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Goyal Brothers Prakashan
Since its publication, Essentials of Artificial Intelligence has been adopted at numerous universities and colleges offering introductory AI courses at the graduate and undergraduate levels. Based on the author's course at Stanford University, the book is an integrated, cohesive introduction to the field. The author has a fresh, entertaining writing style that combines clear presentations with humor and AI anecdotes. At the same time, as an active AI researcher, he presents the material authoritatively and with insight that reflects a contemporary, first hand understanding of the field. Pedagogically designed, this book offers a range of exercises and examples.
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
The book featuring “AI BASICS FOR SCHOOL STUDENTS” targets learning of concepts as prescribed by the CBSE. The objective of the module is to develop a readiness for understanding and appreciating Artificial Intelligence and its application in our lives. The units dwelled include Excite, Relate, Purpose, Possibilities and AI Ethics which are set to empower the kids to identify and appreciate AI and describe its applications in daily life and to apply and reflect on the Human-Machine Interactions.
Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Touchpad AI series has some salient features such as AI Game, AI Lab. KEY FEATURES (5-7 points)(each point should be 70 characters with space)(to be filled by author) ● National Education Policy 2020 ● AI Game: It contains an interesting game or activity for the students. ● AI Lab: It contains questions to improve practical skills. ● Brainy Fact: It is an interesting fact relevant to the topic. ● AI Glossary: This section contains definition of important AI terms. ● Digital Solutions DESCRIPTION Touchpad Artificial Intelligence series has some salient features such as AI Reboot, AI Deep Thinking, AI in Life, AI Lab and AI Ready which ensures that NEP 2020 guidelines are followed. The series is written keeping in mind about the future and scope that lies in Artificial Intelligence. The knowledge is spread in a phased manner so that at no age the kid finds it difficult to understand the theory. There are some brainstorming activities in the form of AI Task in between the topics to ensure that students give pause to their learning and use their skills to reach to some creative ideas in solving given problems. Every chapter has competency based questions as guided by CBSE to ensure that students are capable of applying their learning to solve some real-life challenges. There are plenty of Video Sessions for students and teachers to go beyond the syllabus and enrich their knowledge. WHAT WILL YOU LEARN You will learn about: ● Communication skills ● Management skills ● Fundamentals of computers ● ICT Tools ● Entrepreneurship ● Green Skills ● Introduction to AI ● Computer vision ● Natural Language Processing ● Data Science ● AI Project Cycle ● Advance Python WHO THIS BOOK IS FOR Grade 10 TABLE OF CONTENTS 1. Part A Employability Skills a. Unit-1 Communication Skills-II b. Unit-2 Self Management Skills-II c. Unit-3 ICT Skills-II d. Unit-4 Entrepreneurial Skills-II e. Unit-5 Green Skills-II 2. Part B Subject Specific Skills a. Unit-1 Introduction to AI b. Unit-2 AI Project Cycle c. Unit-3 Advance Python d. Unit-4 Data Science e. Unit-5 Computer Vision f. Unit-6 Natural Language Processing g. Unit-7 Evaluation 3. Part C Practical Work a. Python Practical Questions b. Viva Voce Questions 4. Projects 5. AI Glossary 6. AI Innovators 7. CBSE Sample Question Paper
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.
A concise, practical introduction to artificial intelligence, this title starts with the fundamentals of knowledge representation, inference, expert systems, natural language processing, machine learning, neural networks, agents, robots, and much more. Examples and algorithms are presented throughout, and the book includes a complete glossary.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.