Machine learning

As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods create computer programs by extracting rules and patterns out of massive data sets. It should be noted that although pattern identification is important to Machine Learning, without rule extraction a process falls more accurately in the field of data mining.

Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations. Many inference problems turn out to be NP-hard or harder, so part of machine learning research is the development of tractable approximate inference algorithms. 数据挖掘研究院

Machine learning has a wide spectrum of applications including search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion. 数据挖掘研究院

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method. Some machine learning researchers create methods within the framework of Bayesian statistics. 数据挖掘研究院

Algorithm types

Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:

• supervised learning --- where the algorithm generates a function that maps inputs to desired outputs. One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector into one of several classes by looking at several input-output examples of the function.
• unsupervised learning --- which models a set of inputs: labeled examples are not available.
• semi-supervised learning --- which combines both labeled and unlabeled examples to generate an appropriate function or classifier.
• reinforcement learning --- where the algorithm learns a policy of how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback that guides the learning algorithm.
• transduction --- similar to supervised learning, but does not explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and new inputs.
• learning to learn --- where the algorithm learns its own inductive bias based on previous experience.

The performance and computational analysis of machine learning algorithms is a branch of statistics known as computational learning theory. 数据挖掘实验室

Machine learning topics

This list represents the topics covered on a typical machine learning course.

• Modeling conditional probability density functions: regression and classification
  • Artificial neural networks
  • Decision trees
  • Gene expression programming
  • Genetic Programming
  • Gaussian process regression
  • Linear discriminant analysis
  • k-Nearest Neighbor
  • Minimum message length
  • Perceptron
  • Quadratic classifier
  • Radial basis functions
  • Support vector machines
• Inductive Transfer and Learning to Learn
  • Inductive transfer
• Modeling probability density functions through generative models:
  • Expectation-maximization algorithm
  • Graphical models including Bayesian networks and Markov Random Fields
  • Generative Topographic Mapping
• Appromixate inference techniques:
  • Markov chain Monte Carlo method
  • Variational Bayes
• Optimization: most of methods listed above either use optimization or are instances of optimization algorithms.
• Multi-objective Machine Learning: An approach that addresses multiple, and often confliciting learning objectives explicitly using Pareto-based multi-objective optimization techniques.