Everything about Machine Learning totally explained
» For the journal, see Machine Learning (journal).
As a broad subfield of
artificial intelligence,
machine learning is concerned with the design and development of
algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning:
inductive, and
deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.
The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to
data mining and
statistics, but also
theoretical computer science.
Applications
Machine learning has a wide spectrum of applications including
natural language processing,
syntactic pattern recognition,
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.
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 can't be entirely eliminated since the designer of the system must specify how the data is 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 statistical machine learning researchers create methods within the framework of
Bayesian statistics.
Algorithm types
Machine learning
algorithms are organized into a
taxonomy, based on the desired outcome of the algorithm. Common algorithm types include:
- Supervised learning — in which 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 — An agent 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 — in which 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 doesn't explicitly construct a function: instead, tries to predict new outputs based on training inputs, training outputs, and test inputs which are available while training.
- Learning to learn — in which the algorithm learns its own inductive bias based on previous experience.
The computational analysis of machine learning algorithms and their performance is a branch of
theoretical computer science known as
computational learning theory.
Machine learning topics
» This list represents the topics covered on a typical machine learning course.
Prerequisites
Bayesian theory
Modeling conditional probability density functions: regression and classification
Artificial neural networks
Decision trees
Gene expression programming
Genetic algorithms
Genetic programming
Inductive Logic Programming
Gaussian process regression
Linear discriminant analysis
K-nearest neighbor
Minimum message length
Perceptron
Quadratic classifier
Radial basis function networks
Support vector machines
Algorithms for estimating model parameters:
Dynamic programming
Expectation-maximization algorithm
Modeling probability density functions through generative models:
Graphical models including Bayesian networks and Markov random fields
Generative topographic map
Approximate inference techniques
Monte Carlo methods
Variational Bayes
Variable-order Markov models
Variable-order Bayesian networks
Loopy belief propagation
Optimization
Most of methods listed above either use optimization or are instances of optimization algorithms
Meta-learning (ensemble methods)
Boosting
Bootstrap aggregating
Random forest
Weighted majority algorithm
Inductive transfer and learning to learn
Inductive transfer
Reinforcement learning
Temporal difference learning
Monte-Carlo method
Further Information
Get more info on 'Machine Learning'.
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