Conferences related to Machine Learning

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2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2018 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


2018 IEEE Conference on Computational Intelligence and Games (CIG)

The IEEE Conference on Computational Intelligence and Games is the premier annual event for researchers applying computational and artificial intelligence techniques to games. The domain of the conference includes all sorts of CI/AI applied to all sorts of games, including board games, video games and mathematical games.

  • 2017 IEEE Conference on Computational Intelligence and Games (CIG)

    Computer games not only offer a killer application for computational intelligence (CI), machine learning and search but also provide a compelling domain where problem solving and decision making meet artifact creation; both of which can be experienced via a highly immersive, complex and rich interaction. Additionally, methods from computational intelligence promise to have a big impact on game technology and development, assisting designers and developers and enabling new types of computer games. The Computational Intelligence and Games (CIG) conference series brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field. The annual IEEE Conference on Computational Intelligence and Games (CIG) is one of the premier international conferences in the field of computational and artificial intelligence and games.

  • 2016 IEEE Conference on Computational Intelligence and Games (CIG)

    Games can be used as a challenging scenery for benchmarking methods from computational intelligence since they provide dynamic and competitive elements that are germane to real-world problems. This conference brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.The IEEE Conference on Computational Intelligence and Games is the premier annual event for researchers applying computational and artificial intelligence techniques to games. The domain of the conference includes all sorts of CI/AI applied to all sorts of games, including board games, video games and mathematical games.

  • 2015 IEEE Conference on Computational Intelligence and Games (CIG)

    Games have proven to be an ideal domain for the study of computational intelligence as not only are they fun to play and interesting to observe, but they provide competitive and dynamic environments that model many real-world problems. Additionally, methods from computational intelligence promise to have a big impact on game technology and development, assisting designers and developers and enabling new types of computer games. The Computational Intelligence and Games (CIG) conference series brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field. The annual IEEE Conference on Computational Intelligence and Games (IEEE CIG) is one of the premier international conferences in the field of computational intelligence and games.

  • 2014 IEEE Conference on Computational Intelligence and Games (CIG)

    Games can be used as a challenging scenery for benchmarking methods from computational intelligencesince they provide dynamic and competitive elements that are germane to real-world problems. This conference brings together leading researchers andpractitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2013 IEEE Conference on Computational Intelligence and Games (CIG)

    The conference covers applications of computational intelligence to games, defined in a broad sense, as well as applications to supporting technology such as content, art work, and non-player characters.

  • 2012 IEEE Conference on Computational Intelligence and Games (CIG)

    Games provide dynamic environments modelling many real-world problems and methods from computational intelligence promise to having a big impact on game technology and development. CIG 2012 brings together leading researchers, designers, developers, and practitioners from academia and industry to discuss recent advances and explore future directions in this ever changing field.

  • 2011 IEEE Conference on Computational Intelligence and Games (CIG)

    Games have proven to be an ideal domain for the study of computational intelligence as not only are they fun to play and interesting to observe, but they provide competitive and dynamic environments that model many real-world problems. The 2010 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2010 IEEE Symposium on Computational Intelligence and Games (CIG)

    Games have proven to be an ideal domain for the study of computational intelligence as not only are they fun to play and interesting to observe, but they provide competitive and dynamic environments that model many real-world problems. The 2010 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2009 IEEE Symposium on Computational Intelligence and Games (CIG)

    The 2009 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in the area of computational intelligence applied to games.

  • 2008 IEEE Symposium on Computational Intelligence and Games (CIG)

  • 2007 IEEE Symposium on Computational Intelligence and Games (CIG)

  • 2006 IEEE Symposium on Computational Intelligence and Games (CIG)

  • 2005 IEEE Symposium on Computational Intelligence and Games (CIG)


2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE is the top leading conferencein the area of theory and applications of fuzzy logic.


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Periodicals related to Machine Learning

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


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Most published Xplore authors for Machine Learning

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Xplore Articles related to Machine Learning

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Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics

[{u'author_order': 1, u'affiliation': u'Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, Abu Dhabi United Arab Emirates (e-mail: rkarn@masdar.ac.ae)', u'full_name': u'Rupesh Karn'}, {u'author_order': 2, u'affiliation': u'P.O. Box 218, IBM Research, Yorktown Heights, New York United States 10598 (e-mail: kudva@us.ibm.com)', u'full_name': u'Prabhakar Kudva'}, {u'author_order': 3, u'affiliation': u'Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, Abu Dhabi United Arab Emirates (e-mail: ielfadel@masdar.ac.ae)', u'full_name': u'Ibrahim M. Elfadel'}] IEEE Transactions on Parallel and Distributed Systems, None

Cloud network monitoring data is dynamic and distributed. Signals to monitor the cloud can appear, disappear or change their importance and clarity over time. Machine learning (ML) models tuned to a given data set can therefore quickly become inadequate. A model might be highly accurate at one point in time but may lose its accuracy at a later time due ...


Centrifugal Pump Cavitation Detection Using Machine Learning Algorithm Technique

[{u'author_order': 1, u'affiliation': u'Dept. of Energy & Power Electronics, SELECT, VIT University, Vellore, India', u'full_name': u'Nabanita Dutta'}, {u'author_order': 2, u'affiliation': u'Dept. of Energy & Power Electronics, SELECT, VIT University, Vellore, India', u'full_name': u'S. Umashankar'}, {u'author_order': 3, u'affiliation': u'Dept. of Energy & Power Electronics, SELECT, VIT University, Vellore, India', u'full_name': u'V. K. Arun Shankar'}, {u'author_order': 4, u'affiliation': u'Dept. of Energy Technology, Aalborg University, Esbjerg, Denmark', u'full_name': u'Sanjeevikumar Padmanaban'}, {u'author_order': 5, u'affiliation': u'Faculty of Electrical Engg., Wroclaw University of Technology, Poland', u'full_name': u'Zbigniew Leonowicz'}, {u'author_order': 6, u'affiliation': u'Dept. of Electrical & Electronics Engg/, Nottingham University, United Kingdom', u'full_name': u'Patrick Wheeler'}] 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), None

Cavitation is one of the major disadvantages in pumping system, which enhance to form bubbles in the pipeline and it reduces the efficiency of the pump. So it should be identified and take the preventive measure. Machine Learning is a fast and computational method which can easily detect any faults in the pumping system. Still now lots of work has ...


Machine Learning Methods for Spacecraft Telemetry Mining

[{u'author_order': 1, u'affiliation': u'Teaching assistant, Computer & Systems dept., faculty of Engineering, Zagazig University, Egypt (e-mail: sara.khalil@zu.edu.eg).', u'full_name': u'Sara K. Ibrahim'}, {u'author_order': 2, u'affiliation': u'System engineer, National Authority for Remote Sensing and Space Science, 23 Josef tito, new Nozha Cairo, Egypt (e-mail: ayman.mahmoud@narss.sci.eg).', u'full_name': u'Ayman Ahmed'}, {u'author_order': 3, u'affiliation': u'Computer & Systems dept., faculty of Engineering, Zagazig University, Egypt (e-mail: (dr.mohamedamalzeidan@gmail.com).', u'full_name': u'M. Amal Eldin Zeidan'}, {u'author_order': 4, u'affiliation': u'Computer & Systems dept., faculty of Engineering, Zagazig University, Egypt (e-mail: (ieziedan@gmail.com).', u'full_name': u'Ibrahim Ziedan'}] IEEE Transactions on Aerospace and Electronic Systems, None

Spacecrafts are critical systems that have to survive space environment effects. Due to its complexity, these types of systems are designed in a way to mitigate errors and maneuver the critical situations. Spacecraft delivers to the ground operator an abundance data related to system status telemetry; the telemetry parameters are monitored to indicate spacecraft performance. Recently, researchers proposed using Machine ...


A Machine Learning Attacks Resistant Two Stage Physical Unclonable Functions Design

[{u'author_order': 1, u'affiliation': u'Electronics and Computer Science, University of Southampton, Southampton, UK', u'full_name': u'Haibo Su'}, {u'author_order': 2, u'affiliation': u'Electronics and Computer Science, University of Southampton, Southampton, UK', u'full_name': u'Mark Zwolinski'}, {u'author_order': 3, u'affiliation': u'Electronics and Computer Science, University of Southampton, Southampton, UK', u'full_name': u'Basel Halak'}] 2018 IEEE 3rd International Verification and Security Workshop (IVSW), None

Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit ...


Machine Learning Based Computational Electromagnetic Analysis for Electromagnetic Compatibility

[{u'author_order': 1, u'affiliation': u'University of Hong Kong', u'full_name': u'L. J. Jiang'}, {u'author_order': 2, u'affiliation': u'University of Hong Kong', u'full_name': u'H. M. Yao'}, {u'author_order': 3, u'affiliation': u'Xidian University', u'full_name': u'H. H. Zhang'}, {u'author_order': 4, u'affiliation': u'Carnegie Melon University', u'full_name': u'Y. W. Qin'}] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM), None

While machine learning is becoming a demanding request in every corner of modern technology development, we are trying to see if we could make computational electromagnetic algorithms compatible to machine learning methods. In this paper, we introduce two efforts in line with this direction: solving method of moments (MoM) can be seen as a training training process. Consequently, the artificial ...


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Educational Resources on Machine Learning

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eLearning

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IEEE.tv Videos

Machine Learning of Motor Skills for Robotics
Signal Processing and Machine Learning
ICASSP 2011 Trends in Machine Learning for Signal Processing
Large Scale Data Mining Using Genetics-Based Machine Learning 3
Cat and Mouse, Email Phishing and Machine Learning - Cybersecurity in a Hyperconnected World
Large Scale Data Mining Using Genetics-Based Machine Learning 1
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
Large Scale Data Mining Using Genetics-Based Machine Learning 2
Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Big Data and Machine Learning in Cancer Genomics
Fog Computing Test Bed: Cutting Costs and Latency in Data Transmission - Fog World Congress
Geoffrey Hinton receives the IEEE/RSE James Clerk Maxwell Medal - Honors Ceremony 2016
The Path to Robust Machine Learning: IEEE TechEthics Keynote with Richard Mallah
Fengrui Shi: Knowledge Co-creation in the OrganiCity: Data Annotation with JAMAiCA
Challenges and SP Tools for Big Data Analytics
Large-scale Neural Systems for Vision and Cognition
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Sensing and Decision Making in Social Networks
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
Accelerating Machine Learning with Non-Volatile Memory: Exploring device and circuit tradeoffs - Pritish Narayanan: 2016 International Conference on Rebooting Computing

IEEE-USA E-Books

  • Machine Learning: Fundamentals

    Prognostics and health management (PHM) facilitates maintenance decision‐making and provides usage feedback for the product design and validation process. Electronic component and product manufacturers need new ways to gain insights from the massive volume of data recently streaming in from their systems and sensors, and this can be accomplished by using machine learning (ML). This chapter provides the fundamentals of ML. ML algorithms can be divided into the following four categories depending on the amount and type of supervision they need while training: supervised, unsupervised, semi‐supervised, and reinforcement learning. ML algorithms can be classified into two different learning methods based on whether or not the algorithms can learn incrementally from a stream of incoming data: batch and online learning. Probability theory plays a significant role in ML, specifically as the design of learning algorithms often depends on probabilistic assumption of the data.

  • Machine Learning: Diagnostics and Prognostics

    Prognostics and health management (PHM) has emerged as an essential approach for preventing catastrophic failure and increasing system availability by reducing downtime, extending maintenance cycles, executing time repair actions, and lowering life‐cycle costs. This chapter provides a basic understanding of data‐driven diagnostics and prognostics. It reviews recent advancements of diagnosis and prognosis techniques with a focus on their applications in practice. The chapter discusses research opportunities that can lead to further improvement of PHM in both theory and practice. Bagging, also known as bootstrap aggregating, is an ensemble learning method that uses a series of homogeneous or heterogeneous machine learning algorithms to improve classification performance. Adaptive boosting is the first practical boosting algorithm and aims to convert a set of weak classifiers into a strong one sequentially. Prognostic techniques can be categorized into two groups: regression analysis and particle filtering.

  • Machine Learning: Data Pre‐processing

    In prognostics and health management (PHM), data pre‐processing generally involves the following tasks: data cleansing, normalization, feature discovery, and imbalanced data management. Data cleansing is the process of detecting and correcting corrupt or inaccurate data. Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature extraction, also known as dimensionality reduction, is the transformation of high‐dimensional data into a meaningful representation of reduced dimensionality, which should have a dimensionality that corresponds to the intrinsic dimensionality of the data. Linear discriminant analysis (LDA) is commonly used as a dimensionality reduction technique in the data pre‐processing step for classification and machine learning applications. Feature selection, also called variable selection/attribute selection, is the process of selecting a subset of relevant features for use in model construction. The synthetic minority oversampling technique (SMOTE) algorithm produces artificial data based on the feature space similarities between minority data points.

  • Machine Learning: Anomaly Detection

    It is important to identify deviation from the nominally healthy behavior of the product and detect the onset of the product's potential faults for achieving prognostics and health management (PHM). This chapter offers a comprehensive overview of the research on anomaly detection and discusses the challenges in anomaly detection. For anomaly detection, methods can be categorized into distance‐based, clustering based, classification‐based, and statistical anomaly detection methods. The chapter provides the underlying background of the type of anomalies that can be classified into one of the following categories: point anomalies, contextual anomalies, and collective anomalies. Clustering is the partitioning of a dataset into clusters by maximizing inter‐cluster distances and minimizing intra‐cluster distances. The chapter summarizes the advantages and disadvantages of clustering‐based anomaly detection methods. A self‐organizing maps (SOM), also known as a Kohonen neural network, is a type of unsupervised learning.

  • A Brief Introduction to Machine Learning for Engineers

    There is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often ¿¿¿for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful¿¿¿ and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra.

  • Lifelong Machine Learning:Second Edition

    <p><i>Lifelong Machine Learning, Second Edition</i> is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.</p> <p>Research in lifelong learning has developed significantly in the r latively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks--which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning--most notably, multi- task learning, transfer learning, and meta-learning--because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.</p>

  • Adversarial Machine Learning

    <p>The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to vade detection by changing their behavior or the content of malicius objects they develop.</p> <p>The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep l arning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research.</p> <p>Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.</p>

  • MACHINE LEARNING FOR DATA STREAMS

    None

  • 9 Sales Gets a Machine-Learning Makeover

    We live in a data-saturated world where a great many of our interactions with other humans happen online. It makes sense then that one of the most human of business activities--sales--is currently undergoing a digital renaissance. While the sales function has historically relied on metrics, today there is far more sales-centric data, and far richer data, than ever. It comes from social media, from website interactions, and from A/B tests, just to name a few.

  • From Signal Processing to Machine Learning

    This chapter reviews the main landmarks of signal processing in the 20th century from the perspective of algorithmic developments. It focuses on cross‐fertilization with the field of statistical (machine) learning in the last decades. In the 21st century, model and data assumptions as well as algorithmic constraints are no longer valid, and the field of machine‐learning signal processing has erupted, with many successful stories to tell. The chapter also focuses on digital signal processing (DSP), which deals with the analysis of digitized and discrete sampled signals. Machine learning is a branch of computer science and artificial intelligence that enables computers to learn from data. Machine learning adequately fits the constraints and solution requirements posed by DSP problems: from computational efficiency, online adaptation, and learning with limited supervision, to their ability to combine heterogeneous information, to incorporate prior knowledge about the problem, or to interact with the user to achieve improved performance.



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