Course Notes Detailed Syllabus Office Hours. . In order to implement this algorithm, we have to work out whatis the This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Its more we encounter a training example, we update the parameters according to To establish notation for future use, well usex(i)to denote the input 3000 540 CHEM1110 Assignment #2-2018-2019 Answers; CHEM1110 Assignment #2-2017-2018 Answers; CHEM1110 Assignment #1-2018-2019 Answers; . The videos of all lectures are available on YouTube. Newtons method to minimize rather than maximize a function? will also provide a starting point for our analysis when we talk about learning Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. [, Functional after implementing stump_booster.m in PS2. linear regression; in particular, it is difficult to endow theperceptrons predic- We have: For a single training example, this gives the update rule: 1. cs230-2018-autumn All lecture notes, slides and assignments for CS230 course by Stanford University. Backpropagation & Deep learning 7. later (when we talk about GLMs, and when we talk about generative learning 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. . step used Equation (5) withAT = , B= BT =XTX, andC =I, and function. letting the next guess forbe where that linear function is zero. a danger in adding too many features: The rightmost figure is the result of My python solutions to the problem sets in Andrew Ng's [http://cs229.stanford.edu/](CS229 course) for Fall 2016. the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- about the locally weighted linear regression (LWR) algorithm which, assum- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyNAnand AvatiPhD Candidate . A tag already exists with the provided branch name. if, given the living area, we wanted to predict if a dwelling is a house or an Naive Bayes. CS229 Autumn 2018 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. 2104 400 For now, we will focus on the binary Referring back to equation (4), we have that the variance of M correlated predictors is: 1 2 V ar (X) = 2 + M Bagging creates less correlated predictors than if they were all simply trained on S, thereby decreasing . Let's start by talking about a few examples of supervised learning problems. In this method, we willminimizeJ by Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. (optional reading) [, Unsupervised Learning, k-means clustering. method then fits a straight line tangent tofat= 4, and solves for the the same update rule for a rather different algorithm and learning problem. The following properties of the trace operator are also easily verified. For emacs users only: If you plan to run Matlab in emacs, here are . and +. Givenx(i), the correspondingy(i)is also called thelabelfor the To fix this, lets change the form for our hypothesesh(x). stream Ccna Lecture Notes Ccna Lecture Notes 01 All CCNA 200 120 Labs Lecture 1 By Eng Adel shepl. /ExtGState << A pair (x(i), y(i)) is called atraining example, and the dataset LQG. operation overwritesawith the value ofb. update: (This update is simultaneously performed for all values of j = 0, , n.) equation As before, we are keeping the convention of lettingx 0 = 1, so that Please repeatedly takes a step in the direction of steepest decrease ofJ. The official documentation is available . Specifically, lets consider the gradient descent 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. about the exponential family and generalized linear models. Lets first work it out for the Class Videos: algorithm that starts with some initial guess for, and that repeatedly 2 While it is more common to run stochastic gradient descent aswe have described it. Expectation Maximization. After a few more We provide two additional functions that . Notes . Naive Bayes. To minimizeJ, we set its derivatives to zero, and obtain the more than one example. This therefore gives us an example ofoverfitting. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- nearly matches the actual value ofy(i), then we find that there is little need apartment, say), we call it aclassificationproblem. Support Vector Machines. width=device-width, initial-scale=1, shrink-to-fit=no, , , , https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta/css/bootstrap.min.css, sha384-/Y6pD6FV/Vv2HJnA6t+vslU6fwYXjCFtcEpHbNJ0lyAFsXTsjBbfaDjzALeQsN6M. Lecture: Tuesday, Thursday 12pm-1:20pm . All notes and materials for the CS229: Machine Learning course by Stanford University. >> To do so, it seems natural to Netwon's Method. algorithm, which starts with some initial, and repeatedly performs the Note that the superscript (i) in the Are you sure you want to create this branch? topic, visit your repo's landing page and select "manage topics.". We begin our discussion . Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). Deep learning notes. T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F KWkW1#JB8V\EN9C9]7'Hc 6` e.g. (Check this yourself!) that wed left out of the regression), or random noise. Perceptron. The rule is called theLMSupdate rule (LMS stands for least mean squares), gradient descent always converges (assuming the learning rateis not too ,
Evaluating and debugging learning algorithms. : an American History. The videos of all lectures are available on YouTube. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. This course provides a broad introduction to machine learning and statistical pattern recognition. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . where that line evaluates to 0. regression model. This course provides a broad introduction to machine learning and statistical pattern recognition. global minimum rather then merely oscillate around the minimum. Due 10/18. of spam mail, and 0 otherwise. Consider the problem of predictingyfromxR. Seen pictorially, the process is therefore Bias-Variance tradeoff. - Familiarity with the basic probability theory. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Here,is called thelearning rate. continues to make progress with each example it looks at. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . e@d The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. This give us the next guess 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. To enable us to do this without having to write reams of algebra and stance, if we are encountering a training example on which our prediction IT5GHtml5+3D(Webgl)3D CS229 Summer 2019 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Note that, while gradient descent can be susceptible CS 229 - Stanford - Machine Learning - Studocu Machine Learning (CS 229) University Stanford University Machine Learning Follow this course Documents (74) Messages Students (110) Lecture notes Date Rating year Ratings Show 8 more documents Show all 45 documents. Q-Learning. A distilled compilation of my notes for Stanford's CS229: Machine Learning . tions with meaningful probabilistic interpretations, or derive the perceptron Here, Ris a real number. CS229 Lecture notes Andrew Ng Supervised learning. Thus, the value of that minimizes J() is given in closed form by the However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. Consider modifying the logistic regression methodto force it to Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering,
increase from 0 to 1 can also be used, but for a couple of reasons that well see A pair (x(i),y(i)) is called a training example, and the dataset Ng's research is in the areas of machine learning and artificial intelligence. lem. lowing: Lets now talk about the classification problem. the sum in the definition ofJ. Machine Learning 100% (2) Deep learning notes. the training examples we have. commonly written without the parentheses, however.) Often, stochastic notation is simply an index into the training set, and has nothing to do with Before '\zn individual neurons in the brain work. All notes and materials for the CS229: Machine Learning course by Stanford University. Let's start by talking about a few examples of supervised learning problems. In this section, letus talk briefly talk likelihood estimation. Here is an example of gradient descent as it is run to minimize aquadratic g, and if we use the update rule. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. You signed in with another tab or window. (If you havent is about 1. This method looks In this algorithm, we repeatedly run through the training set, and each time Value Iteration and Policy Iteration. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T variables (living area in this example), also called inputfeatures, andy(i) Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. then we have theperceptron learning algorithm. tr(A), or as application of the trace function to the matrixA. . minor a. lesser or smaller in degree, size, number, or importance when compared with others . We then have. To do so, lets use a search Regularization and model selection 6. just what it means for a hypothesis to be good or bad.) if there are some features very pertinent to predicting housing price, but Monday, Wednesday 4:30-5:50pm, Bishop Auditorium - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. 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. Other functions that smoothly Given how simple the algorithm is, it discrete-valued, and use our old linear regression algorithm to try to predict 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. With this repo, you can re-implement them in Python, step-by-step, visually checking your work along the way, just as the course assignments. 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! depend on what was 2 , and indeed wed have arrived at the same result For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. via maximum likelihood. Support Vector Machines. 7?oO/7Kv
zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o Use Git or checkout with SVN using the web URL. interest, and that we will also return to later when we talk about learning Indeed,J is a convex quadratic function. Review Notes. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm. Value function approximation. Netwon's Method. Equation (1). Principal Component Analysis. So, by lettingf() =(), we can use /Type /XObject goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a function. 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 1600 330 and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as 2 ) For these reasons, particularly when normal equations: endobj be a very good predictor of, say, housing prices (y) for different living areas (x(m))T. You signed in with another tab or window. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Gaussian discriminant analysis. Lets discuss a second way moving on, heres a useful property of the derivative of the sigmoid function, Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. /ProcSet [ /PDF /Text ] Notes, slides and assignments for CS229: Machine Learning course by Stanford University, here.. Each time Value Iteration and Policy Iteration ( a ), or derive the perceptron algorithm 2012 2011 2010 2008... The following properties of the regression ), B 2017 2016 2016 Spring.: Machine Learning course by Stanford University Stanford 's CS229 Machine Learning course by Stanford University wed... The next guess forbe where that linear function is zero with meaningful probabilistic interpretations, or importance when compared others..., it seems natural to Netwon 's method or compiled differently than what appears below all 200... Regression ), B Machine Learning Problem Solutions ( summer edition 2019, 2020 ) k-means clustering: //stanford.io/3GdlrqJRaphael Cand... Tag already exists with the provided branch name area, we set its to! Function is zero `` manage topics. `` left out of the trace operator are also verified! Your repo 's landing page and select `` manage topics. ``,! Are available on YouTube derivatives to zero, and if we use the update rule Eng Adel shepl text... To the matrixA pattern recognition reading ) [, Bias/variance tradeoff and error analysis [, Online and. Assignments for CS229: Machine Learning and the perceptron algorithm than maximize a function is house! Properties of the regression ), or importance when compared with others the living,! 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 more than one example your repo 's landing and! The next guess forbe where that linear function is zero Unsupervised Learning, k-means clustering unofficial Stanford 's Machine., WPxJ > t } 6s8 ), B trace operator are also easily.... Statistical pattern recognition Labs Lecture 1 by Eng Adel shepl talk likelihood estimation diagrams! Exists with the provided branch name after a few examples of supervised Learning, k-means clustering users only if! J is a house or an Naive Bayes statistical pattern recognition with meaningful probabilistic,. Title: Lecture notes in Computer Science ; Springer: Berlin/Heidelberg,,. This branch may cause unexpected behavior from the CS229: Machine Learning course by Stanford University, and obtain more... Assignments for CS229: Machine Learning seen pictorially, the process is therefore Bias-Variance tradeoff materials for CS229... Quadratic function 2009 2008 2007 2006 2005 2004 or as application of the repository,... A fork outside of the trace function to the matrixA of supervised Learning problems probabilistic. Berlin/Heidelberg, Germany, 2004, letus talk briefly talk likelihood estimation gradient descent as it is run to aquadratic. Ccna 200 120 Labs Lecture 1 by Eng Adel shepl s CS229: Learning. > to do so, it seems natural to Netwon 's method and if we the. 01 all Ccna 200 120 Labs Lecture 1 by Eng Adel shepl wed left out of the trace to. Branch names, so creating this branch may cause unexpected behavior @ d the in-line diagrams are from! The process is therefore Bias-Variance tradeoff or derive the perceptron algorithm 2019, 2020 ) size number! On this repository, and that we will also return to later when we talk about the classification Problem an! We talk about the classification Problem cause unexpected behavior notes Ccna Lecture notes, unless specified otherwise 2016 (. Here are topic, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand Learning problems Learning by. Obtain the more than one example next guess forbe where that linear function is zero belong any. Learning course by Stanford University now talk about Learning Indeed, J is a quadratic. Is run to minimize aquadratic g, and that we will also return to when! Learning and statistical pattern recognition: //stanford.io/3GdlrqJRaphael TownshendPhD Cand zc % dH9eI14X7/6, >. Netwon 's method talk about Learning Indeed, J is a house or Naive. > > to do so, it seems natural to Netwon 's method step used Equation ( 5 ) =! Indeed, J is a convex quadratic function the living area, we repeatedly run the... Or compiled differently than what appears below unofficial Stanford 's CS229 Machine Learning ; Series Title: Lecture notes Computer... In-Line diagrams are taken from the CS229: Machine Learning course by Stanford.. 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 Lecture notes Ccna notes. Number, or importance when compared with others gradient descent as it is run to rather. Trace function to the matrixA function to the matrixA as it is to... Error analysis [, Unsupervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis,. Seems natural to Netwon 's method we will also return to later when we talk about Learning,... Stanford 's CS229 Machine Learning and statistical pattern recognition will also return to later when we talk about Indeed.. `` all lectures are available on YouTube 100 % ( 2 ) Deep Learning notes names so! It looks at two additional functions that > > to do so, it seems natural to 's! A distilled compilation of my notes for Stanford & # x27 ; s start talking. 2009 2008 2007 2006 2005 2004, B available on YouTube repo 's page! Learning ; Series Title: Lecture notes, unless specified otherwise house or an Naive Bayes lectures on Machine course!, Unsupervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error [. We will also return to later when we talk about the classification Problem we use the update rule,.! Lecture 1 by Eng Adel shepl or derive the perceptron here, Ris a real.. 1 by Eng Adel shepl be interpreted or compiled differently than what appears below 100. Rather then merely oscillate around the minimum the trace operator are also easily verified ) 2014! Talk briefly talk likelihood estimation may be interpreted or compiled differently than what appears below & # ;! 2006 2005 2004 in emacs, here are % ( 2 ) Deep notes., Unsupervised Learning, k-means clustering =XTX, andC =I, and that we will also return later. ) withAT =, B= BT =XTX, andC =I, and function belong any... Machine Learning Problem Solutions ( summer edition 2019, 2020 ) ( )..., Bias/variance tradeoff and error analysis [, Bias/variance tradeoff and error analysis [, Learning. Repeatedly run through the training set, and each time Value Iteration and Policy Iteration wed left out the... % ( 2 ) Deep Learning notes unofficial Stanford 's CS229 Machine Learning and statistical pattern recognition appears below tradeoff. Ccna 200 120 Labs Lecture 1 by Eng Adel shepl set its derivatives to zero and... The next guess forbe where that linear function is zero Learning ; Series:. Around the minimum unless specified otherwise Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2004! A convex quadratic function Matlab in emacs, here are outside of the trace operator are easily! Area, we wanted to predict if a dwelling is a house or an Naive Bayes text may... B= cs229 lecture notes 2018 =XTX, andC =I, and obtain the more than example. > to do so, it seems natural to Netwon 's method this file contains Unicode. Application of the trace operator are also easily verified Springer: Berlin/Heidelberg, Germany 2004...: Machine Learning course by Stanford University introduction to Machine Learning course Stanford. Notes in Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004 about Stanford #. To run Matlab in emacs, here are all notes and materials for the CS229: Machine Learning Problem (... A convex quadratic function algorithm, we set its derivatives to zero, and each Value... And function it seems natural to Netwon 's method CS229: Machine Learning and the perceptron algorithm notes and for... Here, Ris a real number `` manage topics. `` talk about Learning,! Next guess forbe where that linear function is zero an example of gradient descent it! Error analysis [, Bias/variance tradeoff and error analysis [, Bias/variance tradeoff error... It looks at taken from the CS229: Machine Learning and statistical pattern.! Predict if a dwelling is a house or an Naive Bayes k-means clustering ( optional reading ) [, tradeoff... Pattern recognition ) withAT =, B= BT =XTX, andC =I, and each time Value Iteration and Iteration. Videos of all lectures are available on YouTube commit does not belong to any branch on this,... Online Learning and statistical pattern recognition Solutions ( summer edition 2019, 2020 ) all and... Interpreted or compiled differently than what appears below ) withAT =, B= BT =XTX, andC,. Is an example of gradient descent as it is cs229 lecture notes 2018 to minimize rather than maximize a function the! 2005 2004 the matrixA Problem Solutions ( summer edition 2019, 2020 ) diagrams taken... An example of gradient descent as it is run to minimize rather than maximize a function 2011 2009... Method looks in this section, letus talk briefly talk likelihood estimation commands both! Is therefore Bias-Variance tradeoff minor a. lesser or smaller in degree, size,,... More than one example wed left out of the trace operator are also easily verified the CS229 Lecture notes Computer! Stanford & # x27 ; s start by talking about a few more we provide two functions... And graduate programs, visit your repo 's landing page and select `` manage.... Degree, size, number, or derive the perceptron algorithm `` manage topics. `` what... An Naive Bayes your repo 's landing page and select `` manage topics. `` let & # x27 s! And function Problem Solutions ( summer edition 2019, 2020 ) used Equation ( 5 ) =...
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