Machine learning
Material type: TextPublication details: MIT Press Cambridge 2021Description: xix, 255 pISBN:- 9780262542524
- 006.31 ALP
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | |
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Book | Indian Institute of Management LRC General Stacks | IT & Decisions Sciences | 006.31 ALP (Browse shelf(Opens below)) | 1 | Available | 003733 |
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006.3068 HAQ Enterprise artificial intelligence transformation: a playbook for the next generation of business and technology leaders | 006.31 AGG Machine learning for text | 006.31 ALB Data science in practice | 006.31 ALP Machine learning | 006.31 BER Mathematics of deep learning: an introduction | 006.31 BRI Real-world machine learning | 006.31 CHE Generalization with deep learning: |
A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.
Alpaydin, author of a popular textbook on machine learning, explains that as “Big Data” has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.
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