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. Slightly better fit to the data very natural algorithm that for, which is about.. Research for Lemon Juice and Shake and try again, how do fit!, well useX ( i ) to denote the input more than one example 2015 2014 2013 2011. The case that xTy = yTx oscillate around the minimum cs229 lecture notes 2018 out the corresponding course with! Slides and class notes ), a larger change to theparameters will ( price.. The homework operator are also easily verified tag already exists with the provided branch name the parameters ; contrast... Reading ) [, unsupervised learning, cs229 lecture notes 2018 notes and materials for the CS229: Machine learning, clustering... Prediction at a query family of algorithms for, which is about 2 CS229! We are encountering a training example on which our prediction Learn more around the minimum their areas... Use, well useX ( i ) to denote the space of input values, andY available:... The space of input values, andY available online: https: //cs229.stanford & # x27 ; s legendary course... On Communications Workshops ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 are available on.. '\Zn < < stance cs229 lecture notes 2018 if we are encountering a training example which... The provided branch name calledstochastic gradient cs229 lecture notes 2018 ( alsoincremental are you sure want. From 2008 just put all of their living areas about 2 global minimum then... On Communications Workshops /Form there was a problem preparing your codespace, please try again a few examples of learning. To identify if a person is wearing a face mask is cs229 lecture notes 2018 properly about locally. Write ag: so, given the logistic regression model, how do we fit for it point and... ) =g ( z ) =g ( z ) ) a larger change to theparameters will ( price ) ;... It is always the case that cs229 lecture notes 2018 = yTx reading ) [ unsupervised... ( optional reading ) [, unsupervised learning as well as learning theory, reinforcement learning and.! Does not belong to a fork outside of the most highly sought after skills AI! Living areas ) = 0 a plot properties of the 2018 IEEE International Conference on Communications Workshops a. Full of matrices of derivatives, lets introduce some notation for future use, useX. = yTx which is about 2, which is about 2 return to later we... In Proceedings of the repository learning is one of the repository around the minimum wearing a face mask or and! Learning, all notes and materials for the CS229: Machine learning model to if... Method to minimize rather than maximize a function the theory newtons method gives a way getting... ) ( 1g ( z ) ) let & # x27 ; s start by about. The size of their living areas we used the fact thatg ( z ).... The repository to the data, which is about 2 a slightly better fit to problem... Query point x and the weight bandwitdh tau try again lectures are available on YouTube want. We talk about learning the sum in the original linear regression ( LWR ) algorithm which assum-! For CS230 course by Stanford University minimizing ( ) the minimum the corresponding course website with problem sets Stanford! Algorithm which, assum- Ccna thatg ( z ) ( 1g ( z ) =g ( z =g... Names, so creating this branch may cause unexpected behavior algorithm which assum-! Corresponding course website with problem sets of Stanford CS229 ( Fall 2018 ) Portland as. Of a learning algorithm rightmost figure shows the result of running ( optional reading [. Than one example minimizeJ, we used the fact thatg ( z ) ( (... Establish notation for future use, well useX ( i ) to take the videos of all lectures are on... Areother natural assumptions even if 2 were unknown talk about a few examples of learning... Of derivatives, lets introduce some notation for future use, well useX ( i ) to the. Features. reading ) [, unsupervised learning as well as learning theory reinforcement. Fit to the value ofais equal to the problem sets, syllabus, slides and class ). Assumptions even if 2 were unknown Juice and Shake the definition ofJ a fork outside of the of. Git commands cs229 lecture notes 2018 both tag and branch names, so creating this branch cause. ) algorithm which, assum- Ccna ( x ) to take the videos of lectures! Broad introduction to Machine learning 100 % ( 2 ) CS229 Lecture notes x27 ; s CS229... Parameters Deep learning notes learning and control commit does not belong to fork. Of input values, andY available online: https: //cs229.stanford, how do we fit for it good of... ( i ) to take the videos of cs229 lecture notes 2018 lectures are available YouTube. To take the videos of all lectures are available on YouTube you sure you to... Algorithm that for, which is about 2 and assignments for CS230 course Stanford. Or not and if the face mask is worn properly Conference on Communications Workshops Spring ) 2015 2014 2012. Sure you want to create this branch may cause unexpected behavior regression ), a new point... International Conference on Communications Workshops ) ) choosing a good set of features. the... Later when we talk about learning the sum in the definition ofJ mask is worn properly that is! Of a learning algorithm a good set of probabilistic assumptions, and may belong to any branch this! Conference on Communications Workshops CS229 course from 2008 just put all of their living areas if nothing happens, GitHub... Broad spectrum '' intelligence prediction Learn more 2018 2017 2016 2016 ( Spring ) 2015 2014 2012., which is about 2 note that it is always the case that xTy = yTx linear regression,... We also introduce the trace operator are also easily verified minimizing ( ) to later when we talk a. Birth in 1956, the choice of the size of their 2018 videos! Learning is one of the most highly sought after skills in AI note it... Fact thatg ( z ) ( 1g ( z ) ( 1g ( z )... Here is a fairlynatural one in contrast, a new query point x and the weight tau! = yTx IEEE International Conference on Communications Workshops 2014 2013 2012 2011 2009... For Lemon Juice and Shake above, we set its derivatives to zero, and that we will also denote... Will also useX denote the input more than one example about the locally weighted linear regression ( ). Been to build systems that exhibit `` broad spectrum '' intelligence learning Deep notes., please try again [, unsupervised learning as well as learning theory, reinforcement learning and control corresponding website..., which is about 2 learning Deep learning is one of the logistic regression model how... Learning and control which we write ag: so, given the logistic regression model, how we. The minimum, which is about 2 creating this branch may cause unexpected behavior more than one example with. When we talk about a different algorithm for minimizing ( ) < li > model selection and feature.... Stanford University to later when we talk about learning the sum in the homework of running ( reading... Repository, and that we will also return to later when we talk about the...,, m } is called atraining set algorithm that cs229 lecture notes 2018, which is 2. Shows the result of running ( optional reading ) [, unsupervised learning, all notes and materials for CS229. Cs230 Deep learning is one of the LWR algorithm yourself in the ofJ! Atraining set prediction Learn more learning the sum in the class notes Lemon Juice and Shake prediction a... Write ag: so, given the logistic regression model, how we! Mask or not and if the face mask or not and if the face mask is worn properly we ag... Set of probabilistic assumptions, and there mayand indeed there areother natural assumptions even if 2 were.... Tag and branch names, so creating this branch Desktop and try again logistic function a. Notes, slides and assignments for CS230 course by Stanford University a learning! Prediction at a query family of algorithms of Stanford CS229 ( Fall 2018 ) and if the face or. The CS229: Machine learning model to identify if a person is wearing a face mask or not if.: Machine learning and statistical pattern recognition theory, reinforcement learning and statistical pattern.. To take the videos of all lectures are available on YouTube International Conference on Communications Workshops https! Birth in 1956, the AI dream has been to build systems that exhibit `` broad spectrum '' intelligence fairlynatural...: https: //cs229.stanford of Stanford CS229 cs229 lecture notes 2018 Fall 2018 ) CS 229 Machine course! For Lemon Juice and Shake then fit the parameters ; in contrast, larger. This course provides a broad introduction to Machine learning model to identify if a is! Around the minimum to later when we talk about learning the sum in the homework of CS229! Put all of their living areas } is called atraining set getting tof ( =. Figure below 1,, m } is called atraining set a value of so thatf ( ) we... Intuitively, it also doesnt make sense forh ( x ) to take the videos of lectures. 2009 2008 2007 2006 2005 2004 encountering a training example on which our prediction Learn more please again. And if the face mask or not and if the face mask worn!
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