machine Learning with R cookbook. Packt Publishing Ltd.
- Raschka, S. (2015). Python Machine Learning. Packt Publishing Ltd.
- Ankan, A., & Panda, A. (2015). Mastering Probabilistic Graphical Models Using Python. Packt Publishing Ltd.
- Grus, J. (2015). Data Science from Scratch: First Principles with Python. O'Reilly Media.
- Madhavan, S. (2015). Mastering Python for Data Science. Packt Publishing Ltd.
- Zaccone, G. (2016). Getting started with TensorFlow. Packt Publishing Ltd.
- VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media.
- Muller, A. C., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc.
- Guttag, J. V. (2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press.
- Dixon, J. (2016). Mastering. NET Machine Learning. Packt Publishing Ltd.
- Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
- Geron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems.O'Reilly Media.
- Mitchell, T. M. (1997). Machine Learning. McGraw Hill.
- Smola, A., & Vishwanathan, S. V. N. (2008). Introduction to Machine Learning. Cambridge University Press.
- Alpaydin, E. (2010). Introduction to Machine Learning. MIT Press.
- Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010). Introduction to Pattern Recognition: A Matlab Approach. Academic Press.
- Rogers, S., & Girolami, M. (2011). A First Course in Machine Learning. CRC Press.
- Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning From Data. AMLBook.
- Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
- Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., & Held, P. (2013). Computational Intelligence: A Methodological Introduction. Springer Science & Business Media.
- Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
- Nilsson, N. J. (2015). Introduction to machine learning. An early draft of a proposed textbook.
- James, G., Witten, D., & Hastie, T. (2017). An Introduction to Statistical Learning: With Applications in R.
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
- Theodoridis, S., Koutroumbas, K., (2009). Pattern Recognition, 4th Ed., Academic Press
- Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern Classification. John Wiley & Sons.
- Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of Machine Learning. MIT press.
- Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
- Theodoridis, S. (2015). Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
- Rojas, R. (1996). Neural Networks: A Systematic Introduction. Springer Science & Business Media.
- Ripley, B. D. (1996). Pattern recognition and neural networks. Cambridge University Press.
- Moon, T. K., & Stirling, W. C. (2000). Mathematical methods and algorithms for signal processing. Pearson.
- Webb, A. R. (2002). Statistical Pattern Recognition. 2nd Ed. John Wiley & Sons.
- MacKay, D. J. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.
- Kushner, H., & Yin, G. G. (2003). Stochastic Approximation and Recursive Algorithms and Applications (Vol. 35). 2nd Ed. Springer Science & Business Media.
- Taylor,J. S. & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
- Williams, C. K., & Rasmussen, C. E. (2006). Gaussian Processes for Machine Learning. MIT Press.
- Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media.
- Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., & Tibshirani, R. (2009). The Elements of Statistical Learning. 2nd Ed. New York: Springer.
- Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models. MIT Press.
- Haykin, S. O. (2009). Neural Networks and Learning Machines. 3rd Ed. Pearson.
- Gentle, J. E. (2009). Computational statistics. Springer.
- Russell, S. J., & Norvig, P. (2010). "Part IV: Uncertain knowledge and reasoning". Artificial Intelligence: A Modern Approach. Prentice Hall.
- Bühlmann, P., & Van De Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media.
- Givens, G. H., & Hoeting, J. A. (2012). Computational statistics. 2nd Ed. John Wiley & Sons.
- Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
- Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge University Press.
- Bubeck, S. & Cesa-Bianchi, N. (2012). Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. Foundations and Trends® in Machine Learning, 5(1), 1-122. Now Publishers.
- Jebara, T. (2012). Machine Learning: Discriminative and Generative. Springer Science & Business Media.
- Flach, P. (2012). Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press.
- Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press.
- Blum, A., Hopcroft, J., & Kannan, R. (2015). Foundations of Data Science. (link)
- Goodman, N. D., & Tenenbaum, J. B. (2016). Probabilistic Models of Cognition. 2nd Ed. (link)
- Bengio, Y., Goodfellow, I. J., & Courville, A. (2016). Deep Learning. MIT Press.
- PRMLT - MATLAB Toolbox for the book of PRML by C. Bishop
- pmtk3 - Probabilistic Modeling Toolkit for MLPP book by Murphy in Matlab/Octave (3rd edition)
- pyprobml - Python code for MLPP book by K. Murphy
- BRMLtoolbox - MATLAB and Julia code for the BRML book by D. Barber
- PyBRML - Python code for the BRML book by D. Barber
- PythonDataScienceHandbook - Python code for the PDSH book by J. VanderPlas
- handson-ml - Python code for the HOML book by A. Geron
- Microsoft R
- BigML - Prediction and Analytics tasks under 16MB are free
- Neural Network Toolbox - MATLAB
- Torch7 - a scientific computing framework for Machine Learning algorithms (based on Lua)
- Lush - an OOP language for large-scale numerical and graphic applications (based on Lisp)
- TensorFlow - Google
- CNTK - Microsoft
- Pylearn2 - A Machine Learning research library
- scikit-learn - Python
- mlpy - Python
- Orange - Data Visualization and Analysis
- Matlab Machine Learning Toolboxes
- mloss.org. Machine Learning open source software
- April-ANN - A Pattern Recognizer In Lua with ANNs
- Weka- Data mining software in Java
- MLTK - Machine Learning Toolkit in Java
- Bayesian Modeling and Monte Carlo Methods
- The Lightspeed Matlab Toolbox
- MCML - broad range support for Monte Carlo methods to implement Machine Learning applications
- Orange - Visual programming language
- MLPACK - C++
- Shogun - C++ toolbox that offers interfaces for MATLAB,Octave, Python, R, Java, Lua, Ruby and C# (mainly for Kernel Machines)
- autograd - Efficiently computes derivatives of numpy code (Python)
- pySPACE - Signal Processing And Classification Environment (SPACE) in Python
- dlib - C++ (with Python API)
- Computational Statistics Toolbox - MATLAB
- Exploratory Data Analysis (EDA) Toolbox - MATLAB
- aimacode - Code for the AIMA book by Russell and Norvig
- Core ML - Apple
- pycm - Python
- UCI Machine Learning Repository - a large collection of standard datasets for testing learning algorithms
- Awesome Public Datasets (GitHub)
- Datasets on Kaggle
- DeepLearning.Net - a list of datasets that can be used for benchmarking Deep Learning algorithms
- List of datasets for machine learning research
- System Identification / Estimation Theory
- Information Theory
- Predictive Learning vs. Representation Learning
- Artificial Intelligence - Google Scholar Metrics (Top Publications)
- Computer Vision and Pattern Recognition - Google Scholar Metrics (Top Publications)
- Data Mining and Analysis - Google Scholar Metrics (Top Publications)
- Machine Learning - Nature
- NIPS - A top-tier Conference in Machine Learning (and other topics)
- ICML - A top-tier Conference in Machine Learning (and other topics)
- Machine Learning Types - Medium
- Video Tutorials - Youtube channel of 'Mathematical Monk'
- Basic Concepts in Machine Learning
- Awesome-Machine-Learning (Github) - A curated list of Machine Learning frameworks, libraries and software (by language)
- Computational Statistics in Python (2016 version, Github)
- Comparison of software toolkits
- Software for Data Mining, Analytics, Data Science, and Knowledge Discovery - KDnuggets
- Machine Learning and Statistical Learning in R
- Metacademy - List of concepts in Machine Learning
- Machine Learning, Statistical Inference and Induction - Notebook
- A Course in Machine Learning by Hal Daumé III - textbook
- Courses on Statistical Pattern Recognition - summary of 33 courses
- FastML - List of Machine Learning courses online
- Results and Errors Percentage on Standard Datasets - MNIST, CIFAR, Pascal VOC, etc.
- Machine Learning Surveys - List of literature surveys, reviews, and tutorials on Machine Learning and related topics
- Machine Learning on Google+ - online community
- The Shape of Data - Data Mining and Machine Learning blog
- Data Mining & Machine Learning - a mindmap diagram comparing the two areas
- Basic Glossary of Machine Learning
- Machine Learning with MATLAB - free ebook
- References for Machine Learning - Wikia
- Which machine learning algorithm should I use? - blog post
- Tutorial: Getting Started with Machine Learning in Python - blog post
- How to know that your machine learning problem is hopeless? - Stack Exchange
- Yandex School of Data Analysis - Github
- practical-ml (GitHub) - code
- Hyperparameter Optimization in Machine Learning Models - blog post
- Hyperparameter tuning for machine learning models - blog post