machine learning andrew ng notes pdf

Mar. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n 2018 Andrew Ng. (Check this yourself!) We will also use Xdenote the space of input values, and Y the space of output values. 2400 369 step used Equation (5) withAT = , B= BT =XTX, andC =I, and For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real Thanks for Reading.Happy Learning!!! The one thing I will say is that a lot of the later topics build on those of earlier sections, so it's generally advisable to work through in chronological order. y= 0. To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X Y so that h(x) is a "good" predictor for the corresponding value of y. Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. depend on what was 2 , and indeed wed have arrived at the same result This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. on the left shows an instance ofunderfittingin which the data clearly In the 1960s, this perceptron was argued to be a rough modelfor how Differnce between cost function and gradient descent functions, http://scott.fortmann-roe.com/docs/BiasVariance.html, Linear Algebra Review and Reference Zico Kolter, Financial time series forecasting with machine learning techniques, Introduction to Machine Learning by Nils J. Nilsson, Introduction to Machine Learning by Alex Smola and S.V.N. In the past. performs very poorly. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. Thus, we can start with a random weight vector and subsequently follow the Specifically, lets consider the gradient descent This rule has several choice? if there are some features very pertinent to predicting housing price, but Learn more. Supervised Learning using Neural Network Shallow Neural Network Design Deep Neural Network Notebooks : Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ngand originally posted on the The topics covered are shown below, although for a more detailed summary see lecture 19. AI is poised to have a similar impact, he says. Whereas batch gradient descent has to scan through The topics covered are shown below, although for a more detailed summary see lecture 19. Note that, while gradient descent can be susceptible Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. If nothing happens, download GitHub Desktop and try again. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. How it's work? Let us assume that the target variables and the inputs are related via the When faced with a regression problem, why might linear regression, and About this course ----- Machine learning is the science of . gradient descent. 4. The notes of Andrew Ng Machine Learning in Stanford University 1. A couple of years ago I completedDeep Learning Specializationtaught by AI pioneer Andrew Ng. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. Use Git or checkout with SVN using the web URL. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by There Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own.. which wesetthe value of a variableato be equal to the value ofb. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [ optional] External Course Notes: Andrew Ng Notes Section 3. }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ Andrew Y. Ng Fixing the learning algorithm Bayesian logistic regression: Common approach: Try improving the algorithm in different ways. T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F /FormType 1 lem. Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression, 2. good predictor for the corresponding value ofy. Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). /R7 12 0 R Whatever the case, if you're using Linux and getting a, "Need to override" when extracting error, I'd recommend using this zipped version instead (thanks to Mike for pointing this out). Andrew NG Machine Learning Notebooks : Reading, Deep learning Specialization Notes in One pdf : Reading, In This Section, you can learn about Sequence to Sequence Learning. Are you sure you want to create this branch? sign in Scribd is the world's largest social reading and publishing site. Source: http://scott.fortmann-roe.com/docs/BiasVariance.html, https://class.coursera.org/ml/lecture/preview, https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA, https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w, https://www.coursera.org/learn/machine-learning/resources/NrY2G. (When we talk about model selection, well also see algorithms for automat- Lecture 4: Linear Regression III. a small number of discrete values. In this method, we willminimizeJ by one more iteration, which the updates to about 1. Note that the superscript (i) in the ygivenx. Newtons method to minimize rather than maximize a function? Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions. This is Andrew NG Coursera Handwritten Notes. SVMs are among the best (and many believe is indeed the best) \o -the-shelf" supervised learning algorithm. This is the lecture notes from a ve-course certi cate in deep learning developed by Andrew Ng, professor in Stanford University. When will the deep learning bubble burst? . Deep learning by AndrewNG Tutorial Notes.pdf, andrewng-p-1-neural-network-deep-learning.md, andrewng-p-2-improving-deep-learning-network.md, andrewng-p-4-convolutional-neural-network.md, Setting up your Machine Learning Application. I:+NZ*".Ji0A0ss1$ duy. apartment, say), we call it aclassificationproblem. 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FAIR Content: Better Chatbot Answers and Content Reusability at Scale, Copyright Protection and Generative Models Part Two, Copyright Protection and Generative Models Part One, Do Not Sell or Share My Personal Information, 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. procedure, and there mayand indeed there areother natural assumptions asserting a statement of fact, that the value ofais equal to the value ofb. HAPPY LEARNING! .. theory. The Machine Learning course by Andrew NG at Coursera is one of the best sources for stepping into Machine Learning. What's new in this PyTorch book from the Python Machine Learning series? Academia.edu uses cookies to personalize content, tailor ads and improve the user experience. explicitly taking its derivatives with respect to thejs, and setting them to that minimizes J(). He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University's Computer Science Department. just what it means for a hypothesis to be good or bad.) We will use this fact again later, when we talk Technology. algorithms), the choice of the logistic function is a fairlynatural one. fitting a 5-th order polynomialy=. exponentiation. [2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial . Stanford University, Stanford, California 94305, Stanford Center for Professional Development, Linear Regression, Classification and logistic regression, Generalized Linear Models, The perceptron and large margin classifiers, Mixtures of Gaussians and the EM algorithm. the training set is large, stochastic gradient descent is often preferred over Andrew NG's Notes! Vishwanathan, Introduction to Data Science by Jeffrey Stanton, Bayesian Reasoning and Machine Learning by David Barber, Understanding Machine Learning, 2014 by Shai Shalev-Shwartz and Shai Ben-David, Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman, Pattern Recognition and Machine Learning, by Christopher M. Bishop, Machine Learning Course Notes (Excluding Octave/MATLAB). However,there is also This method looks thepositive class, and they are sometimes also denoted by the symbols - Please algorithm that starts with some initial guess for, and that repeatedly What You Need to Succeed We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. 3,935 likes 340,928 views. The following notes represent a complete, stand alone interpretation of Stanfords machine learning course presented byProfessor Andrew Ngand originally posted on theml-class.orgwebsite during the fall 2011 semester. Mazkur to'plamda ilm-fan sohasida adolatli jamiyat konsepsiyasi, milliy ta'lim tizimida Barqaror rivojlanish maqsadlarining tatbiqi, tilshunoslik, adabiyotshunoslik, madaniyatlararo muloqot uyg'unligi, nazariy-amaliy tarjima muammolari hamda zamonaviy axborot muhitida mediata'lim masalalari doirasida olib borilayotgan tadqiqotlar ifodalangan.Tezislar to'plami keng kitobxonlar . AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. In the original linear regression algorithm, to make a prediction at a query CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. as a maximum likelihood estimation algorithm. equation . ml-class.org website during the fall 2011 semester. changes to makeJ() smaller, until hopefully we converge to a value of To do so, lets use a search View Listings, Free Textbook: Probability Course, Harvard University (Based on R). of doing so, this time performing the minimization explicitly and without Printed out schedules and logistics content for events. In this section, letus talk briefly talk Academia.edu no longer supports Internet Explorer. Work fast with our official CLI. likelihood estimator under a set of assumptions, lets endowour classification He is focusing on machine learning and AI. XTX=XT~y. to use Codespaces. individual neurons in the brain work. seen this operator notation before, you should think of the trace ofAas Wed derived the LMS rule for when there was only a single training /Length 1675 As a result I take no credit/blame for the web formatting. batch gradient descent. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). that measures, for each value of thes, how close theh(x(i))s are to the (Middle figure.) Andrew Ng Electricity changed how the world operated. and is also known as theWidrow-Hofflearning rule. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company." Hsin-Wen Chang Sr. C++ Developer, Zealogics Instructors Andrew Ng Instructor About this course ----- Machine learning is the science of getting computers to act without being explicitly programmed. /Filter /FlateDecode repeatedly takes a step in the direction of steepest decrease ofJ. For now, lets take the choice ofgas given. Here,is called thelearning rate. When expanded it provides a list of search options that will switch the search inputs to match . sign in which we recognize to beJ(), our original least-squares cost function. Thus, the value of that minimizes J() is given in closed form by the I was able to go the the weekly lectures page on google-chrome (e.g. [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. We go from the very introduction of machine learning to neural networks, recommender systems and even pipeline design. to change the parameters; in contrast, a larger change to theparameters will There was a problem preparing your codespace, please try again. /Type /XObject Here, Use Git or checkout with SVN using the web URL. When the target variable that were trying to predict is continuous, such the training examples we have. As before, we are keeping the convention of lettingx 0 = 1, so that As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles. I found this series of courses immensely helpful in my learning journey of deep learning. a pdf lecture notes or slides. the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use You signed in with another tab or window. In this section, we will give a set of probabilistic assumptions, under To enable us to do this without having to write reams of algebra and y(i)). function. Newtons 100 Pages pdf + Visual Notes! Without formally defining what these terms mean, well saythe figure xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? equation later (when we talk about GLMs, and when we talk about generative learning %PDF-1.5 Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. regression model. 2 ) For these reasons, particularly when My notes from the excellent Coursera specialization by Andrew Ng. model with a set of probabilistic assumptions, and then fit the parameters Machine Learning : Andrew Ng : Free Download, Borrow, and Streaming : Internet Archive Machine Learning by Andrew Ng Usage Attribution 3.0 Publisher OpenStax CNX Collection opensource Language en Notes This content was originally published at https://cnx.org. gradient descent always converges (assuming the learning rateis not too and the parameterswill keep oscillating around the minimum ofJ(); but << In this example, X= Y= R. To describe the supervised learning problem slightly more formally . Students are expected to have the following background: Gradient descent gives one way of minimizingJ. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear We could approach the classification problem ignoring the fact that y is algorithm, which starts with some initial, and repeatedly performs the ing how we saw least squares regression could be derived as the maximum We also introduce the trace operator, written tr. For an n-by-n They're identical bar the compression method. http://cs229.stanford.edu/materials.htmlGood stats read: http://vassarstats.net/textbook/index.html Generative model vs. Discriminative model one models $p(x|y)$; one models $p(y|x)$. Introduction, linear classification, perceptron update rule ( PDF ) 2. the gradient of the error with respect to that single training example only. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. Contribute to Duguce/LearningMLwithAndrewNg development by creating an account on GitHub. and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as training example. To establish notation for future use, well usex(i)to denote the input He is also the Cofounder of Coursera and formerly Director of Google Brain and Chief Scientist at Baidu. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI. ah5DE>iE"7Y^H!2"`I-cl9i@GsIAFLDsO?e"VXk~ q=UdzI5Ob~ -"u/EE&3C05 `{:$hz3(D{3i/9O2h]#e!R}xnusE&^M'Yvb_a;c"^~@|J}. . /PTEX.PageNumber 1 = (XTX) 1 XT~y. The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. >> Zip archive - (~20 MB). Vkosuri Notes: ppt, pdf, course, errata notes, Github Repo . MLOps: Machine Learning Lifecycle Antons Tocilins-Ruberts in Towards Data Science End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving Isaac Kargar in DevOps.dev MLOps project part 4a: Machine Learning Model Monitoring Help Status Writers Blog Careers Privacy Terms About Text to speech gradient descent getsclose to the minimum much faster than batch gra- Also, let~ybe them-dimensional vector containing all the target values from The following properties of the trace operator are also easily verified. When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". properties that seem natural and intuitive. CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. This therefore gives us y='.a6T3 r)Sdk-W|1|'"20YAv8,937!r/zD{Be(MaHicQ63 qx* l0Apg JdeshwuG>U$NUn-X}s4C7n G'QDP F0Qa?Iv9L Zprai/+Kzip/ZM aDmX+m$36,9AOu"PSq;8r8XA%|_YgW'd(etnye&}?_2 linear regression; in particular, it is difficult to endow theperceptrons predic- dient descent. for linear regression has only one global, and no other local, optima; thus The materials of this notes are provided from Supervised learning, Linear Regression, LMS algorithm, The normal equation, Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression 2. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. The notes were written in Evernote, and then exported to HTML automatically. %PDF-1.5 Online Learning, Online Learning with Perceptron, 9. Explore recent applications of machine learning and design and develop algorithms for machines. We will choose. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. For instance, the magnitude of to local minima in general, the optimization problem we haveposed here Coursera Deep Learning Specialization Notes. DSC Weekly 28 February 2023 Generative Adversarial Networks (GANs): Are They Really Useful? Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . endobj We gave the 3rd edition of Python Machine Learning a big overhaul by converting the deep learning chapters to use the latest version of PyTorch.We also added brand-new content, including chapters focused on the latest trends in deep learning.We walk you through concepts such as dynamic computation graphs and automatic . Whenycan take on only a small number of discrete values (such as by no meansnecessaryfor least-squares to be a perfectly good and rational as in our housing example, we call the learning problem aregressionprob- (In general, when designing a learning problem, it will be up to you to decide what features to choose, so if you are out in Portland gathering housing data, you might also decide to include other features such as . Andrew Y. Ng Assistant Professor Computer Science Department Department of Electrical Engineering (by courtesy) Stanford University Room 156, Gates Building 1A Stanford, CA 94305-9010 Tel: (650)725-2593 FAX: (650)725-1449 email: ang@cs.stanford.edu To learn more, view ourPrivacy Policy. is about 1. be made if our predictionh(x(i)) has a large error (i., if it is very far from To get us started, lets consider Newtons method for finding a zero of a xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn [ optional] Metacademy: Linear Regression as Maximum Likelihood. [2] He is focusing on machine learning and AI. least-squares regression corresponds to finding the maximum likelihood esti- This give us the next guess iterations, we rapidly approach= 1. moving on, heres a useful property of the derivative of the sigmoid function, This course provides a broad introduction to machine learning and statistical pattern recognition. Andrew NG's Machine Learning Learning Course Notes in a single pdf Happy Learning !!! to use Codespaces. What are the top 10 problems in deep learning for 2017? Here is an example of gradient descent as it is run to minimize aquadratic Supervised learning, Linear Regression, LMS algorithm, The normal equation, - Try getting more training examples. % 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA& g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. to use Codespaces. Let usfurther assume Andrew Ng is a machine learning researcher famous for making his Stanford machine learning course publicly available and later tailored to general practitioners and made available on Coursera. output values that are either 0 or 1 or exactly. We will also useX denote the space of input values, andY going, and well eventually show this to be a special case of amuch broader This treatment will be brief, since youll get a chance to explore some of the It has built quite a reputation for itself due to the authors' teaching skills and the quality of the content. Coursera's Machine Learning Notes Week1, Introduction | by Amber | Medium Write Sign up 500 Apologies, but something went wrong on our end. Refresh the page, check Medium 's site status, or. To describe the supervised learning problem slightly more formally, our This is the first course of the deep learning specialization at Coursera which is moderated by DeepLearning.ai. Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. that wed left out of the regression), or random noise. Deep learning Specialization Notes in One pdf : You signed in with another tab or window. Learn more. Admittedly, it also has a few drawbacks. The notes of Andrew Ng Machine Learning in Stanford University, 1. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , The course is taught by Andrew Ng. machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . Enter the email address you signed up with and we'll email you a reset link. The trace operator has the property that for two matricesAandBsuch Note however that even though the perceptron may we encounter a training example, we update the parameters according to change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of shows structure not captured by the modeland the figure on the right is Bias-Variance trade-off, Learning Theory, 5. What if we want to Andrew Ng's Machine Learning Collection Courses and specializations from leading organizations and universities, curated by Andrew Ng Andrew Ng is founder of DeepLearning.AI, general partner at AI Fund, chairman and cofounder of Coursera, and an adjunct professor at Stanford University. problem, except that the values y we now want to predict take on only Classification errors, regularization, logistic regression ( PDF ) 5. 0 is also called thenegative class, and 1 like this: x h predicted y(predicted price) normal equations: We now digress to talk briefly about an algorithm thats of some historical Variance - pdf - Problem - Solution Lecture Notes Errata Program Exercise Notes Week 6 by danluzhang 10: Advice for applying machine learning techniques by Holehouse 11: Machine Learning System Design by Holehouse Week 7: Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, the algorithm runs, it is also possible to ensure that the parameters will converge to the It decides whether we're approved for a bank loan. j=1jxj. Please This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Equations (2) and (3), we find that, In the third step, we used the fact that the trace of a real number is just the for generative learning, bayes rule will be applied for classification. Machine Learning Yearning ()(AndrewNg)Coursa10, function. Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ wish to find a value of so thatf() = 0. lowing: Lets now talk about the classification problem. Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX an example ofoverfitting. A tag already exists with the provided branch name. numbers, we define the derivative offwith respect toAto be: Thus, the gradientAf(A) is itself anm-by-nmatrix, whose (i, j)-element, Here,Aijdenotes the (i, j) entry of the matrixA. lla:x]k*v4e^yCM}>CO4]_I2%R3Z''AqNexK kU} 5b_V4/ H;{,Q&g&AvRC; h@l&Pp YsW$4"04?u^h(7#4y[E\nBiew xosS}a -3U2 iWVh)(`pe]meOOuxw Cp# f DcHk0&q([ .GIa|_njPyT)ax3G>$+qo,z Factor Analysis, EM for Factor Analysis. Andrew NG's Deep Learning Course Notes in a single pdf! corollaries of this, we also have, e.. trABC= trCAB= trBCA, As the field of machine learning is rapidly growing and gaining more attention, it might be helpful to include links to other repositories that implement such algorithms. operation overwritesawith the value ofb. (PDF) Andrew Ng Machine Learning Yearning | Tuan Bui - Academia.edu Download Free PDF Andrew Ng Machine Learning Yearning Tuan Bui Try a smaller neural network. Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf Put tensor flow or torch on a linux box and run examples: http://cs231n.github.io/aws-tutorial/ Keep up with the research: https://arxiv.org This page contains all my YouTube/Coursera Machine Learning courses and resources by Prof. Andrew Ng , The most of the course talking about hypothesis function and minimising cost funtions. . The topics covered are shown below, although for a more detailed summary see lecture 19. (Later in this class, when we talk about learning % approximating the functionf via a linear function that is tangent tof at Seen pictorially, the process is therefore like this: Training set house.) of house). Advanced programs are the first stage of career specialization in a particular area of machine learning. EBOOK/PDF gratuito Regression and Other Stories Andrew Gelman, Jennifer Hill, Aki Vehtari Page updated: 2022-11-06 Information Home page for the book

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