Machine Learning Areas

Depending on the nature of the learning "signal" or "feedback" available to a learning system, machine learning tasks are typically classified into three broad areas:

Supervised learning

Supervised learning is the type of learning that takes place when the training data are labelled with the correct outcome, which gives the learning algorithm examples for learning. This is like having a supervisor who can show different objects and tell what they represent. The task of the learning algorithm is to learn the relation. More technically, given a set of example inputs X and their outcomes Y, the supervised learning aims to learn a general mapping function f that transforms inputs to outputs: f: X a Y

Unsupervised Learning

On the other hand, unsupervised learning is harder because there is no supervisor telling you what the objects represent; instead, the learning algorithm should figure that out which objects go together by itself. Unsupervised learning algorithms do not assume any outcome labels Y, since they focus on grouping similar inputs X into clusters. Unsupervised learning can hence discover hidden patterns in data as well as similar items in the dataset.

Reinforcement Learning

Reinforcement learning assumes that an agent, which can be a robot, a bot or a computer program, interacts with a dynamic environment to achieve a specific goal. The environment is described with a set of states and the agent can take different actions to move from one state to another. Some states are marked as goal states and if the agent achieves that state, it receives a large reward. In other states, the reward is smaller, non-existing or even negative.

Machine Learning Can Solve Key IT Operations Problems

Today's IT operations, struggle daily to cope with—and derive value from—huge amounts of data generated in dynamic infrastructures and applications. Due to the complexity of the underlying systems, human and policy driven management is, basically, unable to react fast enough and realize value from large amounts of data statistics and patterns. Machine learning can help IT operations teams to analyze IT performance issues, and provide insights to maintain high levels of availability for critical business systems and applications.

Machine learning relies on these different types of data analysis:
Descriptive (data mining): Looks at data and analyzes past events for insight for how to approach the future, quantifying data relationships. Using anomaly detection (supervised and unsupervised learning approach), IT operations can locate problematic behavior changes hidden in huge volumes of operations data, so IT operations can know what happened and get to a root cause faster.
Predictive (forecasting): Turns data into valuable, actionable information by using data to predict (supervised learning approach) when problems will occur, given past behavior, analyzing frequent operational patterns that could lead to incidents.
Prescriptive (optimization): Automatically synthesizes big data and other inputs to make predictions about what could go wrong and suggest decision options for taking steps to prevent issues.
To automatically identify and isolate disruptions and failures, IT operations needs to be able to identify and predict anomalies and detect risk in IT environments.


Due to the complexity of IT systems, machine learning is best geared to automatically and quickly analyze tremendous volumes of data distributed across disparate data stores, identifying patterns for detecting anomalies, and revealing performance and security risks.

Machine learning can help IT managers to not only isolate errors, but also gain valuable insight in real-time into those data anomalies that create system errors and failures. Automated analysis of the data created by IT systems is critical for seeing the clues as to why applications and systems fail. By clearly seeing the associated causes of issues and correlating them to a specific error, operations managers can better maintain peak operational efficiency for their IT infrastructures, reduce the mean-time-to-resolution within support organizations and provide end users with a near error-free experience. Credit This article is excerpted from my upcoming book: Practical Machine Learning in Java, scheduled to be published later this year.

Credit : This article is excerpted from my upcoming book: Practical Machine Learning in Java, scheduled to be published later this year.


  • Bangkok University, Bangkok
  • Asian Institute of Technology, Bangkok
  • SAGE University, India
  • Sensus Edusoft (P) Ltd., India
  • National Dong Hwa University, Taiwan

Featured and Associated events

Upcoming Conferences Are in Thailand, UK, Netherlands, Rome

Sep 2018

The CI Summit 2018

The Summit on Computational & Web Intelligence
Sep 14, 2018, Bankok, Thailand

Nov 2018

The IOT Congress 2018

World Congress on IOT
Nov 16, 2018, London, UK

Feb 2019

ESOFT 2019

International Conference on embedded & Software Design
Feb 22, 2019, Amsterdam, Netherlands

Apr 2019

The ML Summit 2019

The Summit on Machine Learning
Apr 19, 2019, Italy, Rome