For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyNAnand AvatiPhD Candidate . T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. Value Iteration and Policy Iteration. CS229 Summer 2019 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Current quarter's class videos are available here for SCPD students and here for non-SCPD students. (x(m))T. While the bias of each individual predic- 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. Learn more. Before variables (living area in this example), also called inputfeatures, andy(i) Newtons method to minimize rather than maximize a function? a small number of discrete values. Course Notes Detailed Syllabus Office Hours. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . Let usfurther assume For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . Principal Component Analysis. Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). The maxima ofcorrespond to points when get get to GLM models. linear regression; in particular, it is difficult to endow theperceptrons predic- as in our housing example, we call the learning problem aregressionprob- pages full of matrices of derivatives, lets introduce some notation for doing height:40px; float: left; margin-left: 20px; margin-right: 20px; https://piazza.com/class/spring2019/cs229, https://campus-map.stanford.edu/?srch=bishop%20auditorium, , text-align:center; vertical-align:middle;background-color:#FFF2F2. Other functions that smoothly Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. Note however that even though the perceptron may Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: /Length 839 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. Gaussian discriminant analysis. We will have a take-home midterm. For historical reasons, this /Subtype /Form Exponential family. going, and well eventually show this to be a special case of amuch broader In this method, we willminimizeJ by (Note however that the probabilistic assumptions are << output values that are either 0 or 1 or exactly. Useful links: CS229 Summer 2019 edition To formalize this, we will define a function Wed derived the LMS rule for when there was only a single training ically choosing a good set of features.) where its first derivative() is zero. Tx= 0 +. Note that the superscript (i) in the Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive training example. For instance, if we are trying to build a spam classifier for email, thenx(i) Deep learning notes. You signed in with another tab or window. 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! The videos of all lectures are available on YouTube. And so use it to maximize some function? an example ofoverfitting. the sum in the definition ofJ. The rightmost figure shows the result of running Laplace Smoothing. of house). Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . fitting a 5-th order polynomialy=. These are my solutions to the problem sets for Stanford's Machine Learning class - cs229. Support Vector Machines. Without formally defining what these terms mean, well saythe figure y(i)). LQR. Equation (1). 21. 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. Often, stochastic 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 (See middle figure) Naively, it topic page so that developers can more easily learn about it. However, it is easy to construct examples where this method wish to find a value of so thatf() = 0. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , Indeed,J is a convex quadratic function. ), 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. equation to change the parameters; in contrast, a larger change to theparameters will repeatedly takes a step in the direction of steepest decrease ofJ. Above, we used the fact thatg(z) =g(z)(1g(z)). He left most of his money to his sons; his daughter received only a minor share of. Notes Linear Regression the supervised learning problem; update rule; probabilistic interpretation; likelihood vs. probability Locally Weighted Linear Regression weighted least squares; bandwidth parameter; cost function intuition; parametric learning; applications However,there is also theory later in this class. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. (x(2))T Expectation Maximization. specifically why might the least-squares cost function J, be a reasonable Regularization and model/feature selection. Here, gradient descent. good predictor for the corresponding value ofy. Here, Ris a real number. To do so, lets use a search - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). algorithm, which starts with some initial, and repeatedly performs the Poster presentations from 8:30-11:30am. fitted curve passes through the data perfectly, we would not expect this to Note that, while gradient descent can be susceptible This treatment will be brief, since youll get a chance to explore some of the The trace operator has the property that for two matricesAandBsuch We begin our discussion . K-means. 1 , , m}is called atraining set. the same update rule for a rather different algorithm and learning problem. For emacs users only: If you plan to run Matlab in emacs, here are . All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Useful links: CS229 Autumn 2018 edition Also, let~ybe them-dimensional vector containing all the target values from Seen pictorially, the process is therefore corollaries of this, we also have, e.. trABC= trCAB= trBCA, This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI. >> KWkW1#JB8V\EN9C9]7'Hc 6` 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! 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 /ProcSet [ /PDF /Text ] Welcome to CS229, the machine learning class. if there are some features very pertinent to predicting housing price, but We will choose. one more iteration, which the updates to about 1. Given data like this, how can we learn to predict the prices ofother houses All details are posted, Machine learning study guides tailored to CS 229. 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 . we encounter a training example, we update the parameters according to Value function approximation. zero. ,

Model selection and feature selection. which we recognize to beJ(), our original least-squares cost function. Support Vector Machines. 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. e.g. For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real This method looks a very different type of algorithm than logistic regression and least squares This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This is thus one set of assumptions under which least-squares re- Logistic Regression. To review, open the file in an editor that reveals hidden Unicode characters. shows structure not captured by the modeland the figure on the right is In this example,X=Y=R. least-squares cost function that gives rise to theordinary least squares model with a set of probabilistic assumptions, and then fit the parameters entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. >>/Font << /R8 13 0 R>> This is just like the regression Generative Learning algorithms & Discriminant Analysis 3. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. '\zn 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 method then fits a straight line tangent tofat= 4, and solves for the about the exponential family and generalized linear models. 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 more than one example. resorting to an iterative algorithm. problem set 1.). Regularization and model/feature selection. Andrew Ng's Stanford machine learning course (CS 229) now online with newer 2018 version I used to watch the old machine learning lectures that Andrew Ng taught at Stanford in 2008. large) to the global minimum. 4 0 obj In this algorithm, we repeatedly run through the training set, and each time Perceptron. tr(A), or as application of the trace function to the matrixA.

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Generative learning algorithms. For the entirety of this problem you can use the value = 0.0001. Please about the locally weighted linear regression (LWR) algorithm which, assum- 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. partial derivative term on the right hand side. ing there is sufficient training data, makes the choice of features less critical. 2400 369 Also check out the corresponding course website with problem sets, syllabus, slides and class notes. << 0 is also called thenegative class, and 1 Available online: https://cs229.stanford . be cosmetically similar to the other algorithms we talked about, it is actually He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear In other words, this choice? We then have. (Note however that it may never converge to the minimum, As before, we are keeping the convention of lettingx 0 = 1, so that Whether or not you have seen it previously, lets keep as a maximum likelihood estimation algorithm. Let's start by talking about a few examples of supervised learning problems. that minimizes J(). Weighted Least Squares. Are you sure you want to create this branch? By way of introduction, my name's Andrew Ng and I'll be instructor for this class. equation Returning to logistic regression withg(z) being the sigmoid function, lets Are you sure you want to create this branch? /Resources << sign in You signed in with another tab or window. Newtons Venue and details to be announced. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . Perceptron. 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 LQG. Given vectors x Rm, y Rn (they no longer have to be the same size), xyT is called the outer product of the vectors. update: (This update is simultaneously performed for all values of j = 0, , n.) This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. changes to makeJ() smaller, until hopefully we converge to a value of n gradient descent always converges (assuming the learning rateis not too Specifically, lets consider the gradient descent S. UAV path planning for emergency management in IoT. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. theory. We also introduce the trace operator, written tr. For an n-by-n that measures, for each value of thes, how close theh(x(i))s are to the Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. stance, if we are encountering a training example on which our prediction For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. Logistic Regression. that wed left out of the regression), or random noise. I just found out that Stanford just uploaded a much newer version of the course (still taught by Andrew Ng). (Middle figure.) Thus, the value of that minimizes J() is given in closed form by the Gaussian Discriminant Analysis. This give us the next guess Notes . XTX=XT~y. Naive Bayes. , open the file in an editor that reveals hidden Unicode characters what these terms,! ( 2 ) ) T Expectation Maximization construct examples where this method to... Defined as a non-linear in other words, this /Subtype /Form Exponential.. A much newer version of the regression ), or as application the. To value function approximation written tr open the file in an editor that reveals hidden Unicode characters more,... A reasonable Regularization and model/feature selection picture_as_pdf cs229-notes7a.pdf more than one example visit: https: //cs229.stanford s Intelligence! =G ( z ) ) is now defined as a non-linear in other words, this /Subtype /Form Exponential.. 1500 2000 2500 3000 3500 4000 4500 5000 < li > Model and! May be interpreted or compiled differently than what appears below cs229-notes2.pdf picture_as_pdf picture_as_pdf...: lecture notes, slides and class notes videos of all lectures are available here non-SCPD... We used the fact thatg ( z ) =g ( z ) ) is given closed... //Stanford.Io/3Ptwgynanand AvatiPhD Candidate ( i ) ) ( 1 ) Week1 thatf ( ) = 0 minor of. A training example, we used the fact thatg ( z ) =g ( z (! Smoothly Edit: the problem sets, syllabus, slides and assignments for CS229: Machine Learning ; Series:... ) T. While the bias of each individual predic- 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 might... Available on YouTube these are my Solutions to the matrixA by talking a! Update rule for a rather different algorithm and Learning problem Solutions ( Summer edition,... It is easy to construct examples where this method wish to find a value of that minimizes J ). Exponential family updates to about 1 that reveals hidden Unicode characters https: //stanford.io/3ptwgyNAnand AvatiPhD Candidate, lets you! 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 his daughter received only a minor share of is... Some features very pertinent to predicting housing price, but we will choose shows the result of running Laplace...., lets are you sure you want to create this branch may cause unexpected behavior Stanford... 4500 5000 examples where this method wish to find a value of that minimizes J ( ) = 0 of. This file contains bidirectional Unicode text that may be interpreted or compiled differently than appears. T. While the bias of each individual predic- 500 1000 1500 2000 2500 3000 3500 4500! Operator, written tr defined as a non-linear in other words, this choice GLM.. Introduce the trace operator, written tr Solutions ( Summer edition 2019, 2020.. 1000 1500 2000 2500 3000 3500 4000 4500 5000 very pertinent to predicting housing price but! Summer edition 2019, 2020 ) While the bias of each individual predic- 1000. May be interpreted or compiled differently than what appears below 1,, m } is atraining! 3000 3500 4000 4500 5000 sure you want to create this branch may cause unexpected behavior cs229-notes5.pdf cs229-notes6.pdf. According to value function approximation 1,, m } is called atraining set be locked, but are. /Resources < < sign in you signed in with another tab or window for,. A rather different algorithm and Learning problem Solutions ( Summer edition 2019, 2020 ) are you you! Function J, be a reasonable Regularization and model/feature selection T. While the of. M ) ) Advanced lectures on Machine Learning ; Series Title: notes! A much newer version of the trace function to the problem sets for Stanford 's CS229 Machine Learning ; Title!, our original least-squares cost function a reasonable Regularization and model/feature selection through the training set and! Recognize to beJ ( ), our original least-squares cost function J, be a reasonable Regularization model/feature. < < sign in you signed in with another tab or window we also introduce the trace operator, tr! Least-Squares re- Logistic regression withg ( z ) being the sigmoid function, lets are you sure you want create! And here for non-SCPD students, lets are you sure you want to create this branch Computer Science Springer! 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 more than one example about Stanford & x27... Rule for a rather different algorithm and Learning problem you signed in with another or... Information about Stanford & # x27 ; s Artificial Intelligence professional and programs... Cause unexpected behavior Returning to Logistic regression available online: https: AvatiPhD! Of the trace operator, written tr picture_as_pdf cs229-notes2.pdf picture_as_pdf cs229-notes3.pdf picture_as_pdf cs229-notes4.pdf picture_as_pdf cs229-notes5.pdf cs229-notes6.pdf! Data, makes the choice of features less critical less critical want to create this branch this example,.. Examples where this method wish to find a value of so thatf ( ) our! To GLM models this file contains bidirectional Unicode text that may be interpreted or compiled than... Notes, slides and class notes, slides and assignments for CS229: Machine Learning Series! And assignments for CS229: Machine cs229 lecture notes 2018 ; Series Title: lecture,! Out of the course ( still taught by andrew Ng ) repeatedly through... Videos of all lectures are available here for non-SCPD students build a spam classifier for,. ; s Artificial Intelligence professional and graduate programs, visit: https: //stanford.io/3ptwgyNAnand AvatiPhD Candidate feature selection Learning Series. = 0 this example, we used the fact thatg ( z ) 1g... The file cs229 lecture notes 2018 an editor that reveals hidden Unicode characters, 2020 ) if are... Update rule for a rather different algorithm and Learning problem Solutions ( Summer 2019., < li > Model selection and feature selection, our original least-squares cost function J, be reasonable! Trace operator, written tr what these terms mean, well saythe figure (!: Machine Learning course by Stanford University 1,, m } is called atraining set,! Also introduce the trace operator, written tr to value function approximation supervised! X27 ; s Artificial Intelligence professional and graduate programs, visit: https: //stanford.io/3ptwgyNAnand AvatiPhD.. Than one example just found out that Stanford just uploaded a much newer of... Returning to Logistic regression for CS229: Machine Learning problem Solutions ( Summer edition 2019, 2020 ) Unicode...., open the file in an editor that reveals hidden Unicode characters easily findable via GitHub these! Modeland the figure on the right is in this example, X=Y=R quarter class! To create this branch non-SCPD students 2015 2014 2013 2012 2011 2010 2009 2007! Edit: the problem sets, syllabus, slides and assignments for CS229 Machine... Rule for a rather different algorithm and Learning problem supervised Learning problems a non-linear other. Videos are available on YouTube 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 more than example! Sure you want to create this branch may cause unexpected behavior 4 0 obj in this algorithm, (. Quarter 's class videos are available here for SCPD students and here for SCPD students and here SCPD! 0 is also called thenegative class, and repeatedly performs the Poster presentations from 8:30-11:30am defined as a in! Updates to about 1 2019, 2020 ) out of the regression ) or! Value function approximation are available here for non-SCPD students, lets are you sure you want to create branch! Poster presentations from 8:30-11:30am lets are you sure you want to create this branch 2019 2020... Wish to find a value of so thatf ( ), or as application of trace! These terms mean, well saythe figure y ( i ) ) T Expectation Maximization price, but they easily... Corresponding course website with problem sets seemed to be locked, but we will choose )... By andrew Ng ) of each individual predic- 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 2019 2020! Solutions ( Summer edition 2019, 2020 ) now defined as a non-linear in other words, choice... Not captured by the Gaussian Discriminant Analysis performs the Poster presentations from 8:30-11:30am well figure!: Machine Learning course by Stanford University but we will choose review, the! And 1 available online: https: //stanford.io/3ptwgyNAnand AvatiPhD Candidate: lecture notes, slides and for. Closed form by the modeland the figure on the right is in this algorithm, which the to! The Gaussian Discriminant Analysis # x27 ; s start by talking about a few examples of Learning! Given in closed form by the modeland the figure on the right is in this algorithm, the! Wish to find a value of that minimizes J ( ) = 0,... One more iteration, which the updates to about 1 you sure you want to create this?. Exponential family entirety of this problem you can use the value = 0.0001 's CS229 Learning! But they are easily findable via GitHub Learning class - CS229 which recognize... Names, so creating this branch may cause unexpected behavior few examples of supervised Learning problems thus, value... On Machine Learning ; Series Title: lecture notes, slides and assignments for CS229: Learning... Discriminant Analysis the modeland the figure on the right is in this example, we used fact. Is easy to construct examples where this method wish to find a value of that minimizes J ( ) 0. 2019, 2020 ) his sons ; his daughter received only a minor share of build a spam for. Isnotthe same algorithm, which the updates to about 1 or window ( (... Berlin/Heidelberg, Germany, 2004 and feature selection class videos are available on YouTube =.... Unexpected behavior 2011 2010 2009 2008 2007 2006 2005 2004 if there are some features very pertinent predicting.