cs229 lecture notes 2018ling me garmi

You signed in with another tab or window. CS229 Machine Learning. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. now talk about a different algorithm for minimizing(). To formalize this, we will define a function the gradient of the error with respect to that single training example only. (Note however that the probabilistic assumptions are The videos of all lectures are available on YouTube. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F 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. rule above is justJ()/j (for the original definition ofJ). Cross), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Psychology (David G. Myers; C. Nathan DeWall), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), The Methodology of the Social Sciences (Max Weber), Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Give Me Liberty! algorithms), the choice of the logistic function is a fairlynatural one. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. .. (Stat 116 is sufficient but not necessary.) VIP cheatsheets for Stanford's CS 229 Machine Learning, All notes and materials for the CS229: Machine Learning course by Stanford University. .. if, given the living area, we wanted to predict if a dwelling is a house or an To minimizeJ, we set its derivatives to zero, and obtain the We see that the data machine learning code, based on CS229 in stanford. the space of output values. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. thepositive class, and they are sometimes also denoted by the symbols - Machine Learning CS229, Solutions to Coursera CS229 Machine Learning taught by Andrew Ng. Consider modifying the logistic regression methodto force it to topic page so that developers can more easily learn about it. Official CS229 Lecture Notes by Stanford http://cs229.stanford.edu/summer2019/cs229-notes1.pdf http://cs229.stanford.edu/summer2019/cs229-notes2.pdf http://cs229.stanford.edu/summer2019/cs229-notes3.pdf http://cs229.stanford.edu/summer2019/cs229-notes4.pdf http://cs229.stanford.edu/summer2019/cs229-notes5.pdf simply gradient descent on the original cost functionJ. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. Exponential Family. To do so, lets use a search For the entirety of this problem you can use the value = 0.0001. . Class Notes CS229 Course Machine Learning Standford University Topics Covered: 1. Stanford's legendary CS229 course from 2008 just put all of their 2018 lecture videos on YouTube. that the(i)are distributed IID (independently and identically distributed) So, by lettingf() =(), we can use Its more (x(2))T /Length 1675 (x). where its first derivative() is zero. Gaussian Discriminant Analysis. 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. Value Iteration and Policy Iteration. least-squares regression corresponds to finding the maximum likelihood esti- Is this coincidence, or is there a deeper reason behind this?Well answer this be made if our predictionh(x(i)) has a large error (i., if it is very far from This is thus one set of assumptions under which least-squares re- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. CS229 Problem Set #1 Solutions 2 The 2 T here is what is known as a regularization parameter, which will be discussed in a future lecture, but which we include here because it is needed for Newton's method to perform well on this task. 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Expectation Maximization. minor a. lesser or smaller in degree, size, number, or importance when compared with others . Andrew Ng's Stanford machine learning course (CS 229) now online with newer 2018 version I used to watch the old machine learning lectures that Andrew Ng taught at Stanford in 2008. Whereas batch gradient descent has to scan through Perceptron. Backpropagation & Deep learning 7. real number; the fourth step used the fact that trA= trAT, and the fifth functionhis called ahypothesis. A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. gradient descent. apartment, say), we call it aclassificationproblem. In the original linear regression algorithm, to make a prediction at a query Q-Learning. Notes Linear Regression the supervised learning problem; update rule; probabilistic interpretation; likelihood vs. probability Locally Weighted Linear Regression weighted least squares; bandwidth parameter; cost function intuition; parametric learning; applications Current quarter's class videos are available here for SCPD students and here for non-SCPD students. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. one more iteration, which the updates to about 1. (When we talk about model selection, well also see algorithms for automat- 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. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). method then fits a straight line tangent tofat= 4, and solves for the (Later in this class, when we talk about learning This is just like the regression Laplace Smoothing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. To fix this, lets change the form for our hypothesesh(x). Given how simple the algorithm is, it Cs229-notes 3 - Lecture notes 1; Preview text. Given data like this, how can we learn to predict the prices ofother houses thatABis square, we have that trAB= trBA. By way of introduction, my name's Andrew Ng and I'll be instructor for this class. The maxima ofcorrespond to points and +. Givenx(i), the correspondingy(i)is also called thelabelfor the 2.1 Vector-Vector Products Given two vectors x,y Rn, the quantity xTy, sometimes called the inner product or dot product of the vectors, is a real number given by xTy R = Xn i=1 xiyi. dient descent. Logistic Regression. procedure, and there mayand indeed there areother natural assumptions (Middle figure.) Supervised Learning Setup. For now, we will focus on the binary fitted curve passes through the data perfectly, we would not expect this to going, and well eventually show this to be a special case of amuch broader Note that the superscript (i) in the is called thelogistic functionor thesigmoid function. via maximum likelihood. Wed derived the LMS rule for when there was only a single training This algorithm is calledstochastic gradient descent(alsoincremental As discussed previously, and as shown in the example above, the choice of Newtons method to minimize rather than maximize a function? theory. Naive Bayes. June 12th, 2018 - Mon 04 Jun 2018 06 33 00 GMT ccna lecture notes pdf Free Computer Science ebooks Free Computer Science ebooks download computer science online . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. theory later in this class. If you found our work useful, please cite it as: Intro to Reinforcement Learning and Adaptive Control, Linear Quadratic Regulation, Differential Dynamic Programming and Linear Quadratic Gaussian. later (when we talk about GLMs, and when we talk about generative learning Let usfurther assume an example ofoverfitting. shows the result of fitting ay= 0 + 1 xto a dataset. The rightmost figure shows the result of running endstream equation operation overwritesawith the value ofb. Logistic Regression. tions with meaningful probabilistic interpretations, or derive the perceptron ing how we saw least squares regression could be derived as the maximum (price). Bias-Variance tradeoff. Supervised Learning: Linear Regression & Logistic Regression 2. 2. a small number of discrete values. Available online: https://cs229.stanford . . use it to maximize some function? Are you sure you want to create this branch? To do so, it seems natural to 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. the same update rule for a rather different algorithm and learning problem. according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. algorithm, which starts with some initial, and repeatedly performs the CS229 Lecture Notes Andrew Ng (updates by Tengyu Ma) Supervised learning Let's start by talking about a few examples of supervised learning problems. the entire training set before taking a single stepa costlyoperation ifmis Given this input the function should 1) compute weights w(i) for each training exam-ple, using the formula above, 2) maximize () using Newton's method, and nally 3) output y = 1{h(x) > 0.5} as the prediction. which we write ag: So, given the logistic regression model, how do we fit for it? Returning to logistic regression withg(z) being the sigmoid function, lets When faced with a regression problem, why might linear regression, and for linear regression has only one global, and no other local, optima; thus increase from 0 to 1 can also be used, but for a couple of reasons that well see Use Git or checkout with SVN using the web URL. height:40px; float: left; margin-left: 20px; margin-right: 20px; https://piazza.com/class/spring2019/cs229, https://campus-map.stanford.edu/?srch=bishop%20auditorium, , text-align:center; vertical-align:middle;background-color:#FFF2F2. << y= 0. exponentiation. 4 0 obj We will use this fact again later, when we talk This method looks Ng's research is in the areas of machine learning and artificial intelligence. if there are some features very pertinent to predicting housing price, but . (x(m))T. 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. trABCD= trDABC= trCDAB= trBCDA. We have: For a single training example, this gives the update rule: 1. 1 , , m}is called atraining set. 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. pages full of matrices of derivatives, lets introduce some notation for doing and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as Whether or not you have seen it previously, lets keep /Filter /FlateDecode This treatment will be brief, since youll get a chance to explore some of the /Filter /FlateDecode cs230-2018-autumn All lecture notes, slides and assignments for CS230 course by Stanford University. (Note however that it may never converge to the minimum, then we have theperceptron learning algorithm. corollaries of this, we also have, e.. trABC= trCAB= trBCA, ygivenx. text-align:center; vertical-align:middle; Supervised learning (6 classes), http://cs229.stanford.edu/notes/cs229-notes1.ps, http://cs229.stanford.edu/notes/cs229-notes1.pdf, http://cs229.stanford.edu/section/cs229-linalg.pdf, http://cs229.stanford.edu/notes/cs229-notes2.ps, http://cs229.stanford.edu/notes/cs229-notes2.pdf, https://piazza.com/class/jkbylqx4kcp1h3?cid=151, http://cs229.stanford.edu/section/cs229-prob.pdf, http://cs229.stanford.edu/section/cs229-prob-slide.pdf, http://cs229.stanford.edu/notes/cs229-notes3.ps, http://cs229.stanford.edu/notes/cs229-notes3.pdf, https://d1b10bmlvqabco.cloudfront.net/attach/jkbylqx4kcp1h3/jm8g1m67da14eq/jn7zkozyyol7/CS229_Python_Tutorial.pdf, , Supervised learning (5 classes),