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| artificial intelligence notes for bca: Introduction to Machine Learning Ethem Alpaydin, 2014-08-22 Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments. |
| artificial intelligence notes for bca: Artificial Intelligence Rajiv Chopra, 2012 For the students of B.E./B.Tech Computer Science Engineering and Information Technology (CSE/IT) |
| artificial intelligence notes for bca: The Quest for Artificial Intelligence Nils J. Nilsson, 2009-10-30 Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries. |
| artificial intelligence notes for bca: Introduction to Artificial Intelligence and Expert Systems Dan W. Patterson, 1990 |
| artificial intelligence notes for bca: Information Theory, Inference and Learning Algorithms David J. C. MacKay, 2003-09-25 Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning. |
| artificial intelligence notes for bca: Artificial Intelligence Stuart Jonathan Russell, Peter Norvig, 2013-07-31 In this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises. |
| artificial intelligence notes for bca: Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016-11-18 An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. |
| artificial intelligence notes for bca: Search in Artificial Intelligence Leveen Kanal, Vipin Kumar, 1988-06-24 |
| artificial intelligence notes for bca: Contemporary Knowledge Engineering and Cognition Franz Schmalhofer, Gerhard Strube, 1992-08-12 This book has its source in the question of whether any knowledge engineering tools can be applied or analyzed in cognition research and what insights and methods of cognitive science might be relevant for knowledge engineers. It presents the proceedings of a workshop organized by the Special Interest Groups Cognition and Knowledge Engineering of the German Society for Informatics, held in February 1992 in Kaiserslautern. The book is structured into three parts. The first part contrasts work in knowledge engineering with approaches from the side of the soft sciences. The second part deals with case-based approaches in expert systems. Cognition research and the cognitive adequacy of expert systems are discussed in the third part. Contributions from Canada, England, France, Switzerland, and the USA demonstrate how knowledge engineering and cognitive science are woven together internationally. |
| artificial intelligence notes for bca: R.U.R. Karel Čapek, 2023-03-28 We rely on your support to help us keep producing beautiful, free, and unrestricted editions of literature for the digital age. Will you support our efforts with a donation? R.U.R., or Rossum’s Universal Robots is a play written in 1920 by Karel Čapek, a Czech writer who wrote many plays and novels, many of them with science-fiction and dystopian themes. R.U.R. is perhaps the most well-known of these works in the English-speaking world because it brought the word “robot” into the language. “Robot” is derived from the Czech word meaning “worker.” The play is set in the island headquarters of the R.U.R. corporation. The corporation has been manufacturing artificial beings which resemble humans, but who are tireless workers. They can be mass-produced in large numbers and are being adopted as workers in many countries. In the first scene of the play, they are visited by a young woman, Helena Glory, who aspires to relieve the lot of the robots, who she sees as oppressed. However, in what must be the fastest seduction scene in all drama, she is wooed and agrees to marry Harry Domin, the factory manager, who she has just met. She still however aspires to improve the life of robots and find a way to give them souls. Ultimately, however, this admirable desire leads to disaster for humankind. The play was translated into English, and slightly abridged, by Paul Selver and Nigel Playfair in 1923. This version quickly became popular with both British and American audiences and was well received by critics. |
| artificial intelligence notes for bca: Machine Learning Rajiv Chopra, This book attempts to provide a unified overview of the broad field of Machine Learning and its Practical implementation. This book is a survey of the state of art. It breaks this massive subject into comprehensible parts piece by piece. The objective is to focus on basic principles of machine learning with some leading edge topics. This book addresses a full spectrum of machine learning programming. The emphasis is to solve lot many programming examples using step-by step practical implementation of machine learning algorithms. To facilitate easy understanding of machine learning, this book has been written in such a simple style that a student thinks as if a teacher is sitting behind him and guiding him. This book is written as per the new syllabus of different Universities of India. It also Cover the syllabus of B.Tech.(CSE/IT), MCA, BCA of Delhi University, Delhi. GGSIPU, MDU, RGTU, Nagpur University, UTU, APJ Abdul Kalam University so on. The book is intended for both academic and professional audience. |
| artificial intelligence notes for bca: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| artificial intelligence notes for bca: Artificial Intelligence Patrick Henry Winston, Richard Henry Brown, 1979 |
| artificial intelligence notes for bca: PRINCIPLES OF SOFT COMPUTING (With CD ) S.N.Sivanandam & S.N.Deepa, 2007-06 Market_Desc: · B. Tech (UG) students of CSE, IT, ECE· College Libraries· Research Scholars· Operational Research· Management Sector Special Features: Dr. S. N. Sivanandam has published 12 books· He has delivered around 150 special lectures of different specialization in Summer/Winter school and also in various Engineering colleges· He has guided and co guided 30 PhD research works and at present 9 PhD research scholars are working under him· The total number of technical publications in International/National Journals/Conferences is around 700· He has also received Certificate of Merit 2005-2006 for his paper from The Institution of Engineers (India)· He has chaired 7 International Conferences and 30 National Conferences. He is a member of various professional bodies like IE (India), ISTE, CSI, ACS and SSI. He is a technical advisor for various reputed industries and engineering institutions· His research areas include Modeling and Simulation, Neural Networks, Fuzzy Systems and Genetic Algorithm, Pattern Recognition, Multidimensional system analysis, Linear and Nonlinear control system, Signal and Image processing, Control System, Power system, Numerical methods, Parallel Computing, Data Mining and Database Security About The Book: This book is meant for a wide range of readers who wish to learn the basic concepts of soft computing. It can also be helpful for programmers, researchers and management experts who use soft computing techniques. The basic concepts of soft computing are dealt in detail with the relevant information and knowledge available for understanding the computing process. The various neural network concepts are explained with examples, highlighting the difference between various architectures. Fuzzy logic techniques have been clearly dealt with suitable examples. Genetic algorithm operators and the various classifications have been discussed in lucid manner, so that a beginner can understand the concepts with minimal effort. |
| artificial intelligence notes for bca: Choosing Chinese Universities Alice Y.C. Te, 2022-10-07 This book unpacks the complex dynamics of Hong Kong students’ choice in pursuing undergraduate education at the universities of Mainland China. Drawing on an empirical study based on interviews with 51 students, this book investigates how macro political/economic factors, institutional influences, parental influence, and students’ personal motivations have shaped students’ eventual choice of university. Building on Perna’s integrated model of college choice and Lee’s push-pull mobility model, this book conceptualizes that students’ border crossing from Hong Kong to Mainland China for higher education is a trans-contextualized negotiated choice under the One Country, Two Systems principle. The findings reveal that during the decision-making process, influencing factors have conditioned four archetypes of student choice: Pragmatists, Achievers, Averages, and Underachievers. The book closes by proposing an enhanced integrated model of college choice that encompasses both rational motives and sociological factors, and examines the theoretical significance and practical implications of the qualitative study. With its focus on student choice and experiences of studying in China, this book’s research and policy findings will interest researchers, university administrators, school principals, and teachers. |
| artificial intelligence notes for bca: Artificial Intelligence George F. Luger, 2011-11-21 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI. In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Readers learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists. |
| artificial intelligence notes for bca: AI and Big Data on IBM Power Systems Servers Scott Vetter, Ivaylo B. Bozhinov, Anto A John, Rafael Freitas de Lima, Ahmed.(Mash) Mashhour, James Van Oosten, Fernando Vermelho, Allison White, IBM Redbooks, 2019-04-10 As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names. |
| artificial intelligence notes for bca: Artificial Intelligence and Soft Computing – ICAISC 2006 Leszek Rutkowski, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek Zurada, 2006-06-27 This book constitutes the refereed proceedings of the 8th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2006, held in Zakopane, Poland, in June 2006. The 128 revised contributed papers presented are organized in topical sections on neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, rough sets, classification and clustering, image analysis and robotics, bioinformatics and medical applications, various problems of artificial intelligence. |
| artificial intelligence notes for bca: Introduction to Artificial Intelligence Philip C. Jackson, 1974 This book is intended to be a comprehensive introduction to the field of artificial intelligence, written primarily for the student who has some knowledge of computers and mathematics (say, at the junior or senior levels of college). The subjects for discussion are machines that can solve problems, play games, recognize patters, prove mathematical theorems, understand English, and even demonstrate learning, by changing their own behavior so as to perform such tasks more successfully. In general, this book is addressed to all person who are interested in studying the nature of thought, and hopefully much of it can be read without previous, formal exposure to mathematics and computers. |
| artificial intelligence notes for bca: Computational Intelligence Andries P. Engelbrecht, 2007-10-22 Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. Engelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library. Key features of this second edition include: A tutorial, hands-on based presentation of the material. State-of-the-art coverage of the most recent developments in computational intelligence with more elaborate discussions on intelligence and artificial intelligence (AI). New discussion of Darwinian evolution versus Lamarckian evolution, also including swarm robotics, hybrid systems and artificial immune systems. A section on how to perform empirical studies; topics including statistical analysis of stochastic algorithms, and an open source library of CI algorithms. Tables, illustrations, graphs, examples, assignments, Java code implementing the algorithms, and a complete CI implementation and experimental framework. Computational Intelligence: An Introduction, Second Edition is essential reading for third and fourth year undergraduate and postgraduate students studying CI. The first edition has been prescribed by a number of overseas universities and is thus a valuable teaching tool. In addition, it will also be a useful resource for researchers in Computational Intelligence and Artificial Intelligence, as well as engineers, statisticians, operational researchers, and bioinformaticians with an interest in applying AI or CI to solve problems in their domains. Check out http://www.ci.cs.up.ac.za for examples, assignments and Java code implementing the algorithms. |
| artificial intelligence notes for bca: Introduction to Artificial Intelligence Simplilearn, 2020-12-14 This AI beginner’s guide aims to take the readers through the current AI landscape, provides the key fundamentals and terminologies of AI, and offers practical guidelines on why and how you can be a part of the AI revolution, and also the ways in which you can scale up your AI career. |
| artificial intelligence notes for bca: Logics in Artificial Intelligence Steffen Hölldobler, Carsten Lutz, Heinrich Wansing, 2008-09-25 This book constitutes the refereed proceedings of the 11th European Conference on Logics in Artificial Intelligence, JELIA 2008, held in Dresden, Germany, Liverpool, in September/October 2008. The 32 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 98 submissions. The papers cover a broad range of topics including belief revision, description logics, non-monotonic reasoning, multi-agent systems, probabilistic logic, and temporal logic. |
| artificial intelligence notes for bca: Advances in Artificial Intelligence Leila Kosseim, Diana Inkpen, 2012-05-26 This book constitutes the refereed proceedings of the 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, held in Toronto, Canada, in May 2012. The 23 regular papers, 16 short papers, and 4 papers from the Graduate Student Symposium presented were carefully reviewed and selected for inclusion in this book. The papers cover a broad range of topics presenting original work in all areas of artificial intelligence, either theoretical or applied. |
| artificial intelligence notes for bca: Artificial Immune Systems Giuseppe Nicosia, 2004-09-07 This book constitutes the refereed proceedings of the Third International Conference on Artificial Immune Systems, ICARIS 2004, held in Catania, Sicily, Italy, in September 2004. The 34 revised full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on applications of artificial immune systems; conceptual, formal, and theoretical frameworks; artificial immune systems for robotics; emerging metaphors; immunoinformatics; theoretical and experimental studies; future applications; networks; modeling; and distinguishing properties of artificial immune systems. |
| artificial intelligence notes for bca: Python Machine Learning Projects Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan, 2019-05-02 As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions — sometimes without final input from humans who may be impacted by these findings — it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don’t have one already, then provide you with a conceptual understanding of machine learning in the chapter “An Introduction to Machine Learning.” What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. |
| artificial intelligence notes for bca: Foundations of Artificial Intelligence David Kirsh, 1992 In the 11 contributions, theorists historically associated with each position identify the basic tenets of their position.Have the classical methods and ideas of AI outlived their usefulness? Foundations of Artificial Intelligence critically evaluates the fundamental assumptions underpinning the dominant approaches to AI. In the 11 contributions, theorists historically associated with each position identify the basic tenets of their position. They discuss the underlying principles, describe the natural types of problems and tasks in which their approach succeeds, explain where its power comes from, and what its scope and limits are. Theorists generally skeptical of these positions evaluate the effectiveness of the method or approach and explain why it works - to the extent they believe it does - and why it eventually fails.ContentsFoundations of AI: The Big Issues, D. Kirsh - Logic and Artificial Intelligence, N. J. Nilsson - Rigor Mortis: A Response to Nilsson's 'Logic and Artificial Intelligence, ' L. Birnbaum - Open Information Systems Semantics for Distributed Artificial Intelligence, C. Hewitt - Social Conceptions of Knowledge and Action: DAI Foundations and Open Systems Semantics, L. Gasser - Intelligence without Representation, R. A. Brooks - Today the Earwig, Tomorrow Man? D. Kirsh - On the Thresholds of Knowledge, D. B. Lenat, E. A. Feigenbaum - The Owl and the Electric Encyclopedia, B. C. Smith - A Preliminary Analysis of the Soar Architecture as a Basis for General Intelligence, P. S. Rosenbloom, J. E. Laird, A. Newell, R. McCarl - Approaches to the Study of Intelligence, D. A. Norman |
| artificial intelligence notes for bca: Fundamentals of Information Systems Ralph Stair, George Reynolds, 2015-01-01 Equipping you with a solid understanding of the core principles of IS and how it is practiced, the brief FUNDAMENTALS OF INFORMATION SYSTEMS, 8E covers the latest developments from the field and their impact on the rapidly changing role of today's IS professional. A concise nine chapters, this streamlined book includes expansive coverage of mobile solutions, energy and environmental concerns, cloud computing, IS careers, virtual communities, global IS work solutions, and social networking. You learn firsthand how information systems can increase profits and reduce costs as you explore new information on e-commerce and enterprise systems, artificial intelligence, virtual reality, green computing, and other issues reshaping the industry. The book also introduces the challenges and risks of computer crimes, hacking, and cyberterrorism. A long-running example illustrates how technology was used in the design, development, and production of this book. No matter where your career path may lead, FUNDAMENTALS OF INFORMATION SYSTEMS, 8E can help you maximize your success as an employee, a decision maker, and a business leader. |
| artificial intelligence notes for bca: Machine Learning with the Raspberry Pi Donald J. Norris, 2019-11-29 Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable. Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been touched by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, you’ll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and computer vision. What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects. What You'll Learn Acquire a working knowledge of current ML Use the Raspberry Pi to implement ML techniques and algorithms Apply AI and ML tools and techniques to your own work projects and studies Who This Book Is For Engineers and scientists but also experienced makers and hobbyists. Motivated high school students who desire to learn about ML can benefit from this material with determination. |
| artificial intelligence notes for bca: Discrete Mathematics for Computer Science John Schlipf, Sue Whitesides, Gary Haggard, 2020-09-22 Discrete Mathematics for Computer Science by Gary Haggard , John Schlipf , Sue Whitesides A major aim of this book is to help you develop mathematical maturity-elusive as thisobjective may be. We interpret this as preparing you to understand how to do proofs ofresults about discrete structures that represent concepts you deal with in computer science.A correct proof can be viewed as a set of reasoned steps that persuade another student,the course grader, or the instructor about the truth of the assertion. Writing proofs is hardwork even for the most experienced person, but it is a skill that needs to be developedthrough practice. We can only encourage you to be patient with the process. Keep tryingout your proofs on other students, graders, and instructors to gain the confidence that willhelp you in using proofs as a natural part of your ability to solve problems and understandnew material. The six chapters referred to contain the fundamental topics. Thesechapters are used to guide students in learning how to express mathematically precise ideasin the language of mathematics.The two chapters dealing with graph theory and combinatorics are also core materialfor a discrete structures course, but this material always seems more intuitive to studentsthan the formalism of the first four chapters. Topics from the first four chapters are freelyused in these later chapters. The chapter on discrete probability builds on the chapter oncombinatorics. The chapter on the analysis of algorithms uses notions from the core chap-ters but can be presented at an informal level to motivate the topic without spending a lot oftime with the details of the chapter. Finally, the chapter on recurrence relations primarilyuses the early material on induction and an intuitive understanding of the chapter on theanalysis of algorithms. The material in Chapters 1 through 4 deals with sets, logic, relations, and functions.This material should be mastered by all students. A course can cover this material at differ-ent levels and paces depending on the program and the background of the students whenthey take the course. Chapter 6 introduces graph theory, with an emphasis on examplesthat are encountered in computer science. Undirected graphs, trees, and directed graphsare studied. Chapter 7 deals with counting and combinatorics, with topics ranging from theaddition and multiplication principles to permutations and combinations of distinguishableor indistinguishable sets of elements to combinatorial identities.Enrichment topics such as relational databases, languages and regular sets, uncom-putability, finite probability, and recurrence relations all provide insights regarding howdiscrete structures describe the important notions studied and used in computer science.Obviously, these additional topics cannot be dealt with along with the all the core materialin a one-semester course, but the topics provide attractive alternatives for a variety of pro-grams. This text can also be used as a reference in courses. The many problems provideample opportunity for students to deal with the material presented. |
| artificial intelligence notes for bca: IGNOU BCA MCS 014 System Analysis and Design Previous Years Solved Papers Manish Soni, 2024-11-30 The field of System Analysis and Design is a fundamental area within the world of information systems, acting as a blueprint for developing robust, efficient, and scalable software solutions. As organisations increasingly rely on complex information systems to streamline operations, the demand for professionals skilled in analysing and designing these systems is at an all-time high. Recognising the critical importance of this discipline, the Indira Gandhi National Open University (IGNOU) has made System Analysis and Design a key component of its curriculum, challenging students to acquire both theoretical knowledge and practical skills. This book, IGNOU System Analysis and Design Previous Years Solved Papers (MCS-014), is a meticulously curated compilation of unsolved question papers from previous years. It is designed to serve as an essential resource for students preparing for their exams in this subject. The primary objective of this book is to provide students with a comprehensive tool to self-assess their understanding, identify areas for improvement, and enhance their problem-solving abilities. We believe that practising with previous years’ question papers is one of the most effective ways to prepare for exams. This approach not only familiarises students with the types and formats of questions they are likely to encounter but also deepens their comprehension of the subject by applying theoretical concepts to practical scenarios. By working through these unsolved papers, students will be able to evaluate their readiness, improve their time management during exams, and build confidence in tackling complex questions. |
| artificial intelligence notes for bca: Principles of Artificial Intelligence Nils J. Nilsson, 1982-05-01 Previous treatments of Artificial Intelligence (AI) divide the subject into its major areas of application, namely, natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, intelligent data retrieval systems, etc. The major difficulty with this approach is that these application areas are now so extensive, that each could, at best, be only superficially treated in a book of this length. Instead, I have attempted here to describe fundamental AI ideas that underlie many of these applications. My organization of these ideas is not, then, based on the subject matter of their application, but is, instead, based on general computational concepts involving the kinds of data structures used, the types of operations performed on these data struc tures, and the properties of con'trol strategies used by AI systems. I stress, in particular, the important roles played in AI by generalized production systems and the predicate calculus. The notes on which the book is based evolved in courses and seminars at Stanford University and at the University of Massachusetts at Amherst. Although certain topics treated in my previous book, Problem solving Methods in Artificial Intelligence, are covered here as well, this book contains many additional topics such as rule-based systems, robot problem-solving systems, and structured-object representations. |
| artificial intelligence notes for bca: Introduction to Logic Programming Michael Genesereth, Vinay K. Chaudhri, 2022-06-01 Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming due to applications in deductive databases, automated worksheets, Enterprise Management (business rules), Computational Law, and General Game Playing. This book introduces Logic Programming theory, current technology, and popular applications. In this volume, we take an innovative, model-theoretic approach to logic programming. We begin with the fundamental notion of datasets, i.e., sets of ground atoms. Given this fundamental notion, we introduce views, i.e., virtual relations; and we define classical logic programs as sets of view definitions, written using traditional Prolog-like notation but with semantics given in terms of datasets rather than implementation. We then introduce actions, i.e., additions and deletions of ground atoms; and we define dynamic logic programs as sets of action definitions. In addition to the printed book, there is an online version of the text with an interpreter and a compiler for the language used in the text and an integrated development environment for use in developing and deploying practical logic programs. |
| artificial intelligence notes for bca: The Master Algorithm Pedro Domingos, 2015-09-22 Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible. |
| artificial intelligence notes for bca: Mathematics for Computer Science Eric Lehman, F. Thomson Leighton, Albert R. Meyer, 2017-06-05 This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. The color images and text in this book have been converted to grayscale. |
| artificial intelligence notes for bca: Oxford English for Computing Keith Boeckner, P. Charles Brown, 1993 Helps students to combine their knowledge of English with their technical knowledge. Develops all four skills through varied activities, with special emphasis on vocabulary acquisition and grammatical accuracy. Up-to-date technical content. Authentic reading and listening passages covering a wide range of topics, e.g. the use of virtual reality in industry, personal computing, viruses and security, information systems, and multimedia. Letter-writing section offering a complete guideto writing simple, work-related letters. Comprehensive glossary of technical terms which forms a useful mini-dictionary of computing terminology. Separate Answer Book with a key to all exercises, the tapescripts, and useful unit-by-unit teaching notes. Designed for easy use by the non-specialistteacher. |
| artificial intelligence notes for bca: Algorithmic Learning Theory Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann, 2003-06-29 This volume contains all the papers presented at the Ninth International Con- rence on Algorithmic Learning Theory (ALT’98), held at the European education centre Europ ̈aisches Bildungszentrum (ebz) Otzenhausen, Germany, October 8{ 10, 1998. The Conference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI) and the University of Kaiserslautern. Thirty-four papers on all aspects of algorithmic learning theory and related areas were submitted, all electronically. Twenty-six papers were accepted by the program committee based on originality, quality, and relevance to the theory of machine learning. Additionally, three invited talks presented by Akira Maruoka of Tohoku University, Arun Sharma of the University of New South Wales, and Stefan Wrobel from GMD, respectively, were featured at the conference. We would like to express our sincere gratitude to our invited speakers for sharing with us their insights on new and exciting developments in their areas of research. This conference is the ninth in a series of annual meetings established in 1990. The ALT series focuses on all areas related to algorithmic learning theory including (but not limited to): the theory of machine learning, the design and analysis of learning algorithms, computational logic of/for machine discovery, inductive inference of recursive functions and recursively enumerable languages, learning via queries, learning by arti cial and biological neural networks, pattern recognition, learning by analogy, statistical learning, Bayesian/MDL estimation, inductive logic programming, robotics, application of learning to databases, and gene analyses. |
| artificial intelligence notes for bca: Connectome Sebastian Seung, 2012 The audacious effort to map the brain--and along with it mental afflictions, from autism to schizophrenia--by a rising star in neuroscience. |
| artificial intelligence notes for bca: Introduction To Algorithms Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, Clifford Stein, 2001 An extensively revised edition of a mathematically rigorous yet accessible introduction to algorithms. |
| artificial intelligence notes for bca: Book of R Tilman Davies M., 2016 |
| artificial intelligence notes for bca: INTRODUCTION TO DATA MINING WITH CASE STUDIES GUPTA, G.K., 2014-06-28 The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included. |
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
artificial - definition and meaning - Wordnik
Made or contrived by art, or by human skill and labor: opposed to natural: as, artificial heat or light; an artificial magnet. Made in imitation of or as a substitute for that which is natural or real: …
Artificial Definition & Meaning - YourDictionary
Made by human work or art, not by nature; not natural. Not arising from natural or necessary causes; contrived or arbitrary. Made in imitation of or as a substitute for something natural; …
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
artificial - definition and meaning - Wordnik
Made or contrived by art, or by human skill and labor: opposed to natural: as, artificial heat or light; an artificial magnet. Made in imitation of or as a substitute for that which is natural or real: …
Artificial Definition & Meaning - YourDictionary
Made by human work or art, not by nature; not natural. Not arising from natural or necessary causes; contrived or arbitrary. Made in imitation of or as a substitute for something natural; …
