The Mathematics of Machine Learning: Lectures on Supervised Meth
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In this post, we will explore the mathematics behind supervised machine learning methods. Supervised learning is a type of machine learning where the algorithm learns from labeled training data to make predictions or decisions. It is often used in tasks such as classification and regression.
To understand the mathematics behind supervised learning, it is important to first understand some key concepts such as linear algebra, calculus, and probability theory. These concepts form the foundation of many machine learning algorithms and are essential for understanding how they work.
One of the most commonly used supervised learning algorithms is linear regression. This algorithm is used to model the relationship between a dependent variable and one or more independent variables. The goal of linear regression is to find the best-fitting line that minimizes the sum of squared errors between the predicted values and the actual values.
Another popular supervised learning algorithm is logistic regression, which is used for binary classification tasks. Logistic regression models the probability that a given input belongs to a particular class. The algorithm uses a logistic function to map the input variables to a binary output.
Support vector machines (SVMs) are another commonly used supervised learning algorithm. SVMs are used for both classification and regression tasks and work by finding the hyperplane that best separates the data into different classes.
Overall, understanding the mathematics behind supervised learning algorithms is crucial for building and interpreting machine learning models. By gaining a strong foundation in linear algebra, calculus, and probability theory, you can better understand how these algorithms work and make informed decisions when applying them to real-world problems.
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