Machine Learning - A Probabilistic Perspective PDF

PDF Name	Machine Learning – A Probabilistic Perspective
No. of Pages	1098
PDF Size	45.78 MB
Language	English
PDF Category	Education & Jobs
Source / Credits	noiselab.ucsd.edu
Uploaded By	Pradeep

Machine Learning – A Probabilistic Perspective - Summary

Machine learning is an exciting field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way humans learn, gradually improving accuracy. IBM has played a significant role in the growth of machine learning. This technology is widely used in internet search engines, email filters that sort spam, websites that provide personalized recommendations, banking software that detects unusual transactions, and various apps on our smartphones like voice recognition.

Understanding Machine Learning

Machine learning is a vital part of the expanding field of data science. By applying statistical methods, algorithms are trained to make classifications, predictions, and uncover important insights in data mining projects. These insights help in decision-making for applications and businesses, ideally affecting key growth metrics. As big data continues to grow, the demand for data scientists will rise, as they play a crucial role in identifying important business questions and the data needed to answer them.

Machine Learning is recognized as a technology that enables machines to perform a variety of actions, such as predictions, recommendations, and estimations, based on historical data or past experiences.

Machine Learning – A Probabilistic Perspective

There are three vital aspects of Machine Learning, which include:

Task: A task is defined as the main problem that we are interested in, such as predictions, recommendations, or estimations.
Experience: This term refers to learning from historical or past data that helps estimate and address future tasks.
Performance: This means the capability of any machine to solve machine learning tasks or problems and deliver the best outcomes, which can vary depending on the problem type.

Techniques in Machine Learning

1. Supervised Learning

Supervised learning is useful when a machine has sample data, meaning both input and output data come with correct labels. These labels help verify the model’s accuracy through tags and checks.

2. Unsupervised Learning

In unsupervised learning, a machine is trained using input samples without known outputs. In this case, the training data is neither classified nor labeled, which means the machine may not always provide correct outputs compared to supervised learning.

3. Reinforcement Learning

Reinforcement Learning is a type of machine learning that relies on feedback. In this approach, agents (computer programs) explore the environment, take actions, and receive rewards based on their choices. For every positive action, they earn rewards, while negative actions yield penalties. The goal of a reinforcement learning agent is to maximize positive rewards while learning solely from its experiences.

4. Semi-supervised Learning

Semi-supervised Learning is a blend of supervised and unsupervised learning techniques. It operates on datasets that contain both a few labeled examples and a lot of unlabeled data. Since labeling data can be expensive, this approach reduces costs, while still enhancing the accuracy and performance of machine learning models.

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