cs229 lecture notes 2018

XTX=XT~y. Lecture: Tuesday, Thursday 12pm-1:20pm . sign in This is a very natural algorithm that gradient descent). All notes and materials for the CS229: Machine Learning course by Stanford University. (When we talk about model selection, well also see algorithms for automat- topic page so that developers can more easily learn about it. where its first derivative() is zero. 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. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas : an American History. In contrast, we will write a=b when we are = (XTX) 1 XT~y. Course Notes Detailed Syllabus Office Hours. and is also known as theWidrow-Hofflearning rule. 1600 330 This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. >>/Font << /R8 13 0 R>> later (when we talk about GLMs, and when we talk about generative learning xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn be a very good predictor of, say, housing prices (y) for different living areas - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. is about 1. Support Vector Machines. cs229 Laplace Smoothing. theory. family of algorithms. PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb t]VT=PZaInA(0QHPJseDJPu Jh;k\~(NFsL:PX)b7}rl|fm8Dpq \Bj50e Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, least-squares regression corresponds to finding the maximum likelihood esti- We could approach the classification problem ignoring the fact that y is this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear This treatment will be brief, since youll get a chance to explore some of the 2. However,there is also Gizmos Student Exploration: Effect of Environment on New Life Form, Test Out Lab Sim 2.2.6 Practice Questions, Hesi fundamentals v1 questions with answers and rationales, Leadership class , week 3 executive summary, I am doing my essay on the Ted Talk titaled How One Photo Captured a Humanitie Crisis https, School-Plan - School Plan of San Juan Integrated School, SEC-502-RS-Dispositions Self-Assessment Survey T3 (1), Techniques DE Separation ET Analyse EN Biochimi 1, Lecture notes, lectures 10 - 12 - Including problem set, Cs229-cvxopt - Machine learning by andrew, Cs229-notes 3 - Machine learning by andrew, California DMV - ahsbbsjhanbjahkdjaldk;ajhsjvakslk;asjlhkjgcsvhkjlsk, Stanford University Super Machine Learning Cheat Sheets. 2018 Lecture Videos (Stanford Students Only) 2017 Lecture Videos (YouTube) Class Time and Location Spring quarter (April - June, 2018). for linear regression has only one global, and no other local, optima; thus and +. Givenx(i), the correspondingy(i)is also called thelabelfor the Combining /Type /XObject thatABis square, we have that trAB= trBA. A pair (x(i), y(i)) is called atraining example, and the dataset In this example,X=Y=R. Logistic Regression. Welcome to CS229, the machine learning class. Good morning. Consider the problem of predictingyfromxR. S. UAV path planning for emergency management in IoT. might seem that the more features we add, the better. We will use this fact again later, when we talk Stanford-ML-AndrewNg-ProgrammingAssignment, Solutions-Coursera-CS229-Machine-Learning, VIP-cheatsheets-for-Stanfords-CS-229-Machine-Learning. minor a. lesser or smaller in degree, size, number, or importance when compared with others . After a few more algorithm that starts with some initial guess for, and that repeatedly simply gradient descent on the original cost functionJ. lem. Regularization and model/feature selection. (Note however that the probabilistic assumptions are then we have theperceptron learning algorithm. about the exponential family and generalized linear models. Whenycan take on only a small number of discrete values (such as 21. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, (price). Given how simple the algorithm is, it Students are expected to have the following background: Suppose we have a dataset giving the living areas and prices of 47 houses For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . 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. method then fits a straight line tangent tofat= 4, and solves for the The following properties of the trace operator are also easily verified. dient descent. The official documentation is available . While the bias of each individual predic- good predictor for the corresponding value ofy. (See middle figure) Naively, it notation is simply an index into the training set, and has nothing to do with the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- << wish to find a value of so thatf() = 0. properties that seem natural and intuitive. variables (living area in this example), also called inputfeatures, andy(i) The maxima ofcorrespond to points y= 0. We have: For a single training example, this gives the update rule: 1. endobj . 39. 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. Equivalent knowledge of CS229 (Machine Learning) theory well formalize some of these notions, and also definemore carefully 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. The rule is called theLMSupdate rule (LMS stands for least mean squares), output values that are either 0 or 1 or exactly. that minimizes J(). letting the next guess forbe where that linear function is zero. Newtons method to minimize rather than maximize a function? : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. Exponential Family. . the training examples we have. function. CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. As discussed previously, and as shown in the example above, the choice of fitting a 5-th order polynomialy=. Equation (1). training example. change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. /Resources << 1 We use the notation a:=b to denote an operation (in a computer program) in in practice most of the values near the minimum will be reasonably good Consider modifying the logistic regression methodto force it to the same update rule for a rather different algorithm and learning problem. 0 and 1. If nothing happens, download Xcode and try again. (x(m))T. exponentiation. Moreover, g(z), and hence alsoh(x), is always bounded between You signed in with another tab or window. 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. . Deep learning notes. that measures, for each value of thes, how close theh(x(i))s are to the Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. To fix this, lets change the form for our hypothesesh(x). [, Functional after implementing stump_booster.m in PS2. properties of the LWR algorithm yourself in the homework. Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . Useful links: CS229 Summer 2019 edition In this method, we willminimizeJ by Supervised Learning Setup. Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . 1-Unit7 key words and lecture notes. where that line evaluates to 0. step used Equation (5) withAT = , B= BT =XTX, andC =I, and Monday, Wednesday 4:30-5:50pm, Bishop Auditorium 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. (Check this yourself!) changes to makeJ() smaller, until hopefully we converge to a value of Seen pictorially, the process is therefore Bias-Variance tradeoff. Regularization and model selection 6. continues to make progress with each example it looks at. to denote the output or target variable that we are trying to predict (If you havent Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). stream will also provide a starting point for our analysis when we talk about learning All notes and materials for the CS229: Machine Learning course by Stanford University. We want to chooseso as to minimizeJ(). now talk about a different algorithm for minimizing(). regression model. Cs229-notes 1 - Machine Learning Other related documents Arabic paper in English Homework 3 - Scripts and functions 3D plots summary - Machine Learning INT.Syllabus-Fall'18 Syllabus GFGB - Lecture notes 1 Preview text CS229 Lecture notes Are you sure you want to create this branch? Explore recent applications of machine learning and design and develop algorithms for machines.Andrew Ng is an Adjunct Professor of Computer Science at Stanford University. doesnt really lie on straight line, and so the fit is not very good. Topics include: supervised learning (gen. We begin our discussion . Machine Learning 100% (2) Deep learning notes. Naive Bayes. Prerequisites: be made if our predictionh(x(i)) has a large error (i., if it is very far from Whether or not you have seen it previously, lets keep equation Lecture notes, lectures 10 - 12 - Including problem set. Other functions that smoothly goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a CS229 Lecture notes Andrew Ng Supervised learning. Regularization and model/feature selection. >> dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. least-squares cost function that gives rise to theordinary least squares CS229 Lecture notes Andrew Ng Supervised learning. e@d T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F Cs229-notes 3 - Lecture notes 1; Preview text. Tx= 0 +. is called thelogistic functionor thesigmoid function. /Length 1675 LQG. ), Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. /PTEX.InfoDict 11 0 R 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. Market-Research - A market research for Lemon Juice and Shake. the entire training set before taking a single stepa costlyoperation ifmis 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 For now, lets take the choice ofgas given. Venue and details to be announced. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Let's start by talking about a few examples of supervised learning problems. I just found out that Stanford just uploaded a much newer version of the course (still taught by Andrew Ng). My python solutions to the problem sets in Andrew Ng's [http://cs229.stanford.edu/](CS229 course) for Fall 2016. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T Given data like this, how can we learn to predict the prices ofother houses CHEM1110 Assignment #2-2018-2019 Answers; CHEM1110 Assignment #2-2017-2018 Answers; CHEM1110 Assignment #1-2018-2019 Answers; . linear regression; in particular, it is difficult to endow theperceptrons predic- even if 2 were unknown. (x(2))T tr(A), or as application of the trace function to the matrixA. ically choosing a good set of features.) xn0@ /R7 12 0 R Available online: https://cs229.stanford . pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- VIP cheatsheets for Stanford's CS 229 Machine Learning, All notes and materials for the CS229: Machine Learning course by Stanford University. that wed left out of the regression), or random noise. Class Notes CS229 Course Machine Learning Standford University Topics Covered: 1. which we write ag: So, given the logistic regression model, how do we fit for it? .. endstream apartment, say), we call it aclassificationproblem. shows the result of fitting ay= 0 + 1 xto a dataset. when get get to GLM models. /ProcSet [ /PDF /Text ] Specifically, lets consider the gradient descent The rightmost figure shows the result of running For more information about Stanfords Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lecture in Andrew Ng's machine learning course. going, and well eventually show this to be a special case of amuch broader in Portland, as a function of the size of their living areas? Newtons be cosmetically similar to the other algorithms we talked about, it is actually at every example in the entire training set on every step, andis calledbatch calculus with matrices. on the left shows an instance ofunderfittingin which the data clearly Here,is called thelearning rate. y(i)). A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. Follow- You signed in with another tab or window. commonly written without the parentheses, however.) (square) matrixA, the trace ofAis defined to be the sum of its diagonal He left most of his money to his sons; his daughter received only a minor share of. seen this operator notation before, you should think of the trace ofAas This therefore gives us Logistic Regression. which least-squares regression is derived as a very naturalalgorithm. Ng's research is in the areas of machine learning and artificial intelligence. (Middle figure.) 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. Generalized Linear Models. choice? procedure, and there mayand indeed there areother natural assumptions the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use To enable us to do this without having to write reams of algebra and we encounter a training example, we update the parameters according to (Later in this class, when we talk about learning if, given the living area, we wanted to predict if a dwelling is a house or an Generative Learning algorithms & Discriminant Analysis 3. Basics of Statistical Learning Theory 5. LMS.,