Focusing on linear algebra and optimization, the textbook is designed for graduate students and professors in computer science, mathematics, and data science, while also being accessible to advanced undergraduates. It includes numerous examples and exercises, with a solution manual available for instructors to aid in teaching. The structured chapters facilitate a comprehensive understanding of the material within the context of machine learning.
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Focusing on the theory and algorithms of deep learning, this book delves into the intricacies of neural networks, addressing critical questions about their functionality and effectiveness compared to traditional machine-learning models. It explores the challenges of training these networks and highlights common pitfalls. Rich in practical applications, the text covers diverse fields such as recommender systems, machine translation, image classification, and reinforcement learning, providing insights into the design of neural architectures tailored to various problems. The chapters are organized into three distinct categories for structured learning.
This book offers a comprehensive exploration of recommender systems, which deliver personalized suggestions for products or services based on users' past interactions. The methods discussed have been adapted for various applications, including query log mining, social networking, news recommendations, and computational advertising. The content is organized into three main categories: 1. **Algorithms and Evaluation**: This section covers fundamental algorithms, including collaborative filtering, content-based, knowledge-based, and ensemble methods, along with evaluation techniques. 2. **Recommendations in Specific Domains and Contexts**: Here, the importance of contextual information—such as temporal, spatial, social, tagging data, and trustworthiness—is examined, highlighting how it influences recommendation goals. 3. **Advanced Topics and Applications**: This part delves into robustness issues like shilling systems, attack models, and defenses. It also introduces contemporary topics such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning, alongside their applications. While primarily a textbook, the book also caters to industrial practitioners and researchers with its application focus and extensive references. It includes numerous examples and exercises, and a solution manual is available for instructors.
This book explores various methods for outlier ensembles, organizing them by the principles that enhance accuracy. It discusses techniques that improve the effectiveness of these methods and provides a formal classification, examining the conditions under which they excel. The authors analyze the theoretical and practical relationships between outlier ensembles and commonly used ensemble techniques in data mining, particularly in classification. They delve into the subtle differences between ensemble techniques for classification and outlier detection, highlighting how these nuances influence the design of algorithms for outlier detection. Designed for courses in data mining and related fields, the book includes numerous illustrative examples and exercises to aid classroom instruction. It assumes familiarity with the outlier detection problem and the general concept of ensemble analysis in classification, as many methods discussed are adaptations from classification techniques. Unique insights are offered through techniques like wagging, randomized feature weighting, and geometric subsampling, which are not found elsewhere. Additionally, the book analyzes the performance of various base detectors and their effectiveness. It serves as a valuable resource for researchers and practitioners aiming to optimize algorithmic design using ensemble methods.