Outline of Machine Learning and its Cross-disciplinary fields

Aug. 14, 2017, 5:06 p.m. By: Vishakha Jha

Machine Learning

Machine Learning is a branch of the larger discipline of AI which prepares software applications in becoming more appropriate and accurate while evaluating the possible outcomes. It basically corresponds to the most intelligent techniques indulging the decisions making related to vast data. It does not need explicit programming and aims to construct algorithms that can receive input and predict an output through statistical analysis. Machine Learning involves 5 basic steps:

  1. Assembling data: The data from different sources with varying density and volume is collected and used for future learning.

  2. Arranging and composing the data: All the further proceedings depends on the data collected and processed so it is very important to maintain the quality of data. It is required to take proper measures for fixing the issues with data such as missing data or inappropriate data.

  3. Training a model: This step includes selecting the best-suited algorithm and then the representation of data in the form of the model. The data is further divided into two parts – train and test.

  4. Evaluating the model: It focuses on testing the accuracy and assessing the algorithm chosen.

  5. Improving the performance: This might involve enhancing efficiency by adopting an altogether different model or introducing new variables.

Machine Learning broadly includes 4 Cross-Disciplinary Fields- Adversarial machine learning, Predictive analytics, Quantum machine learning and Robot learning.

Adversarial machine learning

The study shows that machine learning is being adopted by most of the companies for their day-to-day operations. But the aspect which we neglect is security which is one of the most important elements that needs to be taken care of. Adversarial machine learning is a research field which is present at the junction of machine learning and computer security. It promotes the safe adoption of machine learning techniques through accepting adversarial settings like malware detection, biometric recognition and spam filtering. The machine learning technique was induced for the stationary environment but as at times this condition is violated, the malicious adversary can easily affect input data and exploit the system. Attacks on machine learning systems can majorly be categorized into one of two types-

  • Evasion Attacks- It is one of the simplest and primary kinds of attacks which works towards diverting the learning outcome.

  • Poisoning Attacks- Emphasis is on influencing the training data to get an access to the learning outcome.

The solution to adversarial machine learning is layered defences. The adoption of techniques like understanding and assessing training data, Sanitizing the data and examining the algorithm can lead us to reduce the system risk.

Predictive analytics

Another field includes predictive analysis i.e. an area of statistics which is concerned with deriving information from data and utilizing it to predict further behaviour along with analysing pattern which can be applied to any prospect- present, past and future. It encloses a variety of statistical techniques including predictive modelling, machine learning, and data mining. The statistics along with data mining and text analytics reveal the prospect of predictive intelligence to users which could be used for both structured and unstructured data. The process of Predictive Analytics includes various steps- Defining the project, Collection of Data, Data Analysis which is further preceded by Statistics and Modeling leading to Deployment and Model Monitoring.

Organizations are engaging machine learning based predictive analytics to achieve an edge over the entire market. The advancements in the field of ML such as neural networks and deep learning algorithms help in unleashing hidden patterns in unstructured data sets leading to discover new information.

Quantum machine learning

Quantum machine learning is a transpiring interdisciplinary research area at the junction of quantum physics and machine learning. The algorithms of Quantum ML can take the benefit from quantum computation so as to revamp classical methods of machine learning. Both the concepts- machine learning and quantum information processing are quickly expanding and have their own associated variability. The quantum versions of ML have the ability to learn from data at a faster pace and with better precision, producing more accurate predictions. Machine learning may lead up to scaling quantum information processing, which can provide us with an insight of creating quantum computers. Computational power helps us in determining how fast we can capitalize on our data and models, and quantum machine learning may lead us to the way for constructing both intelligent systems and quantum computers.

Robot learning

Robot learning is a field at the intersection of ML and robotics. It aims to enhance and improve the skills that a robot acquires including various novel skills and enabling it to adjust to its environment through learning algorithms. The embodiment of the robot enables the same time specific difficulties and a chance for moving towards the learning process. Learning can include different ways from autonomous self-exploration to guidance from a human teacher.

Machine Learning is a field of Computer Science which has the potential to provide human-race with a number of new technologies. It has the ability to go beyond our imagination and with a meaningful exploration, it can lead us to a great number of discoveries. There are many fields which still needs to be studied to get a better future prospect.

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