Machine Learning

From Dahuawiki

1 What is Machine Learning
2 Different Types of Learning
3 Recommended Books
4 Course
5 Conferences

What is Machine Learning

Machine Learning is an important field in artificial intelligence, which mainly concerns how a computer can learn from observations and practice and thereby make predictions for new inputs.

Machine Learning is based on theoretical computation theory and statistics. The principles, models, and algorithms are used by a wide range of artificial intelligence fields like computer vision, speech recognition, pattern classification, natural language processing, data mining, and bioinformatics.

Machine Learning comprises a number of areas. Here is a partial list of the active topics in the learning literatures

Classification and Regression
Clustering
Decision Trees
Kernel Density Estimation
Support Vector Machines
Neural Networks
Bayesian Networks
Markove Random Field and Conditional Random Field
Gaussian Process
Belief Propagation
Monte Carlo and Sampling
Graphical Models
Dimension Reduction
Kernel-based Learning
Manifold-based Learning
Graph-based Inference
Semi-supervised Learning and Transduction
Model Fusion
Model Selection
Information Theory based Learning
Multi-Instance Learning

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Different Types of Learning

Depending on the forms of input and output, the learning process can be roughly categorized into the following types

Supervised Learning is to learn the map from input to output. Pairs of input and output are given in the learning stage.
Unsupervised Learning aims at modeling the input data, such as their clustering structure and sample distribution.
Semi-supervised Learning takes advantages of both labeled and unlabeled data.
Transductive Learning makes prediction on new samples based on the labeled samples.
Active Learning tries to gain information by asking "supervisor" selected questions.
Reinforcement Learning learns the strategy of action from the feedback of environment.

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Recommended Books

The following books are recommended to the researchers in machine learning.

J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
M. I. Jordan. Learning in Graphical Models. MIT Press, 1998.
Vladimir N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2nd ed.). Wiley-Interscience, 2000.
Bernhard Schlkopf and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.
Andrew R. Webb. Statistical Pattern Recognition (2nd ed.). John Wiley and Sons Ltd. 2002.
Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien. Semi-Supervised Learning. MIT Press, 2006.