Robot learning is an exciting and interdisciplinary ?eld. This state is re?ected in the range and form of the papers presented here. Techniques that have - come well established in robot learning are present: evolutionary methods, neural networkapproaches, reinforcement learning; as are techniques from control t- ory, logic programming, and Bayesian statistics. It is notalbe that in many of the papers presented in this volume several of these techniques are employed in conjunction. In papers by Nehmzow, Grossmann and Quoy neural networks are utilised to provide landmark-based representations of the environment, but di?erent techniques are used in each paper to make inferences based on these representations. Biology continues to provide inspiration for the robot learning researcher. In their paper Peter Eggenberger et al. borrow ideas about the role of n- romodulators in switching neural circuits, These are combined with standard techniques from arti?cial neural networks and evolutionary computing to p- vide a powerful new algorithm for evolving robot controllers. In the ?nal paper in this volume Bianco and Cassinis combine observations about the navigation behaviour of insects with techniques from control theory to produce their visual landmarklearning system. Hopefully this convergence of engineering and biol- ical approaches will continue. A rigourous understanding of the ways techniques from these very di?erent disciplines can be fused is an important challenge if progress is to continue. Al these papers are also testament to the utility of using robots to study intelligence and adaptive behaviour.

Deep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data. Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their 'default' usage and their application to tabular data. Part III compounds the power of the previously covered methods by surveying strategies and techniques to supercharge deep learning systems: autoencoders, deep data generation, meta-optimization, multi-model arrangement, and neural network interpretability. Each chapter comes with extensive visualization, code, and relevant research coverage. Modern Deep Learning for Tabular Data is one of the first of its kind - a wide exploration of deep learning theory and applications to tabular data, integrating and documenting novel methods and techniques in the field. This book provides a strong conceptual and theoretical toolkit to approach challenging tabular data problems. What You Will Learn Important concepts and developments in modern machine learning and deep learning, with a strong emphasis on tabular data applications. Understand the promising links between deep learning and tabular data, and when a deep learning approach is or isn't appropriate. Apply promising research and unique modeling approaches in real-world data contexts. Explore and engage with modern, research-backed theoretical advances on deep tabular modeling Utilize unique and successful preprocessing methods to prepare tabular data for successful modelling. Who This Book Is ForData scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security.

This volume contains the proceedings of EuroGP 2000, the European Conf- ence on Genetic Programming, held in Edinburgh on the 15th and 16th April 2000. This event was the third in a series which started with the two European workshops: EuroGP'98, held in Paris in April 1998, and EuroGP'99, held in Gothenburg in May 1999. EuroGP 2000 was held in conjunction with EvoWo- shops 2000 (17th April) and ICES 2000 (17th-19th April). Genetic Programming (GP) is a growing branch of Evolutionary Compu- tion in which the structures in the population being evolved are computer p- grams. GP has been applied successfully to a large number of di?cult problems like automatic design, pattern recognition, robotic control, synthesis of neural networks, symbolic regression, music and picture generation, biomedical app- cations, etc. In recent years,even human-competitive results have been achieved by a number of groups. EuroGP 2000, the ?rst evolutionary computation conference of the new m- lennium, was the biggest event devoted to genetic programming to be held in Europe in 2000. It was a high quality conference where state-of-the-art work on the theory of GP and applications of GP to real world problems was presented.

The increasingly active eld of Evolutionary Computation (EC) provides val- ble tools, inspired by the theory of natural selection and genetic inheritance, to problem solving, machine learning, and optimization in many real-world app- cations. Despite some early intuitions about EC, that can be dated back to the - vention of computers, and a better formal de nition of EC, made in the 1960s, the quest for real-world applications of EC only began in the late 1980s. The dramatic increase in computer performances in the last decade of the 20th c- tury gave rise to a positive feedback process: EC techniques became more and more applicable, stimulating the growth of interest in their study, and allowing, in turn, new powerful EC paradigms to be devised. In parallel with new theoretical results, the number of elds to which EC is being applied is increasing day by day, along with the complexity of applications and application domains. In particular, industrially relevant elds, such as signal and image processing, computer vision, pattern recognition, industrial control, telecommunication, scheduling and timetabling, and aerospace engineering are employing EC techniques to solve complex real-world problems.

ThisvolumecontainsallthepaperspresentedattheInternationalConferenceon Algorithmic Learning Theory 1999 (ALT'99), held at Waseda University Int- nationalConferenceCenter, Tokyo, Japan, December 6?8,1999.Theconference was sponsored by the Japanese Society for Arti cial Intelligence (JSAI). In response to the call for papers, 51 papers on all aspects of algorithmic learning theory and related areas were submitted, of which 26 papers were - lected for presentation by the program committee based on their originality, quality, and relevance to the theory of machine learning. In addition to these regular papers, this volume contains three papers of invited lectures presented byKatharinaMorikoftheUniversityofDortmund, RobertE.SchapireofAT&T Labs, Shannon Lab., and Kenji Yamanishi of NEC, C&C Media Research Lab. ALT'99 is not just one of the ALT conference series, but this conference marks the tenth anniversary in the series that was launched in Tokyo, in Oc- ber 1990, for the discussion of research topics on all areas related to algorithmic learning theory. The ALT series was renamedlast year from\ALT workshop"to \ALT conference,"expressing its wider goalof providing an ideal forum to bring together researchers from both theoretical and practical learning communities, producing novel concepts and criteria that would bene t both. This movement wasre?ectedinthepaperspresentedatALT'99, wheretherewereseveralpapers motivated by application oriented problems such as noise, data precision, etc. Furthermore, ALT'99 benet ed from being held jointly with the 2nd Inter- tional Conference on Discovery Science (DS'99), the conference for discussing, among other things, more applied aspects of machine learning. Also, we could celebrate the tenth anniversary of the ALT series with researchers from both theoretical and practical communities.

Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems.

The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

This volume contains selected papers presented at the Second Asia-Paci c C- ference on Simulated Evolution and Learning (SEAL'98), from 24 to 27 Nov- ber 1998, in Canberra, Australia. SEAL'98 received a total of 92 submissions (67 papers for the regular sessions and 25 for the applications sessions). All papers were reviewed by three independent reviewers. After review, 62 papers were - cepted for oral presentation and 13 for poster presentation. Some of the accepted papers were selected for inclusion in this volume. SEAL'98 also featured a fully refereed special session on Evolutionary Computation in Power Engineering - ganised by Professor Kit Po Wong and Dr Loi Lei Lai. Two of the ve accepted papers are included in this volume. The papers included in these proceedings cover a wide range of topics in simulated evolution and learning, from self-adaptation to dynamic modelling, from reinforcement learning to agent systems, from evolutionary games to e- lutionary economics, and from novel theoretical results to successful applications, among others. SEAL'98 attracted 94 participants from 14 di erent countries, namely A- tralia, Belgium, Brazil, Germany, Iceland, India, Japan, South Korea, New Z- land, Portugal, Sweden, Taiwan, UK and the USA. It had three distinguished international scientists as keynote speakers, giving talks on natural computation (Hans-Paul Schwefel), reinforcement learning (Richard Sutton), and novel m- els in evolutionary design (John Gero). More information about SEAL'98 is still available at http: //www.cs.adfa.edu.au/conference/seal98/.

This book constitutes the refereed proceedings of the Second European Workshop on Genetic Programming, EuroPG '99, held in Göteborg, Sweden in May 1999.The 12 revised full papers and 11 posters presented have been carefully reviewed and selected for inclusion in the book. All the relevant aspects of genetic programming are addressed ranging from traditional and foundational issues to applications in a variety of fields.

This book provides a full presentation of the current concepts and available techniques to make "machine learning" systems more explainable. The approaches presented can be applied to almost all the current "machine learning" models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce "human understandable" explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are "opaque." Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.

This volume contains papers presented at the Fourth European Conference on ComputationalLearningTheory, whichwasheldatNordkirchenCastle, inNo- kirchen, NRW, Germany, from March 29 to 31, 1999. This conference is the fourth in a series of bi-annual conferences established in 1993. TheEuroCOLTconferencesarefocusedontheanalysisoflearningalgorithms and the theory of machine learning, and bring together researchers from a wide variety of related elds. Some of the issues and topics that are addressed include the sample and computational complexity of learning speci c model classes, frameworks modeling the interaction between the learner, teacher and the en- ronment (such as learning with queries, learning control policies and inductive inference), learningwithcomplexmodels(suchasdecisiontrees, neuralnetworks, and support vector machines), learning with minimal prior assumptions (such as mistake-bound models, universal prediction, and agnostic learning), and the study of model selection techniques. We hope that these conferences stimulate an interdisciplinary scienti c interaction that will be fruitful in all represented elds. Thirty- ve papers were submitted to the program committee for conside- tion, and twenty-one of these were accepted for presentation at the conference and publication in these proceedings. In addition, Robert Schapire (AT & T Labs), and Richard Sutton (AT & T Labs) were invited to give lectures and contribute a written version to these proceedings. There were a number of other joint events including a banquet and an excursion to Munster ] . The IFIP WG 1.4 Scholarship was awarded to Andra s Antos for his paper \Lower bounds on the rate of convergence of nonparametric pattern recognition.""

Machine Intelligence 13 ushers in an exciting new phase of artificial intelligence research, one in which machine learning has emerged as a hot-bed of new theory, as a practical tool in engineering disciplines, and as a source of material for cognitive models of the human brain. Based on the Machine Intelligence Workshop of 1992, held at Strathclyde University in Scotland, the book brings together numerous papers from some of the field's leading researchers to discuss current theoretical and practical issues. Highlights include a chapter by J.A. Robinson--the founder of modern computational logic--on the field's great forefathers John von Neumann and Alan Turing, and a chapter by Stephen Muggleton that analyzes Turing's legacy in logic and machine learning. This thirteenth volume in the renowned Machine Intelligence series remains the best source of information for the latest developments in the field. All students and researchers in artificial intelligence and machine learning will want to own a copy.

This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.

The definitive guide to the game theoretic and probabilistic underpinning for Bitcoin's security model. Discusses, how Bitcoin works, includes an overview of probability and game theory and provides a quantitative analysis for Bitcoin security under attack modes. Explains, possible attacks on Bitcoin as its influence grows and includes breakdown of how the how the block reward schedule and adoption will affect the vulnerability of the network.

This book constitutes the thoroughly revised and refereed post-workshop documentation of two international workshops held in conjunction with the Pacific Rim International Conference on Artificial Intelligence, PRICAI'96, in Cairns, Australia, in August 1996.
The volume presents 14 revised full papers togehter with two invited contributions and two introductory surveys particularly commissioned for this book. Among the topics addressed are computational learning, commonsense reasoning, constraint logic programming, fuzzy reasoning, vague data, inductive inference, belief revision, action theory, uncertainty, and probabilistic diagnosis.

This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998.
The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were selected from a total of 100 submissions. The book is divided in sections on applications of ML, Bayesian networks, feature selection, decision trees, support vector learning, multiple models for classification, inductive logic programming, relational learning, instance-based learning, clustering, genetic algorithms, reinforcement learning and neural networks.

This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets-i.e., big data-to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.

This book constitutes the refereed proceedings of the 8th International Workshop on Algorithmic Learning Theory, ALT'97, held in Sendai, Japan, in October 1997.
The volume presents 26 revised full papers selected from 42 submissions. Also included are three invited papers by leading researchers. Among the topics addressed are PAC learning, learning algorithms, inductive learning, inductive inference, learning from examples, game-theoretical aspects, decision procedures, language learning, neural algorithms, and various other aspects of computational learning theory.

This book constitutes the thoroughly refereed post-conference documentation of the First Asia-Pacific Conference on Simulated Evolution and Learning, SEAL'96, held in Taejon, Korea, in November 1996.
The 23 revised full papers were selected for inclusion in this book on the basis of 2 rounds of reviewing and improvements. Also included are invited papers by John L. Casti and Lawrence J. Fogel. The volume covers a wide range of current topics in simulated evolution and learning e.g. evolutionary optimization, evolutionary learning, artificial life, hybrid evolutionary fuzzy systems, evolutionary artificial neural networks, co-evolution, novel evolutionary approaches to computer tomography image reconstruction, power systems load flow control, and water flow control in cropped soils.

The complexity of systems studied in distributed artificial intelligence (DAI), such as multi-agent systems, often makes it extremely difficult or even impossible to correctly and completely specify their behavioral repertoires and dynamics. There is broad agreement that such systems should be equipped with the ability to learn in order to improve their future performance autonomously. The interdisciplinary cooperation of researchers from DAI and machine learning (ML) has established a new and very active area of research and development enjoying steadily increasing attention from both communities. This state-of-the-art report documents current and ongoing developments in the area of learning in DAI systems. It is indispensable reading for anybody active in the area and will serve as a valuable source of information.

This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997.
This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.

This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, Israel, in March 1997.
The book presents 25 revised full papers carefully selected from a total of 36 high-quality submissions. The volume spans the whole spectrum of computational learning theory, with a certain emphasis on mathematical models of machine learning. Among the topics addressed are machine learning, neural nets, statistics, inductive inference, computational complexity, information theory, and theoretical physics.

This book constitutes the refereed proceedings of the 7th International Workshop on Algorithmic Learning Theory, ALT '96, held in Sydney, Australia, in October 1996.
The 16 revised full papers presented were selected from 41 submissions; also included are eight short papers as well as four full length invited contributions by Ross Quinlan, Takeshi Shinohara, Leslie Valiant, and Paul Vitanyi, and an introduction by the volume editors. The book covers all areas related to algorithmic learning theory, ranging from theoretical foundations of machine learning to applications in several areas.

This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in 'big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.

This book includes a selection of twelve carefully revised papers chosen from the papers accepted for presentation at the 4th IEEE/Nagoya-University World Wisepersons Workshop held in Nagoya in November 1995.
The combining of the technologies of fuzzy logic, neural networks, and evolutionary computation is expected to open up a new paradigm of machine learning for the realization of human-like information generating systems. The excellent papers presented are organized in sections on fuzzy and evolutionary computation, fuzzy and learning automata, fuzzy and neural networks, genetic algorithms, and CAM-brain.