• Title: Machine Learning
• Author(s) Abdelhamid Mellouk and Abdennacer Chebira
• Publisher: InTech; eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover/Paperback 450 pages
• eBook: PDF Files
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-953-7619-56-1
• Share This:  

Book Description

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

• N/A

• Amazon

• Machine Learning
• Artificial Intelligence
• Data Analysis and Data Mining
• Neural Networks
• Statistics, R Language and SAS Programming
• Operations Research (OR), Linear Programming, Optimization, and Approximation

• Machine Learning (Abdelhamid Mellouk, et al.)
• PDF Format
• Machine Learning And Its Applications (Peter Wlodarczak)

• Foundations of Large Language Models (Tong Xiao, et al.)

  This is a book about Large Language Models (LLM). It primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies: pre-training, generative models, prompting techniques, and alignment methods.

• Linear Algebra for Computer Vision, Robotics, and Machine Learning

  This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.

• Machine Learning in Production: From Models to Products

  This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Packed with real-world examples that take you from start to finish.

• Interpretable Machine Learning: Black Box Models Explainable

  This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

• Multi-Agent Reinforcement Learning (Stefano V. Albrecht, et al.)

  The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

• Distributional Reinforcement Learning (Marc G. Bellemare, et al)

  Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

• Mathematical Analysis of Machine Learning Algorithms (Tong Zhang)

  This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

• Machine Learning Algorithms (Jason Brownlee)

  This book takes you on an enlightening journey through the fascinating world of machine learning, helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs.

• The Hundred-Page Machine Learning Book (Andriy Burkov)

  Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.