Tom Mitchell Machine Learning Solution Manual.zip
machine learning is a term that describes a variety of technologies that can be used to extract information from data, to develop models, and to adapt those models to new situations. some of the more commonly known techniques are:
- supervised learning
– in which an algorithm learns to classify a training set into one of its categories
- unsupervised learning
– in which it learns to cluster the training set into groups or categories
- reinforcement learning
– in which an algorithm learns to generate the optimal decision for each training example
- neural networks
– in which an algorithm simulates the process of neurons in the brain. the term neural network is used in two senses:
machine learning is a field of computer science and artificial intelligence. it involves the study of algorithms that are designed to extract patterns from data and make decisions. machine learning is mainly used for two purposes: prediction and optimization. prediction means that the machine is asked to predict the outcome of an event. for example, the machine might be asked to predict the weight of a baby based on the mother’s weight and the gestation of the baby. this is also referred to as the “real-world problem”.
in order to train a machine, we must provide it with examples of the desired output and the examples of the input. this process is called “training” the machine, and the goal is to create a computer program that will learn to produce the desired output given the desired input. this program is called a “learning algorithm”. after training, the learning algorithm is then used to make predictions about unseen inputs, or to be used to solve optimization problems.
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