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Introduction to Machine Learning
Aimed at undergraduate students, this text guides readers through the methods and principles of machine learning in an approachable manner without sacrificing mathematical precision or notation. Step-by-step explanations allow students to grasp complicated mathematical calculations and translate the theory and mathematics into programming and applications. The text presents machine learning concepts visually, and uses example datasets from fictional hippopotamuses and illustrations to explain the material in a unique, but easily understood and engaging way. Worked examples connect the mathematics and algorithms to real-world applications and enable students to utilize this technology in new and ever-changing circumstances. Topics covered include Bayesian reasoning, linear regression and classification, margin-based classification, cross-validation, neural networks, decision trees, clustering and dimensionality reduction. End-of-chapter mathematical exercises and additional coding projects reinforce application and decision-making skills.
R 1,547.00
Hands-On Introduction to Machine Learning
Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
R 2,591.00
Practical Introduction to Computer Vision with OpenCV
Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV librariesOffers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issuesProvides enough material for a one semester course in computer vision at senior undergraduate and Masters levels Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in imagesContains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook
R 2,235.00
Introduction to Machine Learning
Aimed at undergraduate students, this text guides readers through the methods and principles of machine learning in an approachable manner without sacrificing mathematical precision or notation. Step-by-step explanations allow students to grasp complicated mathematical calculations and translate the theory and mathematics into programming and applications. The text presents machine learning concepts visually, and uses example datasets from fictional hippopotamuses and illustrations to explain the material in a unique, but easily understood and engaging way. Worked examples connect the mathematics and algorithms to real-world applications and enable students to utilize this technology in new and ever-changing circumstances. Topics covered include Bayesian reasoning, linear regression and classification, margin-based classification, cross-validation, neural networks, decision trees, clustering and dimensionality reduction. End-of-chapter mathematical exercises and additional coding projects reinforce application and decision-making skills.
R 3,829.00
R 580.00
R 1,222.00
R 489.00
R 977.00
Computer Vision and Image Processing
This two volume set (CCIS 1776-1777) constitutes the refereed proceedings of the 7th International Conference on Computer Vision and Image Processing, CVIP 2022, held in Nagpur, India, November 4–6, 2022.The 110 full papers and 11 short papers were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical Image Analysis, Image/ Video Processing for Autonomous Vehicles, Activity Detection/ Recognition, Human Computer Interaction, Segmentation and Shape Representation, Motion and Tracking, Image/ Video Scene Understanding, Image/Video Retrieval, Remote Sensing, Hyperspectral Image Processing, Face, Iris, Emotion, Sign Language and Gesture Recognition, etc.
R 7,783.45
Computer Vision and Image Processing
This two volume set (CCIS 1776-1777) constitutes the refereed proceedings of the 7th International Conference on Computer Vision and Image Processing, CVIP 2022, held in Nagpur, India, November 4–6, 2022.The 110 full papers and 11 short papers were carefully reviewed and selected from 307 submissions. Out of 121 papers, 109 papers are included in this book. The topical scope of the two-volume set focuses on Medical Image Analysis, Image/ Video Processing for Autonomous Vehicles, Activity Detection/ Recognition, Human Computer Interaction, Segmentation and Shape Representation, Motion and Tracking, Image/ Video Scene Understanding, Image/Video Retrieval, Remote Sensing, Hyperspectral Image Processing, Face, Iris, Emotion, Sign Language and Gesture Recognition, etc.
R 5,111.77
Introduction to Online Control
This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.
R 2,139.00
R 4,559.00
Computer Vision: A Modern Approach
Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
R 2,188.00
Introduction to Color Imaging Science
Colour imaging technology has become almost ubiquitous in modern life in the form of monitors, liquid crystal screens, colour printers, scanners, and digital cameras. This book is a comprehensive guide to the scientific and engineering principles of colour imaging. It covers the physics of light and colour, how the eye and physical devices capture colour images, how colour is measured and calibrated, and how images are processed. It stresses physical principles and includes a wealth of real-world examples. The book will be of value to scientists and engineers in the colour imaging industry and, with homework problems, can also be used as a text for graduate courses on colour imaging.
R 5,236.01
R 226.00
Introduction to Color Imaging Science
Colour imaging technology has become almost ubiquitous in modern life in the form of monitors, liquid crystal screens, colour printers, scanners, and digital cameras. This book is a comprehensive guide to the scientific and engineering principles of colour imaging. It covers the physics of light and colour, how the eye and physical devices capture colour images, how colour is measured and calibrated, and how images are processed. It stresses physical principles and includes a wealth of real-world examples. The book will be of value to scientists and engineers in the colour imaging industry and, with homework problems, can also be used as a text for graduate courses on colour imaging.
R 7,723.60
