machine learning
You have an idea and data?
Using both your data and domain knowledge, we develop models that can
predict or even reason with your data. These models can help to streamline
your organization and to improve your overall efficiency.
Using both your data and domain knowledge, we develop models that can
predict or even reason with your data. These models can help to streamline
your organization and to improve your overall efficiency.
prediction
Prediction problems are problems of the form: given input, what is output? These problems are numerous, and solutions using statistical modeling and machine learning are applied in many areas, such as:
- Consumer behavior
- Physical processes
- Finance
These types of solutions are typically applied when mechanisms are not well understood, but data is sufficiently available.
More about prediction
Prediction problems are problems of the form: given input, what is output? These problems are numerous, and solutions using statistical modeling and machine learning are applied in many areas, such as:
- Consumer behavior
- Physical processes
- Finance
These types of solutions are typically applied when mechanisms are not well understood, but data is sufficiently available.
More about prediction
reasoning
Reasoning problems are of the form: given the output, what are the possible inputs that may have caused this output? For these types of problems, artificial reasoning models are used.
Applications are found in:
- Medical diagnosis
- Machine diagnosis and fault analysis
- Expert systems
These types of solutions are typically applied when expertise is available, but need to be shared among your organization.
More about reasoning
Reasoning problems are of the form: given the output, what are the possible inputs that may have caused this output? For these types of problems, artificial reasoning models are used.
Applications are found in:
- Medical diagnosis
- Machine diagnosis and fault analysis
- Expert systems
These types of solutions are typically applied when expertise is available, but need to be shared among your organization.
More about reasoning
example:
computer with taste
We applied machine learning to learn the computer a taste for wine! Winewinewine provided domain expertise and data. We have analyzed the data, designed the machine learning algorithms, and developed the computational kernel of Winestein, the neural sommelier.
Visit www.winewinewine.nl and ask Winestein what to drink with tonight's dinner! (in Dutch, for the moment.)
what is machine learning?
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn and reason based on data, such as from sensor data or databases.
Neural networks and Bayesian networks are two of the many models from machine learning that we may apply. Which model is used and how it is applied depends strongly on the problem, the requirements, the available data and the available domain knowledge.
computer with taste
We applied machine learning to learn the computer a taste for wine! Winewinewine provided domain expertise and data. We have analyzed the data, designed the machine learning algorithms, and developed the computational kernel of Winestein, the neural sommelier.
Visit www.winewinewine.nl and ask Winestein what to drink with tonight's dinner! (in Dutch, for the moment.)
what is machine learning?
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn and reason based on data, such as from sensor data or databases.
Neural networks and Bayesian networks are two of the many models from machine learning that we may apply. Which model is used and how it is applied depends strongly on the problem, the requirements, the available data and the available domain knowledge.