Table of Contents
Topics #
Foundations #
Topics #
- Automated machine learning
- Cluster analysis
- Dimensionality reduction
- Image completion
- Machine learning for healthcare
- Machine learning for physical sciences
- One-shot learning
- Recommender system
- Training dataset
Methods #
- Artificial neural network
- Boosting (machine learning)
- Decision tree
- Deep learning
- EM algorithm
- Naive Bayes classifier
- Cross validation and Stability selection
Books #
- The elements of statistical learning
-
An Introduction to Statistical Learning with applications in R - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- Machine Learning by Tom M. MItchell
- Bayesian Reasoning and Machine Learning
- Machine Learning: A Probabilistic Perspective by Kevin Murphy
- a reading list by Michael I. Jordan
Courses #
-
Andrew Ng's artificial intelligence | machine learning - open lecture.
-
Practical Machine Learning by Michael Jordan (computer scientist)
- Machine Learning Fall 2009 by Carlos Guestrin
- http://www.youtube.com/playlist?list=PLD0F06AA0D2E8FFBA - Machine Learning by mathematical monk
- CALTECH: learning from data
- Machine Learning and Probabilistic Graphical Models Course by Sargur Srihari
- Kaggle Python Tutorial on Machine Learning
Tools and libraries #
- Python#Modules_.26_Libraries
- scikit-learn
- http://mldemos.epfl.ch/ - MLDemos
- http://harthur.github.com/brain/ - Javascript supervised machine learning library
- GraphLab
- [[https://github.com/JohnLangford/vowpal_wabbit/wiki]] - vowpal wabbit, a fast online learning algorithm
- http://www.cs.waikato.ac.nz/ml/weka/index.html - Weka
- http://blog.mashape.com/post/48074869493/list-of-machine-learning-apis
Tutorials and Articles #
- How to Write a Spelling Corrector by Peter Norvig
- The Ideal Large Scale Learning Class
- Self-Study Guide to Machine Learning
- Machine learning in 10 pictures
- kaggle:Learning from the best
- Machine Learning Done Wrong
- Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur
- Machine Learning is Fun!
- What I learned from Deep Learning Summer School 2016
- Model evaluation, model selection, and algorithm selection in machine learning - Part I
- Google developers: Machine Learning Recipes
- Rules of Machine Learning: Best Practices for ML Engineering
Websites and blogs #
Talks and lectures #
- http://blog.videolectures.net/100-most-popular-machine-learning-talks-at-videolectures-net/
- mathematical monk: Machine learning
- Advice for applying Machine Learning by Andrew Ng
Other resources #
References #
Reviews #
Explaining the prediction #
Incoming Links #
Related Articles (Article 0) #
- Automated machine learning
- Boosting (machine learning)
- Class imbalance
- Computer science
- Deep learning
- Future of work
- Gradient descent
- Learning
- Privacy and Deanonymization
- Python/Modules
- Recommender system
- Regular expression
- scikit-learn
Suggested Pages #
- 0.320 Class imbalance problem
- 0.069 Regex
- 0.053 Neural network
- 0.025 Ulrike von Luxburg
- 0.025 Netflix prize
- 0.025 Yoav Freund
- 0.025 Information Theory, Inference, and Learning Algorithms
- 0.024 Atwood's law
- 0.024 Residual Learning
- 0.022 David Feldman
- More suggestions...