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

Two-mode networks are a natural representation for many kinds of relational data. These networks are bipartite graphs consisting of two distinct sets (“modes”) of entities. For example, one can model multiple recipient email data as a twomode network of (a) individuals and (b) the emails that they send or receive. In this work we present a statistical model for two-mode network data which posits that individuals belong to latent sets and that the members of a particular set tend to co-appear. We show how to infer these latent sets from observed data using a Markov chain Monte Carlo inference algorithm. We apply the model to the Enron email corpus, using it to discover interpretable latent structure as well as evaluating its predictive accuracy on a missing data task. Extensions to the model are discussed that incorporate additional side information such as the email’s sender or text content, further improving the accuracy of the model.

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Latent Set Models for Two-Mode Network Data.

Learning graphical models from data is of fundamental importance in machine learning and statistics; however, this task is often computationally challenging due to the complexity of the models and the large scale of the data sets involved. This dissertation presents a variety of distributed and accelerated inference algorithms for probabilistic graphical models. The first part of this dissertation focuses on a class of directed latent variable models known as topic models. We introduce synchronous and asynchronous distributed algorithms for topic models which yield significant time and memory savings without sacrificing accuracy. We also investigate various approximate inference techniques for topic models, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation and find that these methods learn models of similar accuracy as long as hyperparameters are optimized, giving us the freedom to utilize the most computationally efficient algorithm. The second part of this dissertation focuses on accelerated parameter estimation techniques for undirected models such as Boltzmann machines and exponential random graph models. We investigate an efficient blocked contrastive divergence approach that is based on the composite likelihood framework. We also present a particle filtering approach for approximate maximum likelihood estimation that is able to outperform previously proposed estimation algorithms.

Distributed and accelerated inference algorithms for probabilistic graphical models

The paper presents the synthesis of neural network based feedback laws for dynamic systems using the computed optimal and time histories of the state and control variables. The efficacy of the proposed approach has been successfully demonstrated on a minimum time orbit injection problem. If the method is found to be effective to real life problems with many state and control variables, it can used for a variety of guidance and control problems.

Synthesis of minimum-time feedback laws for dynamic systems using neural networks

Personalization is increasingly important for a range of applications that rely on location-based modeling. A key aspect in building personalized models is using population-level information to smooth noisy sparse data at the individual level. In this paper we develop a general mixture model framework for learning individual-level location models where the model adaptively combines different types of smoothing information. In a series of experiments with Twitter geolocation data and Gowalla check-in data we demonstrate that the proposed approach can be significantly more accurate than more traditional smoothing and matrix factorization techniques. The improvement in performance over matrix factorization is pronounced and may be explained by the tendency of dimensionality reduction methods to over-smooth and not retain enough detail at the individual level.

https://dl.acm.org/doi/pdf/10.1145/2996913.2996953

Padhraic Smyth

Papers

Latent Set Models for Two-Mode Network Data.

Distributed and accelerated inference algorithms for probabilistic graphical models

Synthesis of minimum-time feedback laws for dynamic systems using neural networks

Personalized location models with adaptive mixtures

Models and Patterns