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AI tools also personalize our learning by enabling us to focus on our weak points. Another AI tool may help a bank
detect fraudulent transactions. Barring an occasional error, AI technology successfully filters out spam emails from
our mailboxes. Thus, we note that AI covers several aspects of our lives, such as healthcare, finance, transportation,
education, and entertainment.
However, AI also raises ethical and societal concerns, such as the displacement of people from jobs, privacy, and safety.
It is important for AI developers and researchers to address these concerns and design AI systems that are ethical,
transparent, and accountable.
Artificial intelligence (AI) is a branch of computer science that aims to make machines so intelligent that they can
perform tasks that typically require human intelligence.
10.1.1 Machine Learning
Machine learning (ML) is a subdomain of artificial intelligence (AI) that involves the use of statistical and specialized
algorithms to enable computer systems to learn and improve from experience without being explicitly programmed.
In other words, machine learning systems use data to learn and make decisions rather than relying on pre-determined
rules or instructions.
Machine Learning Process
Training Data Algorithm Learning Trained model Results
Machine Learning Process
There are three main approaches to machine learning: supervised learning, unsupervised learning, and reinforcement
learning. In the machine learning community, a learner is called a model or an agent.
Supervised learning involves training a model on labelled data to make predictions or classifications based on new,
unlabelled data. In supervised learning, the goal is to learn a function that maps input data to output data based on
example input-output pairs. The labelled data used to train the model consists of input-output pairs, where the input is
a feature vector and the output is a label or target variable. The model learns from these input-output pairs by finding
a function that approximates the relationship between the input and output variables. This function can then be used
to make predictions or classifications on new, unlabelled data. For example, suppose we have a dataset of images of
handwritten digits, where each image is labelled with the corresponding digit (0-9). We can use this dataset to train
a supervised learning model to recognize digits in new, unseen images. We might use an algorithm such as logistic
regression or a neural network to learn a mapping between the input images and their corresponding labels. During
the training phase, the model adjusts its parameters to minimize the difference between its predicted output and the
true output labels. This is typically done using an optimization algorithm such as gradient descent. Once the model has
been trained, it can be used to predict the labels of new, unseen images by applying the learned function to the input
features of those images.
Unsupervised learning involves training a model on unlabelled data to identify patterns or relationships in the
data. Reinforcement learning involves training a model to make decisions based on feedback received from the
environment.
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