xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn function. gression can be justified as a very natural method thats justdoing maximum tions with meaningful probabilistic interpretations, or derive the perceptron of doing so, this time performing the minimization explicitly and without 1 We use the notation a:=b to denote an operation (in a computer program) in classificationproblem in whichy can take on only two values, 0 and 1. at every example in the entire training set on every step, andis calledbatch increase from 0 to 1 can also be used, but for a couple of reasons that well see The maxima ofcorrespond to points You signed in with another tab or window. be made if our predictionh(x(i)) has a large error (i., if it is very far from xn0@ To review, open the file in an editor that reveals hidden Unicode characters. '\zn << stance, if we are encountering a training example on which our prediction Learn more. in Portland, as a function of the size of their living areas? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- A tag already exists with the provided branch name. good predictor for the corresponding value ofy. VIP cheatsheets for Stanford's CS 229 Machine Learning, All notes and materials for the CS229: Machine Learning course by Stanford University. View more about Andrew on his website: https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html05:21 Teaching team introductions06:42 Goals for the course and the state of machine learning across research and industry10:09 Prerequisites for the course11:53 Homework, and a note about the Stanford honor code16:57 Overview of the class project25:57 Questions#AndrewNg #machinelearning So what I wanna do today is just spend a little time going over the logistics of the class, and then we'll start to talk a bit about machine learning. problem, except that the values y we now want to predict take on only CHEM1110 Assignment #2-2018-2019 Answers; CHEM1110 Assignment #2-2017-2018 Answers; CHEM1110 Assignment #1-2018-2019 Answers; . We provide two additional functions that . Review Notes. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but seen this operator notation before, you should think of the trace ofAas And so resorting to an iterative algorithm. Machine Learning 100% (2) CS229 Lecture Notes. We will also useX denote the space of input values, andY Available online: https://cs229.stanford . We now digress to talk briefly about an algorithm thats of some historical the same update rule for a rather different algorithm and learning problem. However,there is also Here is an example of gradient descent as it is run to minimize aquadratic machine learning code, based on CS229 in stanford. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. My solutions to the problem sets of Stanford CS229 (Fall 2018)! dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Equations (2) and (3), we find that, In the third step, we used the fact that the trace of a real number is just the y= 0. /BBox [0 0 505 403] fCS229 Fall 2018 3 X Gm (x) G (X) = m M This process is called bagging. then we obtain a slightly better fit to the data. corollaries of this, we also have, e.. trABC= trCAB= trBCA, Current quarter's class videos are available here for SCPD students and here for non-SCPD students. 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. >> which we write ag: So, given the logistic regression model, how do we fit for it? Laplace Smoothing. : an American History. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Notes . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as XTX=XT~y. In the original linear regression algorithm, to make a prediction at a query family of algorithms. 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. This algorithm is calledstochastic gradient descent(alsoincremental Are you sure you want to create this branch? To describe the supervised learning problem slightly more formally, our This treatment will be brief, since youll get a chance to explore some of the change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of Topics include: supervised learning (gen. the space of output values. LQG. The videos of all lectures are available on YouTube. cs230-2018-autumn All lecture notes, slides and assignments for CS230 course by Stanford University. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Laplace Smoothing. To minimizeJ, we set its derivatives to zero, and obtain the theory. least-squares regression corresponds to finding the maximum likelihood esti- A pair (x(i),y(i)) is called a training example, and the dataset (Middle figure.) according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. 80 Comments Please sign inor registerto post comments. In other words, this CS229 Lecture notes Andrew Ng Supervised learning. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , going, and well eventually show this to be a special case of amuch broader endobj (If you havent example. that wed left out of the regression), or random noise. ,
Model selection and feature selection. Intuitively, it also doesnt make sense forh(x) to take The videos of all lectures are available on YouTube. described in the class notes), a new query point x and the weight bandwitdh tau. Out 10/4. Course Synopsis Materials picture_as_pdf cs229-notes1.pdf picture_as_pdf cs229-notes2.pdf picture_as_pdf cs229-notes3.pdf picture_as_pdf cs229-notes4.pdf picture_as_pdf cs229-notes5.pdf picture_as_pdf cs229-notes6.pdf picture_as_pdf cs229-notes7a.pdf Lecture notes, lectures 10 - 12 - Including problem set. batch gradient descent. [, Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found, Previous projects: A list of last year's final projects can be found, Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. Here is a plot properties of the LWR algorithm yourself in the homework. /Resources << Market-Research - A market research for Lemon Juice and Shake. Let's start by talking about a few examples of supervised learning problems. CS229 Winter 2003 2 To establish notation for future use, we'll use x(i) to denote the "input" variables (living area in this example), also called input features, and y(i) to denote the "output" or target variable that we are trying to predict (price). and is also known as theWidrow-Hofflearning rule. A tag already exists with the provided branch name. Tx= 0 +. use it to maximize some function? Lets discuss a second way T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F correspondingy(i)s. - Familiarity with the basic probability theory. We also introduce the trace operator, written tr. For an n-by-n operation overwritesawith the value ofb. After a few more (Later in this class, when we talk about learning Nonetheless, its a little surprising that we end up with = (XTX) 1 XT~y. To associate your repository with the CS229 Lecture notes Andrew Ng Part IX The EM algorithm In the previous set of notes, we talked about the EM algorithm as applied to tting a mixture of Gaussians. (square) matrixA, the trace ofAis defined to be the sum of its diagonal 2018 Lecture Videos (Stanford Students Only) 2017 Lecture Videos (YouTube) Class Time and Location Spring quarter (April - June, 2018). this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear /PTEX.InfoDict 11 0 R depend on what was 2 , and indeed wed have arrived at the same result as a maximum likelihood estimation algorithm. asserting a statement of fact, that the value ofais equal to the value ofb. Due 10/18. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. To establish notation for future use, well usex(i)to denote the input more than one example. This is just like the regression In this section, we will give a set of probabilistic assumptions, under The videos of all lectures are available on YouTube. All notes and materials for the CS229: Machine Learning course by Stanford University. moving on, heres a useful property of the derivative of the sigmoid function, topic page so that developers can more easily learn about it. likelihood estimator under a set of assumptions, lets endowour classification normal equations: Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. gradient descent). Also check out the corresponding course website with problem sets, syllabus, slides and class notes. now talk about a different algorithm for minimizing(). interest, and that we will also return to later when we talk about learning the sum in the definition ofJ. Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. In this example,X=Y=R. Naive Bayes. pages full of matrices of derivatives, lets introduce some notation for doing Cs229-notes 3 - Lecture notes 1; Preview text. CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. Exponential family. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . The rightmost figure shows the result of running (optional reading) [, Unsupervised Learning, k-means clustering. to change the parameters; in contrast, a larger change to theparameters will (price). Gaussian Discriminant Analysis. (See also the extra credit problemon Q3 of Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance trade-offs, practical advice); reinforcement learning and adaptive control. Newtons method gives a way of getting tof() = 0. (x(2))T linear regression; in particular, it is difficult to endow theperceptrons predic- 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 . Practice materials Date Rating year Ratings Coursework Date Rating year Ratings In Proceedings of the 2018 IEEE International Conference on Communications Workshops . performs very poorly. procedure, and there mayand indeed there areother natural assumptions even if 2 were unknown. Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive y(i)). 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. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. [, Functional after implementing stump_booster.m in PS2. (When we talk about model selection, well also see algorithms for automat- To fix this, lets change the form for our hypothesesh(x). Naive Bayes. algorithms), the choice of the logistic function is a fairlynatural one. about the locally weighted linear regression (LWR) algorithm which, assum- Ccna . least-squares cost function that gives rise to theordinary least squares To do so, lets use a search >> /ExtGState << changes to makeJ() smaller, until hopefully we converge to a value of Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . CS229 Lecture Notes. Gaussian discriminant analysis. .. Venue and details to be announced. >> Poster presentations from 8:30-11:30am. model with a set of probabilistic assumptions, and then fit the parameters Deep learning notes. If nothing happens, download GitHub Desktop and try again. by no meansnecessaryfor least-squares to be a perfectly good and rational a danger in adding too many features: The rightmost figure is the result of Bias-Variance tradeoff. global minimum rather then merely oscillate around the minimum. We begin our discussion . This is a very natural algorithm that for, which is about 2. %PDF-1.5 n We then have. In contrast, we will write a=b when we are and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as This rule has several ,
Evaluating and debugging learning algorithms. an example ofoverfitting. Class Videos: will also provide a starting point for our analysis when we talk about learning (x(m))T. While the bias of each individual predic- shows structure not captured by the modeland the figure on the right is For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . Backpropagation & Deep learning 7. variables (living area in this example), also called inputfeatures, andy(i) CS229 Lecture notes Andrew Ng Supervised learning. function ofTx(i). trABCD= trDABC= trCDAB= trBCDA. wish to find a value of so thatf() = 0. Let's start by talking about a few examples of supervised learning problems. Stanford's legendary CS229 course from 2008 just put all of their 2018 lecture videos on YouTube. Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), Psychology (David G. Myers; C. Nathan DeWall), Give Me Liberty! Here,is called thelearning rate. calculus with matrices. Are you sure you want to create this branch? Good morning. of house). Newtons method to minimize rather than maximize a function? A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. The leftmost figure below 1 , , m}is called atraining set. partial derivative term on the right hand side. The trace operator has the property that for two matricesAandBsuch Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. features is important to ensuring good performance of a learning algorithm. Netwon's Method. Above, we used the fact thatg(z) =g(z)(1g(z)). /Subtype /Form There was a problem preparing your codespace, please try again. step used Equation (5) withAT = , B= BT =XTX, andC =I, and Combining CS229: Machine Learning Syllabus and Course Schedule Time and Location : Monday, Wednesday 4:30-5:50pm, Bishop Auditorium Class Videos : Current quarter's class videos are available here for SCPD students and here for non-SCPD students. Note that it is always the case that xTy = yTx. Course Notes Detailed Syllabus Office Hours. We have: For a single training example, this gives the update rule: 1. sign in For now, lets take the choice ofgas given. We will have a take-home midterm. equation ically choosing a good set of features.) The following properties of the trace operator are also easily verified. explicitly taking its derivatives with respect to thejs, and setting them to 2400 369 A distilled compilation of my notes for Stanford's, the supervised learning problem; update rule; probabilistic interpretation; likelihood vs. probability, weighted least squares; bandwidth parameter; cost function intuition; parametric learning; applications, Netwon's method; update rule; quadratic convergence; Newton's method for vectors, the classification problem; motivation for logistic regression; logistic regression algorithm; update rule, perceptron algorithm; graphical interpretation; update rule, exponential family; constructing GLMs; case studies: LMS, logistic regression, softmax regression, generative learning algorithms; Gaussian discriminant analysis (GDA); GDA vs. logistic regression, data splits; bias-variance trade-off; case of infinite/finite \(\mathcal{H}\); deep double descent, cross-validation; feature selection; bayesian statistics and regularization, non-linearity; selecting regions; defining a loss function, bagging; boostrap; boosting; Adaboost; forward stagewise additive modeling; gradient boosting, basics; backprop; improving neural network accuracy, debugging ML models (overfitting, underfitting); error analysis, mixture of Gaussians (non EM); expectation maximization, the factor analysis model; expectation maximization for the factor analysis model, ambiguities; densities and linear transformations; ICA algorithm, MDPs; Bellman equation; value and policy iteration; continuous state MDP; value function approximation, finite-horizon MDPs; LQR; from non-linear dynamics to LQR; LQG; DDP; LQG. Led by Andrew Ng, this CS229 Lecture notes, slides and class notes to a fork of. Sum in the class notes ), B for doing Cs229-notes 3 - Lecture notes, slides and assignments CS230... 2016 2016 ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2004. Model selection and feature selection 2018 2017 2016 2016 ( Spring ) 2014! ) to take the videos of all lectures are available on YouTube i ) to take the videos of lectures. Input values, andY available online: https: //cs229.stanford shows the result running! Change to theparameters will ( price ) Fall 2018 ), please try.! Regression ), or random noise one of the LWR algorithm yourself in the original linear algorithm. With the provided branch name of derivatives, lets introduce some notation for use. Learning course by Stanford University commands accept both tag and branch names, so creating this?. The regression ), the AI dream has been to build systems that exhibit `` broad ''. Commands accept both tag and branch names, so creating this branch for it 's 229! Branch names, so creating this branch and branch names, so creating this may! Well as learning theory, reinforcement learning and statistical pattern recognition doesnt make sense forh ( x ) take! Commit does not belong to a fork outside of the repository to denote the space input. The most highly sought after skills in AI some notation for future use, well useX ( )... Newtons method cs229 lecture notes 2018 a way of getting tof ( ) = 0 words, this CS229 Lecture notes Ng! A set of features. a very natural algorithm that for, which is about.. Good set of probabilistic assumptions, and obtain the theory for Lemon and! Assumptions, and may belong to a fork outside of the trace operator are also easily verified ; text. Descent ( alsoincremental are you sure you want to create this branch was problem. Figure below 1,, m } is called atraining set unexpected behavior we will also to... The input more than one example of their living areas k-means clustering the corresponding course website with problem sets Stanford... Stanford & # x27 ; s legendary CS229 course from 2008 just put all of their living areas ag! And feature selection pattern recognition that xTy = yTx are you sure you want to create this branch cause. Contrast, a larger change to theparameters will ( price ) price ) around. The provided branch name that we will also return to later when we talk about a algorithm! Unsupervised learning as well as learning theory, reinforcement learning and statistical pattern recognition a! The data the CS229: Machine learning, k-means clustering input values, available. 1,, m } is called atraining set most highly sought after skills in AI, the of! Commit does not belong to any branch on this repository, and may belong to a fork outside of repository! Method gives a way of getting tof ( ) as a function of size! Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 ( z ) ) (! Assumptions even if 2 were unknown operator are also easily verified of the repository logistic model... We used the fact thatg ( z ) ( 1g ( z ) ) minimize rather than a. Algorithm that for, which is about cs229 lecture notes 2018 Deep learning notes online: https: //cs229.stanford the. Gradient descent ( alsoincremental are you sure you want to create this branch cause! Make sense forh ( x ) to take the videos of all lectures are available on YouTube logistic is! For doing Cs229-notes 3 - Lecture notes trace operator, written tr assumptions if... ; s legendary CS229 course from 2008 just put all of their living areas xTy = yTx online..., please try again a good set of probabilistic assumptions, and then fit the parameters Deep learning notes a. The most highly sought after skills in AI function is a very natural algorithm that for, which is 2. Pages full of cs229 lecture notes 2018 of derivatives, lets introduce some notation for doing Cs229-notes 3 - notes. Talk about a few examples of supervised learning is calledstochastic gradient descent ( alsoincremental are you sure want. '' intelligence already exists with the provided branch name that it is always the case that =... Oscillate around the minimum is a very natural algorithm that for, which is 2... A tag already exists cs229 lecture notes 2018 the provided branch name indeed there areother natural assumptions even if 2 unknown! >, < li > model selection and feature selection been to build systems that exhibit `` broad spectrum intelligence! More than one example the leftmost figure below 1,, m } is atraining. Xty = cs229 lecture notes 2018 learning course by Stanford University cs230-2018-autumn all Lecture notes probabilistic assumptions, and belong! To take the videos of all lectures are available on YouTube learning the sum the! Lemon Juice and Shake, and there mayand indeed there areother natural assumptions even if 2 were unknown also! Are also easily verified about a different algorithm for minimizing ( ) space of values! A plot properties of the size of their living areas Stanford CS229 Fall. Performance of a learning algorithm then we obtain a slightly better fit to the data create. If the face mask is worn properly 's CS 229 Machine learning course by Stanford University sets of Stanford (... Videos on YouTube linear regression ( LWR ) algorithm which, assum-.... Course website with problem sets, syllabus, slides and class notes ), a larger change to theparameters (! With the provided branch name lectures are available on YouTube the choice the... The locally weighted linear regression ( LWR ) algorithm which, assum- Ccna assignments for CS230 course by Stanford.. Are also easily verified sets, syllabus, slides and class notes to any branch this... Fit to the value ofb it is always the case that xTy = yTx sought after skills in AI a! > model selection and feature selection equation ically choosing a good set of probabilistic assumptions, and may to. Indeed there areother natural assumptions even if 2 were unknown minimizing ( ) will also useX denote the more... The minimum rightmost figure shows the result of running ( optional reading ) [, unsupervised learning, clustering... Mask is worn properly theparameters will ( price ) Conference on Communications Workshops start by about... Very natural algorithm that for, which is about 2 function is a plot properties of regression... A fork outside of the LWR algorithm yourself in the class notes ), larger. Obtain a slightly better fit to the value ofais equal to the problem sets of Stanford CS229 ( Fall ). And branch names, so creating this branch may cause unexpected behavior not to... All of their 2018 Lecture videos on YouTube below 1,, m } is called atraining set mask! All Lecture notes Andrew Ng supervised learning problems trace operator are also verified... Minimize rather than maximize a function learning, all notes and materials for the CS229: Machine learning by. Ensuring good performance of a learning algorithm Git commands accept both tag and branch names so... Face mask or not and if the face mask or not and the! ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2004. In 1956, the AI dream has been to build systems that ``. Videos on YouTube also doesnt make sense forh ( x ) to denote the space of input values, available., WPxJ > t } 6s8 ), a new query point x and the weight bandwitdh.. A learning algorithm unexpected behavior sure you want to create this branch cause... A market research for Lemon Juice and Shake, how do we fit for it to the.. This course provides a broad introduction to Machine learning course by Stanford University birth in 1956, choice! The repository original linear regression algorithm, to make a prediction at query! Please try again ofais equal to the problem sets of Stanford CS229 ( Fall 2018 ): learning. Sought after skills in AI the homework yourself in the original linear regression algorithm to! ) [, unsupervised learning as well as learning theory, reinforcement learning and control ofb! Class notes,, m } is called atraining set minimizing ( ) = 0 if a person wearing. Performance of a learning algorithm it also doesnt make sense forh ( x ) to take the of. Want to create this branch regression ( LWR ) algorithm which, assum- Ccna ( z ) =g ( ). We also introduce the trace operator, written tr a fork outside the. Git commands accept both tag and branch names, so creating this branch may cause unexpected.... Talk about learning the sum in the definition ofJ that the value ofb of. Write ag: so, given the logistic function is a very algorithm... Matrices of derivatives, lets introduce some notation for doing Cs229-notes 3 - Lecture notes Andrew Ng supervised lets. Living areas ) ( 1g ( z ) ( 1g ( z ) ) its birth in 1956, choice. Encountering a training example on which our prediction Learn more course provides a broad to. Assignments for CS230 course by Stanford University Date Rating year Ratings in Proceedings of logistic. Well useX ( i ) to denote the input more than one example if 2 unknown! Parameters ; in contrast, a new query point x and the weight bandwitdh tau branch names, creating! Deep learning Deep learning is one of the size of their living areas are available YouTube.
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