Areas of machine leanring

1. Machine Learning
Applications

2. Introduction
• Machine learning (ML) is a proven to have
significant impact on both industry and
research. There are numerous successful
applications of machine learning; but here we
introduce only a few selective applications.

3. Financial Applications
• Machine learning techniques have produced some of the financial
industry's most successful trading strategies during the past 20
years. With markets, trade execution and financial decision making
becoming more automated and competitive, practitioners
increasingly recognize the need for ML. Learning techniques include
reinforcement learning, optimization methods, recurrent and state
space models, on-line algorithms, evolutionary computing, kernel
methods, Bayesian estimation, wavelets, neural nets, SVMs,
boosting, and multi-agent simulation. Financial domains where
machine learning apply includes high frequency data, trading
strategies, execution models, forecasting, volatility, extreme events,
credit risk, portfolio management, yield curve estimation, option
pricing, and so forth.

4. Weather forecasting
• In recent years, many solutions to intelligent weather
forecast have been proposed, especially on
temperature and rainfall predictions. They solutions
include techniques such as Neural Networks, SVM,
regression, and time series analysis, with the obtained
results confirm that proposed solutions have the
potential for successful application to the problem of
temperature and rainfall estimation, and the
relationships between the factors that contribute to
certain weather conditions can be estimated at a
certain extent. There are also extended application of
weather prediction such as application involving
avalanche danger prediction.

5. Speech recognition
• Machine-learning methods can be used to develop models
that can perform reasonably well in speech recognition and
synthesis tasks, despite our incomplete understanding of
the human speech perception and production mechanisms.
Machine learning can also be used as a complement to
standard statistics to extract knowledge from multivariate
data collections, where the number of variables, the size
(number of data points), and the quality of the data
(missing data, inaccurate transcriptions) would make
standard analysis methods ineffective Finally these
methods can be used to model and simulate the processes
that take place in the human brain during speech
perception and production.

6. Natural Language Processing
• Natural-language-generation systems convert information
from computer databases into normal-sounding human
language. Natural-language-understanding systems convert
samples of human language into more formal
representations that are easier for computer programs to
manipulate. Applications of machine learning to language
processing include document classification, document
segmentation, tagging, entity extraction, problems
involving parsing, inducing representations of linguistic
objects. General techniques include probabilistic parsing,
reinforcement learning in dialog systems, Neural networks,
dimensionality reduction methods, non-negative
factorizations, finite-state techniques, Bayes methods,
SVM, and so forth.

7. Smart environments
• Smart environments is a technological concept
that, according to Mark Weiser is "a physical
world that is richly and invisibly interwoven with
sensors, actuators, displays, and computational
elements, embedded seamlessly in the everyday
objects of our lives, and connected through a
continuous network" One major feature of smart
environments is the Predictive and Decision-
Making capabilities, which is a direct application
of machine learning.

8. Games
• Computer games have evolved from the
simple graphics and gameplay of early titles
like Spacewar, to a wide range of more visually
advanced titles. And at the same time the
game play evolved using AI and machine
learning techniques. Machine learning
techniques involves learning by observations,
learning by instruction and learning by
experience.

9. Robotics
• Robotics is the science and technology of robots,
their design, manufacture, and application.
Robotics requires a working knowledge of
electronics, mechanics and software, and is
usually accompanied by a large working
knowledge of many subjects. Robotics and
machine learning has evolved to become more
than skills involving reaching, grasping, and
manipulation.

10. Medicine and Biology
• Continuous advances in computational
intelligence technology have enabled researchers
to collect and effectively analyze large amounts of
complex clinical and biological data. In recent
years, research in the interdisciplinary area of
computer assisted medical decision-making has
dramatically intensified. The overall objective is
to provide physicians with computer tools that
can assist them with their clinical decisions via
machine learning algorithms.

11. Visit more self help tutorials
• Pick a tutorial of your choice and browse
through it at your own pace.
• The tutorials section is free, self-guiding and
will not involve any additional support.
• Visit us at www.dataminingtools.net