Areas of machine leanring
Download as PPTX, PDF0 likes560 views
This document provides an overview of machine learning applications across several domains: - Financial applications including trading strategies, forecasting, and portfolio management utilize techniques like reinforcement learning and neural networks. - Weather forecasting uses neural networks, support vector machines, and time series analysis to predict temperature and rainfall. - Speech recognition and natural language processing apply machine learning to tasks like document classification, tagging, and parsing using probabilistic and neural network models. - Other applications include smart environments using predictive models, computer games using reinforcement learning, robotics combining mechanics and software, and medical decision support analyzing clinical and biological data.
Areas of machine leanring
- 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