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

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

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Latent dirichlet allocation

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

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Latent Dirichlet Allocation

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

When trying to recover 3D structure from a set of images, the most difficult problem is establishing the correspondence between the measurements. Most existing approaches assume that features can be tracked across frames, whereas methods that exploit rigidity constraints to facilitate matching do so only under restricted camera motion. In this paper we propose a Bayesian approach that avoids the brittleness associated with singling out one "best" correspondence, and instead consider the distribution over all possible correspondences. We treat both a fully Bayesian approach that yields a posterior distribution, and a MAP approach that makes use of EM to maximize this posterior. We show how Markov chain Monte Carlo methods can be used to implement these techniques in practice, and present experimental results on real data.

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Feature Correspondence: A Markov Chain Monte Carlo Approach

We study a form of the pursuit-evasion problem, in which one or more searchers must move through a given environment so as to guarantee detection of any and all evaders, which can move arbitrarily fast. Our goal is to develop techniques for coordinating teams of robots to execute this task in application domains such as clearing a building, for reasons of security or safety. To this end, we introduce a new class of searcher, the Φ-searcher, which can be readily instantiated as a physical mobile robot. We present a detailed analysis of the pursuit-evasion problem using Φ-searchers. We show that computing the minimum number of Φ-searchers required to search a given environment is NP-hard, and present the first complete search algorithm for a single Φ-searcher. We show how this algorithm can be extended to handle multiple searchers, and give examples of computed trajectories.

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Visibility-based pursuit-evasion with limited field of view

EXPERIENCE has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Monte Carlo approximations have demonstrated considerable success in application domains such as computer vision and robot localization and mapping. But, classical Monte Carlo methods, such as particle filters, can suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis makes contributions to the theory of probabilistic state-estimation to solve this problem. It presents a novel approach that outperforms existing algorithms (in terms of computational efficiency) for reliably estimating the state of general (nonlinear, nonGaussian) dynamic systems in real-time in the presence of uncertainty (including rare events such as faults). The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.

Tractable Particle Filters for Robot Fault Diagnosis

Monte Carlo Localization

A Self-surveying Camera Array (SSCA) is a vision-based local-area positioning system consisting of multiple ground-deployed cameras that are capable of self-surveying their extrinsic parameters while tracking and localizing a moving target. This paper presents the self-surveying algorithm being used to track a target helicopter in each camera frame and to localize the helicopter in an array-fixed frame. Three cameras are deployed independently in an arbitrary arrangement that allows each camera to view the helicopter's flight volume. The helicopter then flies an unplanned path that allows the cameras to calibrate the relative locations and orientations by utilizing a self-surveying algorithm that is extended from the well-known structure from motion algorithm and the bundle adjustment technique. This yields the cameras'extrinsic parameters enabling real-time helicopter positioning via triangulation. This paper also presents results from field trials, which verify the feasibility of the SSCA as a readily-deployable system applicable to helicopter tracking and localization. The results demonstrate that, compared to the differential GPS solution as true reference, the SSCA alone is capable of positioning the helicopter with meter-level accuracy. The SSCA has been integrated with onboard inertial sensors providing a reliable positioning system to enable successful autonomous hovering.

Sebastian Thrun

Papers

Feature Correspondence: A Markov Chain Monte Carlo Approach

Visibility-based pursuit-evasion with limited field of view

Tractable Particle Filters for Robot Fault Diagnosis

Monte Carlo Localization

Autonomous Helicopter Tracking and Localization Using a Self-Surveying Camera Array.