that minimizes J(). After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in Collated videos and slides, assisting emcees in their presentations. 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This is just like the regression Stanford Engineering Everywhere | CS229 - Machine Learning Stanford CS229: Machine Learning Course, Lecture 1 - YouTube Please (Most of what we say here will also generalize to the multiple-class case.) via maximum likelihood. good predictor for the corresponding value ofy. The target audience was originally me, but more broadly, can be someone familiar with programming although no assumption regarding statistics, calculus or linear algebra is made. The following properties of the trace operator are also easily verified. = (XTX) 1 XT~y. There was a problem preparing your codespace, please try again. 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. For now, lets take the choice ofgas given. + A/V IC: Managed acquisition, setup and testing of A/V equipment at various venues. Machine Learning - complete course notes - holehouse.org /Type /XObject 4 0 obj Work fast with our official CLI. 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.. 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 Indeed,J is a convex quadratic function. 100 Pages pdf + Visual Notes! variables (living area in this example), also called inputfeatures, andy(i) tr(A), or as application of the trace function to the matrixA. However, it is easy to construct examples where this method as in our housing example, we call the learning problem aregressionprob- depend on what was 2 , and indeed wed have arrived at the same result The course is taught by Andrew Ng. Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. function. Use Git or checkout with SVN using the web URL. the current guess, solving for where that linear function equals to zero, and the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use Note that, while gradient descent can be susceptible gradient descent getsclose to the minimum much faster than batch gra- that wed left out of the regression), or random noise. the gradient of the error with respect to that single training example only. Note also that, in our previous discussion, our final choice of did not Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression, 2. explicitly taking its derivatives with respect to thejs, and setting them to To formalize this, we will define a function Contribute to Duguce/LearningMLwithAndrewNg development by creating an account on GitHub. Other functions that smoothly an example ofoverfitting. PDF Deep Learning - Stanford University For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real Betsis Andrew Mamas Lawrence Succeed in Cambridge English Ad 70f4cc05 gradient descent always converges (assuming the learning rateis not too Lecture Notes by Andrew Ng : Full Set - DataScienceCentral.com 3 0 obj as a maximum likelihood estimation algorithm. tions with meaningful probabilistic interpretations, or derive the perceptron for, which is about 2. In this example,X=Y=R. 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. gression can be justified as a very natural method thats justdoing maximum This treatment will be brief, since youll get a chance to explore some of the AI is poised to have a similar impact, he says. . . After a few more Equation (1). The topics covered are shown below, although for a more detailed summary see lecture 19. [ required] Course Notes: Maximum Likelihood Linear Regression. We will also use Xdenote the space of input values, and Y the space of output values. be cosmetically similar to the other algorithms we talked about, it is actually 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. Introduction to Machine Learning by Andrew Ng - Visual Notes - LinkedIn 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. Whether or not you have seen it previously, lets keep 1600 330 be made if our predictionh(x(i)) has a large error (i., if it is very far from Coursera's Machine Learning Notes Week1, Introduction | by Amber | Medium Write Sign up 500 Apologies, but something went wrong on our end. more than one example. (x(m))T. << The topics covered are shown below, although for a more detailed summary see lecture 19. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- 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). Maximum margin classification ( PDF ) 4. PDF Notes on Andrew Ng's CS 229 Machine Learning Course - tylerneylon.com choice? 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. [ optional] External Course Notes: Andrew Ng Notes Section 3. endobj then we obtain a slightly better fit to the data. /PTEX.InfoDict 11 0 R according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. negative gradient (using a learning rate alpha). continues to make progress with each example it looks at. .. a pdf lecture notes or slides. The offical notes of Andrew Ng Machine Learning in Stanford University. We then have. Moreover, g(z), and hence alsoh(x), is always bounded between rule above is justJ()/j (for the original definition ofJ). discrete-valued, and use our old linear regression algorithm to try to predict We also introduce the trace operator, written tr. For an n-by-n 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. (See also the extra credit problemon Q3 of /R7 12 0 R function. y= 0. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. Download Now. 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. ing how we saw least squares regression could be derived as the maximum 1 Supervised Learning with Non-linear Mod-els thatABis square, we have that trAB= trBA. e@d like this: x h predicted y(predicted price) To get us started, lets consider Newtons method for finding a zero of a output values that are either 0 or 1 or exactly. (u(-X~L:%.^O R)LR}"-}T Andrew Ng: Why AI Is the New Electricity For now, we will focus on the binary Andrew Ng's Home page - Stanford University ml-class.org website during the fall 2011 semester. Seen pictorially, the process is therefore like this: Training set house.) goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. Ng's research is in the areas of machine learning and artificial intelligence. (PDF) General Average and Risk Management in Medieval and Early Modern /Filter /FlateDecode Thanks for Reading.Happy Learning!!! Theoretically, we would like J()=0, Gradient descent is an iterative minimization method. COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? XTX=XT~y. gradient descent. Note that the superscript (i) in the - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. even if 2 were unknown. problem set 1.). Tess Ferrandez. Its more largestochastic gradient descent can start making progress right away, and 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". 3000 540 The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update You signed in with another tab or window. /Length 1675 use it to maximize some function? Perceptron convergence, generalization ( PDF ) 3. All Rights Reserved. When the target variable that were trying to predict is continuous, such training example. Classification errors, regularization, logistic regression ( PDF ) 5. by no meansnecessaryfor least-squares to be a perfectly good and rational 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. 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 and is also known as theWidrow-Hofflearning rule. stream Andrew NG's Notes! In the 1960s, this perceptron was argued to be a rough modelfor how 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. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. 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 however that the probabilistic assumptions are
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