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.

Deep Learning

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5. Causality and structural models in the social sciences 6. Simpson's paradox, confounding, and collapsibility 7. Structural and counterfactual models 8. Imperfect experiments: bounds and counterfactuals 9. Probability of causation: interpretation and identification Epilogue: the art and science of cause and effect.

Causality: models, reasoning, and inference

This paper addresses the problem of automatically detecting specific patterns or shapes in time-series data. A novel and flexible approach is proposed based on segmental semiMarkov models. Unlike dynamic time-warping or templatematching, the proposed framework provides a systematic and coherent framework for leveraging both prior knowledge and training data. The pattern of interest is modeled as a K-state segmental hidden Markov model where each state is responsible for the generation of a component of the overall shape using a state-based regression function. The distance (in time) between segments is modeled as a semiMarkov process, allowing flexible deformation of time. The model can be constructed from a single training example. Recognition of a pattern in a new time series is achieved by a recursive Viterbi-like algorithm which scales linearly in the length of the sequence. The method is successfully demonstrated on real data sets, including an application to end-point detection in semiconductor manufacturing.

Deformable Markov model templates for time-series pattern matching

Techniques such as probabilistic topic models and latent-semantic indexing have been shown to be broadly useful at automatically extracting the topical or semantic content of documents, or more generally for dimension-reduction of sparse count data. These types of models and algorithms can be viewed as generating an abstraction from the words in a document to a lower-dimensional latent variable representation that captures what the document is generally about beyond the specific words it contains. In this paper we propose a new probabilistic model that tempers this approach by representing each document as a combination of (a) a background distribution over common words, (b) a mixture distribution over general topics, and (c) a distribution over words that are treated as being specific to that document. We illustrate how this model can be used for information retrieval by matching documents both at a general topic level and at a specific word level, providing an advantage over techniques that only match documents at a general level (such as topic models or latent-sematic indexing) or that only match documents at the specific word level (such as TF-IDF).

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Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model

Preface. 1 Mathematical Background. 1.1 Probability and Learning from a Bayesian Perspective. 1.2 Parameter Estimation from Data. 1.3 Mixture Models and the Expectation Maximization Algorithm. 1.4 Graphical Models. 1.5 Classification. 1.6 Clustering. 1.7 Power--Law Distributions. 1.8 Exercises. 2 Basic WWW Technologies. 2.1 Web Documents. 2.2 Resource Identifiers: URI, URL, and URN. 2.3 Protocols. 2.4 Log Files. 2.5 Search Engines. 2.6 Exercises. 3 Web Graphs. 3.1 Internet and Web Graphs. 3.2 Generative Models for the Web Graph and Other Networks. 3.3 Applications. 3.4 Notes and Additional Technical References. 3.5 Exercises. 4 Text Analysis. 4.1 Indexing. 4.2 Lexical Processing. 4.3 Content--Based Ranking. 4.4 Probabilistic Retrieval. 4.5 Latent Semantic Analysis. 4.6 Text Categorization. 4.7 Exploiting Hyperlinks. 4.8 Document Clustering. 4.9 Information Extraction. 4.10 Exercises. 5 Link Analysis. 5.1 Early Approaches to Link Analysis. 5.2 Nonnegative Matrices and Dominant Eigenvectors. 5.3 Hubs and Authorities: HITS. 5.4 PageRank. 5.5 Stability. 5.6 Probabilistic Link Analysis. 5.7 Limitations of Link Analysis. 6 Advanced Crawling Techniques. 6.1 Selective Crawling. 6.2 Focused Crawling. 6.3 Distributed Crawling. 6.4 Web Dynamics. 7 Modeling and Understanding Human Behavior on the Web. 7.1 Introduction. 7.2 Web Data and Measurement Issues. 7.3 Empirical Client--Side Studies of Browsing Behavior. 7.4 Probabilistic Models of Browsing Behavior. 7.5 Modeling and Understanding Search Engine Querying. 7.6 Exercises. 8 Commerce on the Web: Models and Applications. 8.1 Introduction. 8.2 Customer Data on theWeb. 8.3 Automated Recommender Systems. 8.4 Networks and Recommendations. 8.5 Web Path Analysis for Purchase Prediction. 8.6 Exercises. Appendix A: Mathematical Complements. A.1 Graph Theory. A.2 Distributions. A.3 Singular Value Decomposition. A.4 Markov Chains. A.5 Information Theory. Appendix B: List of Main Symbols and Abbreviations. References. Index.

Modeling the Internet and the Web: Probabilistic Method and Algorithms

They help identify and predict individual, as well as aggregate, behavior, as illustrated by four application domains: direct mail, retail, automobile insurance, and health care.

Business applications of data mining

AS WE MARCH INTO THE AGE of digital information, the problem of data overload looms ominously ahead. Our ability to analyze and understand massive datasets lags far behind our ability to gather and store the data. A new generation of computational techniques and tools is required to support the extraction of useful knowledge from the rapidly growing volumes of data. These techniques and tools are the subject of the emerging field of knowledge discovery in databases (KDD) and data mining. Large databases of digital information are ubiquitous. Data from the neighborhood store’s checkout register, your bank’s credit card authorization device, records in your doctor’s office, patterns in your telephone calls, and many more applications generate streams of digital records archived in huge databases, sometimes in so-called data warehouses. Current hardware and database technology allow efficient and inexpensive reliable data storage and access. However, whether the context is business, medicine, science, or government, the datasets themselves (in raw form) are of little direct value. What is of value is the knowledge that can be inferred from the data and put to use. For example, the marketing database of a consumer U s a m a F a y y a d ,

Padhraic Smyth

Papers

Deformable Markov model templates for time-series pattern matching

Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model

Modeling the Internet and the Web: Probabilistic Method and Algorithms

Business applications of data mining

The KDD process for extracting useful knowledge from volumes of data : Data mining and knowledge discovery in databases