--- title: "Does machine learning belong to artificial intelligence" description: "There are many different types of Machine Learning algorithms that can be used to create models of complex datasets" author: "Drishan Vig" published: 2022-08-30 updated: 2022-08-31 canonical: https://yourviews.mindstick.com/view/83708/does-machine-learning-belong-to-artificial-intelligence category: "data science" tags: ["feature learning", "machine learning algorithms", "inductive machine learning", "similarity learning", "neural networks"] reading_time: 4 minutes --- # Does machine learning belong to artificial intelligence When you think about [**machine learning**](https://www.mindstick.com/blog/124906/how-machine-learning-can-help-you-better-optimize-your-prices)**,** you might think of **Deep neural networks**. But there are several sub-fields that fall under the umbrella of machine learning. These include Inductive machine learning, Feature learning, and similarity learning. The difference between these methods is in the way they approach data. ## Deep neural networks are a subset of machine learning - **Neural networks** are composed of many connections and units. While each unit is several orders of magnitude smaller than the neurons in the human brain, they can perform a wide variety of tasks. For example, a **neural network** can process a video by breaking it into tiles. This data is then passed to a second layer of neurons, which then performs the task and provides a final output. - [**Machine learning algorithms**](https://www.mindstick.com/blog/84298/machine-learning-consultants-explain-benefits-of-drones-for-utilities) can be applied to any type of data problem. Examples of common **machine learning algorithms** include regression, k-means clustering, logistic regression, decision trees, and many others. In addition to the **algorithms and data sets**, an artificial neural network consists of layers of artificial neurons or computational units. These units can apply a threshold or activation function to decide whether messages should be passed on. ## Feature learning is a subset of supervised machine learning - **Feature learning** is the process of generating representations of input data. This process is performed by multilayer **neural networks**. These networks learn to represent input data in hidden layers and use this representation for [classification and regression](https://www.mindstick.com/forum/160616/difference-between-classification-and-regression-in-machine-learning) at the output layer. The most **common network architecture** used in feature learning is the **Siamese network**. **Feature learning** can be done with unlabeled or labeled data. It is often used to find **low-dimensional features**. It is also used in semi-[supervised learning](https://www.mindstick.com/blog/302851/supervised-learning-predictive-modeling-and-classification-techniques) and can improve the accuracy of supervised learning. - **Feature learning** is a subset of the supervised machine learning process. It is an important method in the field of **[artificial intelligence](https://www.mindstick.com/articles/44601/impact-of-artificial-intelligence-in-digital-marketing-is-huge-and-worthy)**. It is used to create predictive models based on input data. It uses past examples to teach the algorithm how to predict future instances. It is a popular technique in applications such as **clustering and association.** ## Inductive machine learning - **Inductive machine learning** is an approach to machine learning wherein a model learns a function from a set of examples, without receiving explicit instructions at each step. This method is popular in a number of applications, from autonomous driving to **speech recognition**. Here are some of the **advantages of inductive machine learning.** - This method uses association rule learning to **discover correlations among variables**. Inductive machine learning has applications in many fields, including bioinformatics and [natural language processing](https://www.mindstick.com/articles/33695/natural-language-processing). It can be used to **analyze massive databases**, such as **supermarket transaction data.** ## Similarity learning is a subset of supervised machine learning - **Similarity learning** is a subset of machine learning and is closely related to classification and regression. The goal of **similarity learning** is to discover a similarity function for two objects and use it to make decisions. It has applications in ranking, [recommendation systems](https://answers.mindstick.com/qa/112356/what-are-the-benefits-of-using-reinforcement-learning-for-personalization-in-recommendation-systems), visual identity tracking, and speaker and face verification. - **Similarity learning** is an important part of **artificial intelligence ([AI](https://www.mindstick.com/services/artificial-intelligence)).** It consists of introducing a set of objects to a machine and training it to determine the similarity of those objects. Each object is labeled with a number that represents its similarity. The goal is to approximate the function by using a large number of examples. ## Inductive machine learning is useful in bioinformatics - When using **machine learning**, we can make decisions based on the data that is available. This method can be applied to a variety of tasks. For instance, in **[computational biology](https://answers.mindstick.com/qa/37126/how-do-i-generate-new-research-ideas-in-computational-biology)**, we can use **inductive machine learning** to rationally design drugs based on past experiments. Other applications include evaluating credit offers and determining risk. - The field of **functional genomics** is [one of the fastest-growing](https://answers.mindstick.com/qa/116404/why-is-experiential-tourism-in-india-becoming-one-of-the-fastest-growing-travel-trends) fields of modern science. This field aims to understand the interactions of molecular components within living systems. AI is interested in this area because it requires complex knowledge representation, reasoning, and learning. ## Inductive machine learning is useful in natural language processing - **Inductive machine learning** is a powerful tool that can improve natural language processing by automating the analysis of large data sets. This type of learning involves inducing inductive bias in neural networks. Many marketers use **large databases of supermarket transactions** to train their models. This method helps marketers discover correlations between the various variables in the dataset. - The main difference between **deductive and inductive machine learning** is the type of inference used. While **deductive inference** requires all premises to be met, inductive learning uses existing data as evidence to make predictions. While **deductive learning** refers to predict specific examples in a domain, **inductive learning** refers to learning general rules from specific examples. --- Original Source: https://yourviews.mindstick.com/view/83708/does-machine-learning-belong-to-artificial-intelligence Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.