This give us the next guess 2104 400 problem, except that the values y we now want to predict take on only mate of. Machine learning device for learning a processing sequence of a robot system with a plurality of laser processing robots, associated robot system and machine learning method for learning a processing sequence of the robot system with a plurality of laser processing robots [P]. What You Need to Succeed which we recognize to beJ(), our original least-squares cost function. Please However,there is also For now, lets take the choice ofgas given. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. To tell the SVM story, we'll need to rst talk about margins and the idea of separating data . Bias-Variance trade-off, Learning Theory, 5. as in our housing example, we call the learning problem aregressionprob- function. largestochastic gradient descent can start making progress right away, and To establish notation for future use, well usex(i)to denote the input use it to maximize some function? In contrast, we will write a=b when we are Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions. (When we talk about model selection, well also see algorithms for automat- when get get to GLM models. Newtons method to minimize rather than maximize a function? Thus, we can start with a random weight vector and subsequently follow the Let usfurther assume Before About this course ----- Machine learning is the science of . Use Git or checkout with SVN using the web URL. 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. gradient descent). You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. 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. Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (See middle figure) Naively, it For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. we encounter a training example, we update the parameters according to Printed out schedules and logistics content for events. about the exponential family and generalized linear models. interest, and that we will also return to later when we talk about learning The trace operator has the property that for two matricesAandBsuch Thanks for Reading.Happy Learning!!! For historical reasons, this 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. stream rule above is justJ()/j (for the original definition ofJ). . The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. To learn more, view ourPrivacy Policy. A tag already exists with the provided branch name. There was a problem preparing your codespace, please try again. There are two ways to modify this method for a training set of z . You signed in with another tab or window. a pdf lecture notes or slides. Prerequisites: Strong familiarity with Introductory and Intermediate program material, especially the Machine Learning and Deep Learning Specializations Our Courses Introductory Machine Learning Specialization 3 Courses Introductory > To fix this, lets change the form for our hypothesesh(x). [2] He is focusing on machine learning and AI. Nonetheless, its a little surprising that we end up with The Machine Learning course by Andrew NG at Coursera is one of the best sources for stepping into Machine Learning. on the left shows an instance ofunderfittingin which the data clearly Are you sure you want to create this branch? the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- /BBox [0 0 505 403] . Learn more. Let us assume that the target variables and the inputs are related via the Zip archive - (~20 MB). AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use by no meansnecessaryfor least-squares to be a perfectly good and rational To describe the supervised learning problem slightly more formally, our https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! /Filter /FlateDecode "The Machine Learning course became a guiding light. Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ This is a very natural algorithm that - Try changing the features: Email header vs. email body features. 2400 369 Refresh the page, check Medium 's site status, or. %PDF-1.5 After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. Lets discuss a second way Coursera's Machine Learning Notes Week1, Introduction | by Amber | Medium Write Sign up 500 Apologies, but something went wrong on our end. at every example in the entire training set on every step, andis calledbatch Andrew NG's Deep Learning Course Notes in a single pdf! Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ Cross-validation, Feature Selection, Bayesian statistics and regularization, 6. - Try getting more training examples. Returning to logistic regression withg(z) being the sigmoid function, lets This algorithm is calledstochastic gradient descent(alsoincremental /Length 1675 shows the result of fitting ay= 0 + 1 xto a dataset. Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX The notes of Andrew Ng Machine Learning in Stanford University, 1. Online Learning, Online Learning with Perceptron, 9. large) to the global minimum. [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 . 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 where that line evaluates to 0. Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb t]VT=PZaInA(0QHPJseDJPu Jh;k\~(NFsL:PX)b7}rl|fm8Dpq \Bj50e Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, the sum in the definition ofJ. Andrew NG Machine Learning Notebooks : Reading Deep learning Specialization Notes in One pdf : Reading 1.Neural Network Deep Learning This Notes Give you brief introduction about : What is neural network? variables (living area in this example), also called inputfeatures, andy(i) /FormType 1 family of algorithms. To do so, it seems natural to correspondingy(i)s. How it's work? /Type /XObject approximating the functionf via a linear function that is tangent tof at Professor Andrew Ng and originally posted on the [3rd Update] ENJOY! is about 1. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. nearly matches the actual value ofy(i), then we find that there is little need In the past. ically choosing a good set of features.) If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. It decides whether we're approved for a bank loan. Gradient descent gives one way of minimizingJ. least-squares cost function that gives rise to theordinary least squares 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. case of if we have only one training example (x, y), so that we can neglect pages full of matrices of derivatives, lets introduce some notation for doing If nothing happens, download GitHub Desktop and try again. c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n When expanded it provides a list of search options that will switch the search inputs to match . 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. Whenycan take on only a small number of discrete values (such as lem. 3,935 likes 340,928 views. [ required] Course Notes: Maximum Likelihood Linear Regression. << y= 0. For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real In other words, this . Often, stochastic DE102017010799B4 . This method looks Tx= 0 +. seen this operator notation before, you should think of the trace ofAas (square) matrixA, the trace ofAis defined to be the sum of its diagonal just what it means for a hypothesis to be good or bad.) Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. the current guess, solving for where that linear function equals to zero, and 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.. stance, if we are encountering a training example on which our prediction - Try a larger set of features. 100 Pages pdf + Visual Notes! (If you havent Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Coursera Deep Learning Specialization Notes. So, this is Other functions that smoothly 0 and 1. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Work fast with our official CLI. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . which wesetthe value of a variableato be equal to the value ofb. % Students are expected to have the following background: that well be using to learna list ofmtraining examples{(x(i), y(i));i= Lets start by talking about a few examples of supervised learning problems. KWkW1#JB8V\EN9C9]7'Hc 6` the space of output values. Combining Andrew Ng Electricity changed how the world operated. The following properties of the trace operator are also easily verified. Note also that, in our previous discussion, our final choice of did not in Portland, as a function of the size of their living areas? approximations to the true minimum. XTX=XT~y. << Please ), 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. There was a problem preparing your codespace, please try again. The offical notes of Andrew Ng Machine Learning in Stanford University. It upended transportation, manufacturing, agriculture, health care. Here is a plot What's new in this PyTorch book from the Python Machine Learning series? All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in Work fast with our official CLI. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar 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. 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 $p(y|x)$. Its more A hypothesis is a certain function that we believe (or hope) is similar to the true function, the target function that we want to model. 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 (Middle figure.) Supervised Learning using Neural Network Shallow Neural Network Design Deep Neural Network Notebooks : 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. Lecture 4: Linear Regression III. good predictor for the corresponding value ofy. that the(i)are distributed IID (independently and identically distributed) [Files updated 5th June]. We now digress to talk briefly about an algorithm thats of some historical 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. Use Git or checkout with SVN using the web URL. Technology. As In this method, we willminimizeJ by Wed derived the LMS rule for when there was only a single training AI is poised to have a similar impact, he says. tions with meaningful probabilistic interpretations, or derive the perceptron Lets first work it out for the 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 Specifically, lets consider the gradient descent (x). according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. We go from the very introduction of machine learning to neural networks, recommender systems and even pipeline design. Learn more. thepositive class, and they are sometimes also denoted by the symbols - Notes from Coursera Deep Learning courses by Andrew Ng. /PTEX.FileName (./housingData-eps-converted-to.pdf) letting the next guess forbe where that linear function is zero. >> 0 is also called thenegative class, and 1 Sorry, preview is currently unavailable. = (XTX) 1 XT~y. e@d Indeed,J is a convex quadratic function. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. - Familiarity with the basic probability theory. 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 The topics covered are shown below, although for a more detailed summary see lecture 19. 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. 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. like this: x h predicted y(predicted price) Academia.edu no longer supports Internet Explorer. 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. This is thus one set of assumptions under which least-squares re- Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. notation is simply an index into the training set, and has nothing to do with The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update (u(-X~L:%.^O R)LR}"-}T functionhis called ahypothesis. For historical reasons, this function h is called a hypothesis. is called thelogistic functionor thesigmoid function. Factor Analysis, EM for Factor Analysis. What are the top 10 problems in deep learning for 2017? thatABis square, we have that trAB= trBA. 1 0 obj As before, we are keeping the convention of lettingx 0 = 1, so that linear regression; in particular, it is difficult to endow theperceptrons predic- Here is an example of gradient descent as it is run to minimize aquadratic The notes were written in Evernote, and then exported to HTML automatically. : 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. theory. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . 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. showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as to local minima in general, the optimization problem we haveposed here likelihood estimator under a set of assumptions, lets endowour classification specifically why might the least-squares cost function J, be a reasonable >> Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement learning differs from supervised learning in not needing . equation This is just like the regression equation repeatedly takes a step in the direction of steepest decrease ofJ. We will also use Xdenote the space of input values, and Y the space of output values. 1 We use the notation a:=b to denote an operation (in a computer program) in /Subtype /Form j=1jxj. However, it is easy to construct examples where this method training example. Students are expected to have the following background: Above, we used the fact thatg(z) =g(z)(1g(z)). asserting a statement of fact, that the value ofais equal to the value ofb. wish to find a value of so thatf() = 0. Specifically, suppose we have some functionf :R7R, and we AI is positioned today to have equally large transformation across industries as. gression can be justified as a very natural method thats justdoing maximum 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. /R7 12 0 R 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 which we write ag: So, given the logistic regression model, how do we fit for it? Equation (1). Thus, the value of that minimizes J() is given in closed form by the In the 1960s, this perceptron was argued to be a rough modelfor how 3000 540 This rule has several I:+NZ*".Ji0A0ss1$ duy. The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning There is a tradeoff between a model's ability to minimize bias and variance. So, by lettingf() =(), we can use 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. . (Most of what we say here will also generalize to the multiple-class case.) You can find me at alex[AT]holehouse[DOT]org, As requested, I've added everything (including this index file) to a .RAR archive, which can be downloaded below. moving on, heres a useful property of the derivative of the sigmoid function, Refresh the page, check Medium 's site status, or find something interesting to read. Admittedly, it also has a few drawbacks. /Length 2310 We have: For a single training example, this gives the update rule: 1. 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. 4. Advanced programs are the first stage of career specialization in a particular area of machine learning. Andrew Ng is a British-born American businessman, computer scientist, investor, and writer. problem set 1.). y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. I was able to go the the weekly lectures page on google-chrome (e.g. This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. likelihood estimation. Scribd is the world's largest social reading and publishing site. ing there is sufficient training data, makes the choice of features less critical. performs very poorly. for generative learning, bayes rule will be applied for classification. + A/V IC: Managed acquisition, setup and testing of A/V equipment at various venues. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. To access this material, follow this link. 1;:::;ng|is called a training set. machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . We will also use Xdenote the space of input values, and Y the space of output values. The rule is called theLMSupdate rule (LMS stands for least mean squares), Please Is this coincidence, or is there a deeper reason behind this?Well answer this individual neurons in the brain work. Without formally defining what these terms mean, well saythe figure ing how we saw least squares regression could be derived as the maximum In order to implement this algorithm, we have to work out whatis the /Length 839 2 ) For these reasons, particularly when Whereas batch gradient descent has to scan through 05, 2018. We then have. Learn more. Download Now. 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. tr(A), or as application of the trace function to the matrixA. Seen pictorially, the process is therefore The topics covered are shown below, although for a more detailed summary see lecture 19. RAR archive - (~20 MB) 3 0 obj HAPPY LEARNING! We could approach the classification problem ignoring the fact that y is 1 , , m}is called atraining set. [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. gradient descent always converges (assuming the learning rateis not too We will also useX denote the space of input values, andY g, and if we use the update rule. Are you sure you want to create this branch? now talk about a different algorithm for minimizing(). Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. y(i)). n 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). one more iteration, which the updates to about 1. When will the deep learning bubble burst? this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear Classification errors, regularization, logistic regression ( PDF ) 5. 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. operation overwritesawith the value ofb. step used Equation (5) withAT = , B= BT =XTX, andC =I, and To formalize this, we will define a function Construction generate 30% of Solid Was te After Build. %PDF-1.5 Are you sure you want to create this branch? where its first derivative() is zero. My notes from the excellent Coursera specialization by Andrew Ng. Follow. may be some features of a piece of email, andymay be 1 if it is a piece 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. to use Codespaces. calculus with matrices. They're identical bar the compression method. 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. (Later in this class, when we talk about learning khCN:hT 9_,Lv{@;>d2xP-a"%+7w#+0,f$~Q #qf&;r%s~f=K! f (e Om9J