標題: Titlebook: Mathematical Introduction to Data Science; Sven A. Wegner Textbook 2024 The Editor(s) (if applicable) and The Author(s), under exclusive l [打印本頁] 作者: 和善 時間: 2025-3-21 18:47
書目名稱Mathematical Introduction to Data Science影響因子(影響力)
書目名稱Mathematical Introduction to Data Science影響因子(影響力)學科排名
書目名稱Mathematical Introduction to Data Science網絡公開度
書目名稱Mathematical Introduction to Data Science網絡公開度學科排名
書目名稱Mathematical Introduction to Data Science被引頻次
書目名稱Mathematical Introduction to Data Science被引頻次學科排名
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書目名稱Mathematical Introduction to Data Science年度引用學科排名
書目名稱Mathematical Introduction to Data Science讀者反饋
書目名稱Mathematical Introduction to Data Science讀者反饋學科排名
作者: Bouquet 時間: 2025-3-21 23:09 作者: adroit 時間: 2025-3-22 00:39 作者: 令人心醉 時間: 2025-3-22 05:44 作者: strdulate 時間: 2025-3-22 08:45
Sven A. Wegnerwicklungsdiagnostik fokussiert. Hierzu wird das Dickicht der sprachlichen Subtests und Faktoren aus aktuellen Intelligenztest- und Entwicklungstests durchforstet und hinsichtlich aktueller Klassifikationsversuche bewertet. Illustriert wird der Wert für die praktisch t?tige Diagnostikerin bzw. den pr作者: 剝皮 時間: 2025-3-22 15:01
Sven A. Wegnerte Operationstechniken und Ma?nahmen der Intensivpflege die überlebenschancen von Patienten mit Hirnsch?digungen entscheidend verbessert. Andererseits entstanden in den meisten L?ndern ambulante und station?re Einrichtungen, die solche Patienten nach der Akutphase behandeln. In medizinischen Rehabil作者: 滔滔不絕地說 時間: 2025-3-22 19:56 作者: strdulate 時間: 2025-3-22 22:32 作者: jungle 時間: 2025-3-23 01:37
Sven A. Wegnern neemt de geheugenstoornis toe. Op andere cognitieve domeinen ontstaan dan ook stoornissen en uiteindelijk wordt de diagnose beginnende dementie gesteld, hoogstwaarschijnlijk door de ziekte van Alzheimer. Het diagnostische proces – dat zich uitstrekt over enkele jaren – wordt gedetailleerd beschrev作者: 鈍劍 時間: 2025-3-23 09:06 作者: plasma 時間: 2025-3-23 12:42 作者: 令人發(fā)膩 時間: 2025-3-23 16:52 作者: 盡忠 時間: 2025-3-23 18:52 作者: 恫嚇 時間: 2025-3-24 01:45 作者: 羽毛長成 時間: 2025-3-24 05:54 作者: 商業(yè)上 時間: 2025-3-24 08:10 作者: insular 時間: 2025-3-24 12:26 作者: LEVY 時間: 2025-3-24 15:57
-Nearest Neighbors,N classifier with majority vote and the .-NN regressor with arithmetic mean. The effect of overfitting is illustrated via several examples. We introduce some preprocessing methods and then generalize the initially mentioned setting of metric spaces to distance measures in order to include cosine sim作者: 使害怕 時間: 2025-3-24 19:59 作者: 切掉 時間: 2025-3-25 01:48
Best-Fit Subspaces,ethod of least squares from Chapter ., but this time all coordinates of the data points are considered (and not only those designated as labels). By reformulating the initial minimization problem into a maximization problem, we present the greedy algorithm for calculating a best-fit subspace.作者: antiandrogen 時間: 2025-3-25 07:07
Singular Value Decomposition, Courant-Fischer formula, we then link SVD to the greedy algorithm already discussed in Chapter .. This is followed by several applications such as dimensionality reduction of datasets and lower-rank approximation of matrices. As a concrete example, we discuss image compression. Finally, we illustra作者: 營養(yǎng) 時間: 2025-3-25 07:44
Separation and Fitting of High-Dimensional Gaussians,ntangled) again. Indeed, high dimensionality plays into our hands here, and we formalize this in the form of an asymptotic separation theorem. We also discuss parameter estimation (fitting) for a single Gaussian, using the maximum likelihood method.作者: leniency 時間: 2025-3-25 12:23
Support Vector Machines, machine (SVM) is precisely that classifier for which the decision boundary has the largest possible distance to the data. We reduce the task of finding the SVM to a quadratic optimization problem using the Karush-Kuhn-Tucker theorem and then discuss interpretations of the Lagrange multipliers that 作者: 悠然 時間: 2025-3-25 19:47
Kernel Method, separable dataset into a higher-dimensional (sometimes even infinite-dimensional!) space. If this “embedded dataset” is linearly separable, then we may apply the perceptron algorithm or the SVM method and obtain an induced classifier for the original data. The latter leads to the so-called kernel t作者: Venules 時間: 2025-3-25 21:26
Neural Networks,ks with Heaviside activation, we discuss the uniform approximation of continuous functions by shallow or deep neural networks. Highlights are the theorems of Cybenko, Leshno-Lin-Pinkus-Schocken, and Hanin. In the second part of the chapter, we outline the method of backpropagation, with which the we作者: 放肆的你 時間: 2025-3-26 00:25
What Is Data (Science)?,egorical and continuous labels. As examples we discuss tables of exam results, handwritten letters, body size distributions, social networks, movie ratings, and grayscale digital images. We outline the questions pertaining to datasets that we will address in the following chapters.作者: FRAUD 時間: 2025-3-26 05:37 作者: 使出神 時間: 2025-3-26 11:22
Best-Fit Subspaces,ethod of least squares from Chapter ., but this time all coordinates of the data points are considered (and not only those designated as labels). By reformulating the initial minimization problem into a maximization problem, we present the greedy algorithm for calculating a best-fit subspace.作者: Forage飼料 時間: 2025-3-26 12:52
Separation and Fitting of High-Dimensional Gaussians,ntangled) again. Indeed, high dimensionality plays into our hands here, and we formalize this in the form of an asymptotic separation theorem. We also discuss parameter estimation (fitting) for a single Gaussian, using the maximum likelihood method.作者: gangrene 時間: 2025-3-26 17:21 作者: handle 時間: 2025-3-26 21:23 作者: nephritis 時間: 2025-3-27 02:21 作者: 廢墟 時間: 2025-3-27 05:46 作者: Baffle 時間: 2025-3-27 11:46
Concentration of Measure,We intensify our investigation of uniformly distributed random datasets started in Chapter . and first prove the surface concentration theorem followed by the waist concentration theorem. A probabilistic interpretation of these then shows that the effects initially perceived as odd in Chapter . are, on the contrary, very plausible.作者: 使成整體 時間: 2025-3-27 15:32
Gaussian Random Vectors in High Dimensions,In this chapter, we prove the Gaussian annulus theorem using the Chernoff method. As corollaries, we present the Gaussian orthogonality theorem and the Gaussian distance theorem. These theorems show that the properties of high-dimensional Gaussian data, which initially appeared unintuitive in Chapter ., in fact make very much sense.作者: Cardioversion 時間: 2025-3-27 21:08
,Dimensionality Reduction à la Johnson-Lindenstrauss,As a further consequence of the Gaussian annulus theorem, we prove the Johnson-Lindenstrauss lemma on random projections and illustrate its application to dimensionality reduction.作者: humectant 時間: 2025-3-28 01:54
Perceptron,We return to classification problems with low-dimensional datasets and show how a classifier can be found for binary labeled, linearly separable datasets using the perceptron algorithm.作者: sulcus 時間: 2025-3-28 03:47
Gradient Descent for Convex Functions,In the last chapter, we provide an introduction to the gradient descent method, which is used in many data science and machine learning problems. In addition to classic results on the convergence of the method for .-convex and .-smooth functions, we also discuss the case where the function to be minimized is merely convex and differentiable.作者: insincerity 時間: 2025-3-28 08:04
Selected Results of Probability Theory,As an appendix, we summarize some results from probability theory that we have regularly used in the main text.作者: 溝通 時間: 2025-3-28 11:20 作者: 外露 時間: 2025-3-28 16:30 作者: macular-edema 時間: 2025-3-28 20:39 作者: engrossed 時間: 2025-3-28 23:39 作者: Lipohypertrophy 時間: 2025-3-29 06:07 作者: airborne 時間: 2025-3-29 10:13 作者: 委托 時間: 2025-3-29 11:42
r korrekt sind, sie ?perseveriert?. Die beschriebenen neuropsychologischen Defizite treten bei normaler, im Vergleich zum pr?morbiden Status vermutlich jedoch geminderter Intelligenz auf. Die Prognose ist nach diesen Befunden für anspruchsvolle berufliche Zielsetzungen nicht günstig. Trotz der im Mo作者: 暫時別動 時間: 2025-3-29 17:57 作者: 分開 時間: 2025-3-29 23:11 作者: 熱心 時間: 2025-3-30 03:27 作者: CAMP 時間: 2025-3-30 04:15
Sven A. Wegnert und anhand des Logogenmodells Behandlungsoptionen aufgezeigt. Ein Fallbeispiel aus dem station?ren Rehasetting verdeutlicht die Komplexit?t erworbener Sprachst?rungen und die massiven Auswirkungen auf den Alltag der betroffenen Kinder und Jugendlichen und deren Familien.作者: Moderate 時間: 2025-3-30 11:45
Sven A. Wegnergen konfrontiert. Dabei geht es um die Frage der neuropsychologischen Therapie der Hirnleistungsst?rungen. In diesem Bereich fehlen übersichtsarbeiten weitgehend. Ergotherapeuten sind haupts?chlich auf ihren ?Instinkt“ angewiesen und haben oft Mühe, ein systematisches Therapieprogramm aufzubauen.作者: NIP 時間: 2025-3-30 13:51
Sven A. Wegnergen konfrontiert. Dabei geht es um die Frage der neuropsychologischen Therapie der Hirnleistungsst?rungen. In diesem Bereich fehlen übersichtsarbeiten weitgehend. Ergotherapeuten sind haupts?chlich auf ihren ?Instinkt“ angewiesen und haben oft Mühe, ein systematisches Therapieprogramm aufzubauen.作者: 富饒 時間: 2025-3-30 17:14 作者: anticipate 時間: 2025-3-30 21:45
Sven A. Wegneraande uit methodieken waarmee zij worden ondersteund in het zo goed als mogelijk volhouden van hun dagelijkse activiteiten. Doel hierbij is de pati?nt in zijn sociaal functioneren te ondersteunen, waarbij een zo hoog mogelijke kwaliteit van leven wordt beoogd.作者: Projection 時間: 2025-3-31 04:55 作者: Blasphemy 時間: 2025-3-31 05:26 作者: 造反,叛亂 時間: 2025-3-31 10:33
aande uit methodieken waarmee zij worden ondersteund in het zo goed als mogelijk volhouden van hun dagelijkse activiteiten. Doel hierbij is de pati?nt in zijn sociaal functioneren te ondersteunen, waarbij een zo hoog mogelijke kwaliteit van leven wordt beoogd.作者: exceptional 時間: 2025-3-31 16:24 作者: 手銬 時間: 2025-3-31 21:15 作者: 離開就切除 時間: 2025-4-1 01:39
Kernel Method,rick, where one does not even need to know the higher-dimensional space explicitly, but can, by using only a kernel function, determine a classifier through solving a quadratic optimization problem. We address the existence of kernel functions by considering Mercer’s condition.作者: Calculus 時間: 2025-4-1 01:56
Textbook 2024bors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks..作者: URN 時間: 2025-4-1 08:08
Textbook 2024ialize in Data Science and Machine Learning. It introduces the reader to the most important topics in the latter areas focusing on rigorous proofs and a systematic understanding of the underlying ideas...The textbook comes with 121 classroom-tested exercises. Topics covered include .k.-nearest neigh作者: champaign 時間: 2025-4-1 11:49 作者: 廢除 時間: 2025-4-1 17:25
-tested exercises. Topics covered include .k.-nearest neighbors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks..978-3-662-69425-1978-3-662-69426-8作者: Lime石灰 時間: 2025-4-1 19:29
-Nearest Neighbors,ce some preprocessing methods and then generalize the initially mentioned setting of metric spaces to distance measures in order to include cosine similarity and cosine distance into our theory. As examples, we discuss text mining, product reviews, and handwriting recognition.
