Machine Learning: An Applied Mathematics Introduction

<meta name="keywords" content="machine learning, applied mathematics, artificial intelligence, data science, supervised learning, unsupervised learning, regression, classification, clustering"> <meta property="og:description" content="Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without being explicitly programmed. This article provides an to machine learning for those with a background in applied mathematics."> <meta name="twitter:title" content="Machine Learning: An Applied Mathematics "> <meta name="twitter:image" content="https://example.com/image.jpg"> Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without being explicitly programmed. This is done by providing the computer with a set of data and allowing it to find patterns and relationships in the data. The computer can then use these patterns to make predictions or decisions. Machine learning has a wide range of applications, including: * **Predictive analytics:** Predicting future events based on historical data. * **Classification:** Identifying patterns in data and assigning labels to data points. * **Clustering:** Grouping data points into similar groups. * **Recommendation systems:** Suggesting products or services to users based on their past behavior. <h2>Types of Machine Learning</h2> There are two main types of machine learning: * **Supervised learning:** The computer is provided with a set of labeled data. The computer then learns the relationship between the input data and the output labels. * **Unsupervised learning:** The computer is provided with a set of unlabeled data. The computer then finds patterns and relationships in the data without being told what to look for. <h2>Applications of Machine Learning to Applied Mathematics</h2> Machine learning has a wide range of applications to applied mathematics, including: * **Solving differential equations:** Machine learning can be used to solve differential equations that are too complex to solve analytically. * **Optimizing functions:** Machine learning can be used to find the optimal values of functions that are too complex to optimize analytically. * **Data analysis:** Machine learning can be used to analyze large datasets and find patterns and relationships that would be difficult to find manually. Machine learning is a powerful tool that can be used to solve a wide range of problems in applied mathematics. As the field of machine learning continues to grow, we can expect to see even more applications of machine learning to applied mathematics in the future.

Machine Learning: An Applied Mathematics Introduction

by Paul Wilmott

4.5 out of 5

Language	:	English
File size	:	19222 KB
Screen Reader	:	Supported
Print length	:	290 pages

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