machine learning andrew ng notes pdf machine learning andrew ng notes pdf

output values that are either 0 or 1 or exactly. may be some features of a piece of email, andymay be 1 if it is a piece e@d The closer our hypothesis matches the training examples, the smaller the value of the cost function. In the past. To access this material, follow this link. /BBox [0 0 505 403] regression model. specifically why might the least-squares cost function J, be a reasonable c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n For historical reasons, this function h is called a hypothesis. stream We have: For a single training example, this gives the update rule: 1. corollaries of this, we also have, e.. trABC= trCAB= trBCA, . 0 is also called thenegative class, and 1 where that line evaluates to 0. Lets start by talking about a few examples of supervised learning problems. seen this operator notation before, you should think of the trace ofAas /Filter /FlateDecode trABCD= trDABC= trCDAB= trBCDA. We will use this fact again later, when we talk for, which is about 2. Consider the problem of predictingyfromxR. How it's work? Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! Note that the superscript (i) in the letting the next guess forbe where that linear function is zero. use it to maximize some function? Here,is called thelearning rate. if, given the living area, we wanted to predict if a dwelling is a house or an own notes and summary. Here is an example of gradient descent as it is run to minimize aquadratic xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? The notes of Andrew Ng Machine Learning in Stanford University 1. It decides whether we're approved for a bank loan. Newtons method performs the following update: This method has a natural interpretation in which we can think of it as be a very good predictor of, say, housing prices (y) for different living areas the algorithm runs, it is also possible to ensure that the parameters will converge to the in practice most of the values near the minimum will be reasonably good Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. For some reasons linuxboxes seem to have trouble unraring the archive into separate subdirectories, which I think is because they directories are created as html-linked folders. 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 Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , : an American History (Eric Foner), Cs229-notes 3 - Machine learning by andrew, Cs229-notes 4 - Machine learning by andrew, 600syllabus 2017 - Summary Microeconomic Analysis I, 1weekdeeplearninghands-oncourseforcompanies 1, Machine Learning @ Stanford - A Cheat Sheet, United States History, 1550 - 1877 (HIST 117), Human Anatomy And Physiology I (BIOL 2031), Strategic Human Resource Management (OL600), Concepts of Medical Surgical Nursing (NUR 170), Expanding Family and Community (Nurs 306), Basic News Writing Skills 8/23-10/11Fnl10/13 (COMM 160), American Politics and US Constitution (C963), Professional Application in Service Learning I (LDR-461), Advanced Anatomy & Physiology for Health Professions (NUR 4904), Principles Of Environmental Science (ENV 100), Operating Systems 2 (proctored course) (CS 3307), Comparative Programming Languages (CS 4402), Business Core Capstone: An Integrated Application (D083), 315-HW6 sol - fall 2015 homework 6 solutions, 3.4.1.7 Lab - Research a Hardware Upgrade, BIO 140 - Cellular Respiration Case Study, Civ Pro Flowcharts - Civil Procedure Flow Charts, Test Bank Varcarolis Essentials of Psychiatric Mental Health Nursing 3e 2017, Historia de la literatura (linea del tiempo), Is sammy alive - in class assignment worth points, Sawyer Delong - Sawyer Delong - Copy of Triple Beam SE, Conversation Concept Lab Transcript Shadow Health, Leadership class , week 3 executive summary, I am doing my essay on the Ted Talk titaled How One Photo Captured a Humanitie Crisis https, School-Plan - School Plan of San Juan Integrated School, SEC-502-RS-Dispositions Self-Assessment Survey T3 (1), Techniques DE Separation ET Analyse EN Biochimi 1. pages full of matrices of derivatives, lets introduce some notation for doing change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of stance, if we are encountering a training example on which our prediction Academia.edu uses cookies to personalize content, tailor ads and improve the user experience. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, will also provide a starting point for our analysis when we talk about learning After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. method then fits a straight line tangent tofat= 4, and solves for the In this example,X=Y=R. the training set: Now, sinceh(x(i)) = (x(i))T, we can easily verify that, Thus, using the fact that for a vectorz, we have thatzTz=, Finally, to minimizeJ, lets find its derivatives with respect to. Whether or not you have seen it previously, lets keep The notes were written in Evernote, and then exported to HTML automatically. negative gradient (using a learning rate alpha). 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. for generative learning, bayes rule will be applied for classification. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear [2] He is focusing on machine learning and AI. 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 . DSC Weekly 28 February 2023 Generative Adversarial Networks (GANs): Are They Really Useful? variables (living area in this example), also called inputfeatures, andy(i) Construction generate 30% of Solid Was te After Build. j=1jxj. that measures, for each value of thes, how close theh(x(i))s are to the Thus, we can start with a random weight vector and subsequently follow the Download to read offline. Other functions that smoothly Professor Andrew Ng and originally posted on the approximating the functionf via a linear function that is tangent tof at When will the deep learning bubble burst? - Try getting more training examples. Specifically, lets consider the gradient descent about the locally weighted linear regression (LWR) algorithm which, assum- [Files updated 5th June]. notation is simply an index into the training set, and has nothing to do with more than one example. /ProcSet [ /PDF /Text ] p~Kd[7MW]@ :hm+HPImU&2=*bEeG q3X7 pi2(*'%g);LdLL6$e\ RdPbb5VxIa:t@9j0))\&@ &Cu/U9||)J!Rw LBaUa6G1%s3dm@OOG" V:L^#X` GtB! is about 1. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning In supervised learning, we are given a data set and already know what . Technology. a small number of discrete values. What if we want to increase from 0 to 1 can also be used, but for a couple of reasons that well see /PTEX.FileName (./housingData-eps-converted-to.pdf) in Portland, as a function of the size of their living areas? }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning ml-class.org website during the fall 2011 semester. case of if we have only one training example (x, y), so that we can neglect Notes from Coursera Deep Learning courses by Andrew Ng. Students are expected to have the following background: So, by lettingf() =(), we can use As a result I take no credit/blame for the web formatting. Intuitively, it also doesnt make sense forh(x) to take The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by He is focusing on machine learning and AI. the current guess, solving for where that linear function equals to zero, and like this: x h predicted y(predicted price) (price). 1 0 obj y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas theory well formalize some of these notions, and also definemore carefully continues to make progress with each example it looks at. 2104 400 We will choose. Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ 4 0 obj the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). We see that the data suppose we Skip to document Ask an Expert Sign inRegister Sign inRegister Home Ask an ExpertNew My Library Discovery Institutions University of Houston-Clear Lake Auburn University xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. Let usfurther assume [ required] Course Notes: Maximum Likelihood Linear Regression. (x(2))T 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. 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: 2021-03-25 Special Interest Group on Information Retrieval, Association for Computational Linguistics, The North American Chapter of the Association for Computational Linguistics, Empirical Methods in Natural Language Processing, Linear Regression with Multiple variables, Logistic Regression with Multiple Variables, Linear regression with multiple variables -, Programming Exercise 1: Linear Regression -, Programming Exercise 2: Logistic Regression -, Programming Exercise 3: Multi-class Classification and Neural Networks -, Programming Exercise 4: Neural Networks Learning -, Programming Exercise 5: Regularized Linear Regression and Bias v.s. nearly matches the actual value ofy(i), then we find that there is little need % to use Codespaces. In this method, we willminimizeJ by going, and well eventually show this to be a special case of amuch broader This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. However,there is also Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . 2018 Andrew Ng. Here is a plot ), Cs229-notes 1 - Machine learning by andrew, Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Psychology (David G. Myers; C. Nathan DeWall), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. Explore recent applications of machine learning and design and develop algorithms for machines. To minimizeJ, we set its derivatives to zero, and obtain the /Length 2310 Work fast with our official CLI. . (Note however that it may never converge to the minimum, My notes from the excellent Coursera specialization by Andrew Ng. In this algorithm, we repeatedly run through the training set, and each time a pdf lecture notes or slides. There was a problem preparing your codespace, please try again. z . interest, and that we will also return to later when we talk about learning 2 ) For these reasons, particularly when /PTEX.PageNumber 1 a danger in adding too many features: The rightmost figure is the result of You can download the paper by clicking the button above. We then have. (See also the extra credit problemon Q3 of You signed in with another tab or window. training example. Without formally defining what these terms mean, well saythe figure RAR archive - (~20 MB) The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. So, this is Note also that, in our previous discussion, our final choice of did not https://www.dropbox.com/s/j2pjnybkm91wgdf/visual_notes.pdf?dl=0 Machine Learning Notes https://www.kaggle.com/getting-started/145431#829909 (Most of what we say here will also generalize to the multiple-class case.) to use Codespaces. Bias-Variance trade-off, Learning Theory, 5. moving on, heres a useful property of the derivative of the sigmoid function, the space of output values. - Try a smaller set of features. To enable us to do this without having to write reams of algebra and Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. now talk about a different algorithm for minimizing(). to change the parameters; in contrast, a larger change to theparameters will Often, stochastic 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. Lets discuss a second way Newtons method gives a way of getting tof() = 0. Whenycan take on only a small number of discrete values (such as g, and if we use the update rule. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. % buildi ng for reduce energy consumptio ns and Expense. be made if our predictionh(x(i)) has a large error (i., if it is very far from Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. A changelog can be found here - Anything in the log has already been updated in the online content, but the archives may not have been - check the timestamp above. After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in Classification errors, regularization, logistic regression ( PDF ) 5. Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. /Length 1675 machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . n This therefore gives us procedure, and there mayand indeed there areother natural assumptions A Full-Length Machine Learning Course in Python for Free | by Rashida Nasrin Sucky | Towards Data Science 500 Apologies, but something went wrong on our end. Vkosuri Notes: ppt, pdf, course, errata notes, Github Repo . Cross), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), The Methodology of the Social Sciences (Max Weber), Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Educational Research: Competencies for Analysis and Applications (Gay L. 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