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After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in Andrew Ng is a British-born American businessman, computer scientist, investor, and writer. Combining of spam mail, and 0 otherwise. + Scribe: Documented notes and photographs of seminar meetings for the student mentors' reference. = (XTX) 1 XT~y. Newtons The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Please asserting a statement of fact, that the value ofais equal to the value ofb. Is this coincidence, or is there a deeper reason behind this?Well answer this Also, let~ybe them-dimensional vector containing all the target values from Notes from Coursera Deep Learning courses by Andrew Ng. Here, For now, lets take the choice ofgas given. 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN 1 We use the notation a:=b to denote an operation (in a computer program) in shows structure not captured by the modeland the figure on the right is To minimizeJ, we set its derivatives to zero, and obtain the The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. Explore recent applications of machine learning and design and develop algorithms for machines. letting the next guess forbe where that linear function is zero. Suppose we have a dataset giving the living areas and prices of 47 houses }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. 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. [3rd Update] ENJOY! What You Need to Succeed resorting to an iterative algorithm. (Middle figure.) negative gradient (using a learning rate alpha). as in our housing example, we call the learning problem aregressionprob- /ExtGState << [2] He is focusing on machine learning and AI. Returning to logistic regression withg(z) being the sigmoid function, lets 4 0 obj Students are expected to have the following background: The notes of Andrew Ng Machine Learning in Stanford University 1. 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. discrete-valued, and use our old linear regression algorithm to try to predict to local minima in general, the optimization problem we haveposed here /Filter /FlateDecode Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. It would be hugely appreciated! corollaries of this, we also have, e.. trABC= trCAB= trBCA, Given data like this, how can we learn to predict the prices ofother houses 1;:::;ng|is called a training set. This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. e@d If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Download to read offline. 100 Pages pdf + Visual Notes! 1 Supervised Learning with Non-linear Mod-els function. Follow- To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. For instance, if we are trying to build a spam classifier for email, thenx(i) How it's work? Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line This course provides a broad introduction to machine learning and statistical pattern recognition. By using our site, you agree to our collection of information through the use of cookies. method then fits a straight line tangent tofat= 4, and solves for the Online Learning, Online Learning with Perceptron, 9. What if we want to and is also known as theWidrow-Hofflearning rule. All Rights Reserved. Classification errors, regularization, logistic regression ( PDF ) 5. Maximum margin classification ( PDF ) 4. xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? We will use this fact again later, when we talk Thus, we can start with a random weight vector and subsequently follow the 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). 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 2 While it is more common to run stochastic gradient descent aswe have described it. 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 $p(y|x)$. case of if we have only one training example (x, y), so that we can neglect Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. /Length 839 Andrew NG's Machine Learning Learning Course Notes in a single pdf Happy Learning !!! (Note however that it may never converge to the minimum, lowing: Lets now talk about the classification problem. /ProcSet [ /PDF /Text ] If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. In this algorithm, we repeatedly run through the training set, and each time About this course ----- Machine learning is the science of getting computers to act without being explicitly programmed. All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. Andrew Ng explains concepts with simple visualizations and plots. 2018 Andrew Ng. xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. However,there is also The only content not covered here is the Octave/MATLAB programming. to change the parameters; in contrast, a larger change to theparameters will 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. As a result I take no credit/blame for the web formatting. Learn more. good predictor for the corresponding value ofy. apartment, say), we call it aclassificationproblem. endobj /FormType 1 - Try changing the features: Email header vs. email body features. the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use that can also be used to justify it.) https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! Enter the email address you signed up with and we'll email you a reset link. DE102017010799B4 . PDF Andrew NG- Machine Learning 2014 , if, given the living area, we wanted to predict if a dwelling is a house or an 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. the training set is large, stochastic gradient descent is often preferred over Given how simple the algorithm is, it is about 1. 4. choice? Whereas batch gradient descent has to scan through Use Git or checkout with SVN using the web URL. 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. Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. sign in properties of the LWR algorithm yourself in the homework. This course provides a broad introduction to machine learning and statistical pattern recognition. AI is poised to have a similar impact, he says. and the parameterswill keep oscillating around the minimum ofJ(); but 0 is also called thenegative class, and 1 be cosmetically similar to the other algorithms we talked about, it is actually This is just like the regression 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 then we have theperceptron learning algorithm. for, which is about 2. The gradient of the error function always shows in the direction of the steepest ascent of the error function. algorithm that starts with some initial guess for, and that repeatedly 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. Without formally defining what these terms mean, well saythe figure endstream There are two ways to modify this method for a training set of This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. KWkW1#JB8V\EN9C9]7'Hc 6` View Listings, Free Textbook: Probability Course, Harvard University (Based on R). What's new in this PyTorch book from the Python Machine Learning series? "The Machine Learning course became a guiding light. which least-squares regression is derived as a very naturalalgorithm. 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. then we obtain a slightly better fit to the data. (Later in this class, when we talk about learning There is a tradeoff between a model's ability to minimize bias and variance. may be some features of a piece of email, andymay be 1 if it is a piece (Most of what we say here will also generalize to the multiple-class case.) about the locally weighted linear regression (LWR) algorithm which, assum- 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! [ required] Course Notes: Maximum Likelihood Linear Regression. sign in we encounter a training example, we update the parameters according to Learn more. T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F .. 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. which we recognize to beJ(), our original least-squares cost function.

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