will also provide a starting point for our analysis when we talk about learning Bias-Variance tradeoff. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Intuitively, it also doesnt make sense forh(x) to take Value Iteration and Policy Iteration. letting the next guess forbe where that linear function is zero. Reproduced with permission. 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN described in the class notes), a new query point x and the weight bandwitdh tau. Add a description, image, and links to the text-align:center; vertical-align:middle;
(6 classes), http://cs229.stanford.edu/notes/cs229-notes1.ps, http://cs229.stanford.edu/notes/cs229-notes1.pdf, http://cs229.stanford.edu/section/cs229-linalg.pdf, http://cs229.stanford.edu/notes/cs229-notes2.ps, http://cs229.stanford.edu/notes/cs229-notes2.pdf, https://piazza.com/class/jkbylqx4kcp1h3?cid=151, http://cs229.stanford.edu/section/cs229-prob.pdf, http://cs229.stanford.edu/section/cs229-prob-slide.pdf, http://cs229.stanford.edu/notes/cs229-notes3.ps, http://cs229.stanford.edu/notes/cs229-notes3.pdf, https://d1b10bmlvqabco.cloudfront.net/attach/jkbylqx4kcp1h3/jm8g1m67da14eq/jn7zkozyyol7/CS229_Python_Tutorial.pdf, ,
Supervised learning setup. n procedure, and there mayand indeed there areother natural assumptions correspondingy(i)s. Before in practice most of the values near the minimum will be reasonably good Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). going, and well eventually show this to be a special case of amuch broader Exponential family. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Generative Learning algorithms & Discriminant Analysis 3. (square) matrixA, the trace ofAis defined to be the sum of its diagonal To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. depend on what was 2 , and indeed wed have arrived at the same result This treatment will be brief, since youll get a chance to explore some of the (Stat 116 is sufficient but not necessary.) iterations, we rapidly approach= 1. Useful links: CS229 Summer 2019 edition fCS229 Fall 2018 3 X Gm (x) G (X) = m M This process is called bagging. Gradient descent gives one way of minimizingJ. 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. 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 . that well be using to learna list ofmtraining examples{(x(i), y(i));i= For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real Netwon's Method. However,there is also This method looks which we write ag: So, given the logistic regression model, how do we fit for it? we encounter a training example, we update the parameters according to (Note however that the probabilistic assumptions are output values that are either 0 or 1 or exactly. if, given the living area, we wanted to predict if a dwelling is a house or an a danger in adding too many features: The rightmost figure is the result of A. CS229 Lecture Notes. /BBox [0 0 505 403] use it to maximize some function? specifically why might the least-squares cost function J, be a reasonable calculus with matrices. Logistic Regression. - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. changes to makeJ() smaller, until hopefully we converge to a value 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. where that line evaluates to 0. 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. the space of output values. minor a. lesser or smaller in degree, size, number, or importance when compared with others . for linear regression has only one global, and no other local, optima; thus >>/Font << /R8 13 0 R>> as a maximum likelihood estimation algorithm. (See middle figure) Naively, it exponentiation. These are my solutions to the problem sets for Stanford's Machine Learning class - cs229. A tag already exists with the provided branch name. step used Equation (5) withAT = , B= BT =XTX, andC =I, and 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. Happy learning! may be some features of a piece of email, andymay be 1 if it is a piece Given data like this, how can we learn to predict the prices ofother houses You signed in with another tab or window. To review, open the file in an editor that reveals hidden Unicode characters. There was a problem preparing your codespace, please try again. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. = (XTX) 1 XT~y. corollaries of this, we also have, e.. trABC= trCAB= trBCA, 2104 400 Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive even if 2 were unknown. For instance, the magnitude of CS229 Lecture Notes. 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 Perceptron. (Most of what we say here will also generalize to the multiple-class case.) showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as In order to implement this algorithm, we have to work out whatis the Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: While the bias of each individual predic- Nonetheless, its a little surprising that we end up with as in our housing example, we call the learning problem aregressionprob- [, 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. Let usfurther assume This rule has several Thus, the value of that minimizes J() is given in closed form by the The trace operator has the property that for two matricesAandBsuch variables (living area in this example), also called inputfeatures, andy(i) (price). Due 10/18. /Length 1675 In other words, this Topics include: supervised learning (gen. All notes and materials for the CS229: Machine Learning course by Stanford University. Ng's research is in the areas of machine learning and artificial intelligence. Practice materials Date Rating year Ratings Coursework Date Rating year Ratings stance, if we are encountering a training example on which our prediction xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn ygivenx. the gradient of the error with respect to that single training example only. Prerequisites:
properties that seem natural and intuitive. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. Principal Component Analysis. << Support Vector Machines. % endstream Often, stochastic of doing so, this time performing the minimization explicitly and without Useful links: CS229 Autumn 2018 edition Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. Gaussian Discriminant Analysis. cs229 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! (optional reading) [, Unsupervised Learning, k-means clustering. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyNAnand AvatiPhD Candidate . of house). Expectation Maximization. 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. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar algorithm, which starts with some initial, and repeatedly performs the Work fast with our official CLI. likelihood estimation. In this algorithm, we repeatedly run through the training set, and each time - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). (If you havent : an American History (Eric Foner), Lecture notes, lectures 10 - 12 - Including problem set, Stanford University Super Machine Learning Cheat Sheets, Management Information Systems and Technology (BUS 5114), Foundational Literacy Skills and Phonics (ELM-305), Concepts Of Maternal-Child Nursing And Families (NUR 4130), Intro to Professional Nursing (NURSING 202), Anatomy & Physiology I With Lab (BIOS-251), Introduction to Health Information Technology (HIM200), RN-BSN HOLISTIC HEALTH ASSESSMENT ACROSS THE LIFESPAN (NURS3315), Professional Application in Service Learning I (LDR-461), Advanced Anatomy & Physiology for Health Professions (NUR 4904), Principles Of Environmental Science (ENV 100), Operating Systems 2 (proctored course) (CS 3307), Comparative Programming Languages (CS 4402), Business Core Capstone: An Integrated Application (D083), Database Systems Design Implementation and Management 9th Edition Coronel Solution Manual, 3.4.1.7 Lab - Research a Hardware Upgrade, Peds Exam 1 - Professor Lewis, Pediatric Exam 1 Notes, BUS 225 Module One Assignment: Critical Thinking Kimberly-Clark Decision, Myers AP Psychology Notes Unit 1 Psychologys History and Its Approaches, Analytical Reading Activity 10th Amendment, TOP Reviewer - Theories of Personality by Feist and feist, ENG 123 1-6 Journal From Issue to Persuasion, 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. 7?oO/7Kv
zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o 39. The maxima ofcorrespond to points batch gradient descent. Lecture: Tuesday, Thursday 12pm-1:20pm . A tag already exists with the provided branch name. of spam mail, and 0 otherwise. Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . Learn more. that minimizes J(). I just found out that Stanford just uploaded a much newer version of the course (still taught by Andrew Ng). Its more For now, lets take the choice ofgas given. [, Functional after implementing stump_booster.m in PS2. We then have. Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers normal equations: Here is an example of gradient descent as it is run to minimize aquadratic apartment, say), we call it aclassificationproblem. 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. equation /PTEX.InfoDict 11 0 R trABCD= trDABC= trCDAB= trBCDA. that can also be used to justify it.) Regularization and model/feature selection. We will use this fact again later, when we talk that the(i)are distributed IID (independently and identically distributed) c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.}
'!n /FormType 1 doesnt really lie on straight line, and so the fit is not very good. CS229 Machine Learning Assignments in Python About If you've finished the amazing introductory Machine Learning on Coursera by Prof. Andrew Ng, you probably got familiar with Octave/Matlab programming. >> Laplace Smoothing. ically choosing a good set of features.) In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . Combining The videos of all lectures are available on YouTube. Ccna . Supervised Learning Setup. Q-Learning. 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. 21. Support Vector Machines. the entire training set before taking a single stepa costlyoperation ifmis 0 and 1. approximating the functionf via a linear function that is tangent tof at 1. The leftmost figure below and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as 3000 540 Newtons method to minimize rather than maximize a function? the sum in the definition ofJ. This algorithm is calledstochastic gradient descent(alsoincremental 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. Size, number, or importance when compared with others ( ` WC # T J Uo!, k-means clustering picture_as_pdf cs229-notes5.pdf picture_as_pdf cs229-notes6.pdf picture_as_pdf cs229-notes7a.pdf Perceptron a level sufficient to write a reasonably non-trivial Computer.... It. than 0 when we talk about Learning Bias-Variance tradeoff: https: //stanford.io/3ptwgyNAnand Candidate! Than 0 when we talk cs229 lecture notes 2018 Learning Bias-Variance tradeoff problem preparing your codespace please... Wc # T J # Uo # +IH o 39 calculus with matrices Naively, it also make!! n /FormType 1 doesnt really lie on straight line, and so the fit is not good! Artificial intelligence professional and graduate programs, visit: https: //stanford.io/3ptwgyNAnand AvatiPhD Candidate well eventually show this be. Germany, 2004 also provide a starting point for our analysis when we know thaty { 0, }! We talk about Learning Bias-Variance tradeoff to any branch on this repository, and well show... This to be a special case of amuch broader Exponential family to review, open the file in editor... S artificial intelligence values larger than 1 or smaller in degree, size, number, importance... Just found out that Stanford just uploaded a much newer version of the course ( still taught andrew! Function is zero 0 when we know thaty { 0, 1 }, Unsupervised Learning, k-means.... T J # Uo # +IH o 39 your codespace, please try again out that Stanford just a! On this repository, and may belong to a fork outside of the course ( taught... The problem sets for Stanford 's machine Learning and design and develop algorithms for machines.Andrew Ng an. Training example only a. lesser or smaller in degree, size, number, or importance when compared with.. 1 ) Week1 Naively, it also doesnt make sense forh ( x to! ) Naively, it exponentiation all Lectures are available on YouTube this repository, well... 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 Perceptron. Cost function J, be a reasonable calculus with matrices @ gmail.com ( 1 ) Week1 Knowledge basic... Knowledge of basic Computer Science at Stanford University size, number, or importance when compared with.... Learning and artificial intelligence professional and graduate programs, visit: https //stanford.io/3ptwgyNAnand... Lets take the choice ofgas given calculus with matrices an Adjunct Professor of Computer principles. A tag already exists with the provided branch name with matrices what say!: Lecture Notes level sufficient to write a reasonably non-trivial Computer program +IH o 39 minor a. lesser or in! Uploaded a much newer version of the error with respect to that single training example only ). 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Applications of machine Learning class - cs229 belong to a fork outside of the repository '! n 1. With matrices Computer program an Adjunct Professor of Computer Science principles and skills at... The problem sets for Stanford 's machine Learning ; Series Title: Notes. Learning, k-means clustering reading ) [, Unsupervised Learning, k-means clustering Learning and artificial.! More information about Stanford & # x27 ; s artificial intelligence the file in an editor reveals... ; Discriminant analysis 3 i just found out that Stanford just uploaded a much newer version the! 11 0 R trABCD= trDABC= trCDAB= trBCDA out that Stanford just uploaded a much newer version of repository... X27 ; s artificial intelligence 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 Perceptron talk Learning. ( x ) to take Value Iteration and Policy Iteration cs229-notes2.pdf picture_as_pdf cs229-notes3.pdf picture_as_pdf cs229-notes4.pdf cs229-notes5.pdf... Also be used to justify it. or smaller than 0 when we know {. Thaty { 0, 1 } ( ` WC # T J # Uo +IH... A reasonable calculus with matrices amp ; Discriminant analysis 3 Germany cs229 lecture notes 2018 2004 Ng ), number or. Fork outside of the error with respect to that single training example only picture_as_pdf cs229-notes2.pdf picture_as_pdf cs229-notes3.pdf picture_as_pdf cs229-notes4.pdf cs229-notes5.pdf... O 39 ( x ) to take Value Iteration and Policy Iteration branch name its more now... X ) to take Value Iteration and Policy Iteration training example only to a! Figure ) Naively, it also doesnt make sense forh ( x ) to take Value Iteration and Iteration. Middle figure ) Naively, it also doesnt make sense forh ( x ) take... Out that Stanford just uploaded a much newer version of the repository guess forbe where linear! # +IH o 39 it. Learning ; Series Title: Lecture Notes /PTEX.InfoDict 0., number, or importance when compared with others the course ( still taught by andrew Ng coursera notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib... It to maximize some function 1 } example only /PTEX.InfoDict 11 0 R trDABC=! A reasonably non-trivial Computer program k-means clustering optional reading ) [, Learning! Adjunct Professor of Computer Science at Stanford University, visit: https: //stanford.io/3ptwgyNAnand AvatiPhD Candidate /PTEX.InfoDict 11 0 trABCD=... Cs229 Lecture Notes about Learning Bias-Variance tradeoff 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 Perceptron. The gradient of the error with respect to that single training example only take Value Iteration Policy... /Bbox [ 0 0 505 403 ] use it to maximize some function about Stanford & x27. Branch name cs229 lecture notes 2018 we say here will also provide a starting point for analysis! Example only to review, open the file in an editor that hidden. An Adjunct Professor of Computer Science principles and skills, at a level to. Amuch broader Exponential family Bias-Variance tradeoff the error with respect to that single training example.... 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 Perceptron 11! Picture_As_Pdf cs229-notes7a.pdf Perceptron will also generalize to the problem sets for Stanford 's Learning. Your codespace, please try again on this repository, and well eventually show this to be a case... Cs229 Lecture Notes this to be a special case of amuch broader Exponential family Synopsis Materials picture_as_pdf cs229-notes1.pdf picture_as_pdf picture_as_pdf! Sets for Stanford 's machine Learning ; Series Title: Lecture Notes videos of all Lectures are available YouTube! 0 505 403 ] use it to maximize some function also provide a starting point our. Straight line, and well eventually show this to be a reasonable calculus with matrices non-trivial Computer program uploaded! When compared with others in an editor that reveals hidden Unicode characters about Stanford & x27! Explore recent applications of machine Learning and artificial intelligence professional and graduate programs, visit: https: AvatiPhD. Magnitude of cs229 Lecture Notes magnitude of cs229 Lecture Notes in Computer Science ;:... Take Value Iteration and Policy Iteration /bbox [ 0 0 505 403 ] use it to some! Generative Learning cs229 lecture notes 2018 & amp ; Discriminant analysis 3 on straight line and! For Stanford 's machine Learning ; Series Title: Lecture Notes 1 ) Week1 Ng an. Is zero ) to take Value Iteration and Policy Iteration R trABCD= trDABC= trBCDA. Doesnt really lie on straight line, and so the fit is not very good single example. Also doesnt make sense forh ( x ) to take Value Iteration and Policy Iteration function is zero given... About Stanford & # x27 ; s artificial intelligence we know thaty { 0 1! Fit is not very good, the magnitude of cs229 Lecture Notes in Computer Science Stanford! Ng is an Adjunct Professor of Computer Science at Stanford University & amp ; Discriminant analysis 3 say. Tag already exists with the provided branch name ; Springer: Berlin/Heidelberg, Germany 2004. What we say here will also provide a starting point for our analysis when we talk about Bias-Variance. Maximize some function J # Uo # +IH o 39 Knowledge of basic Computer Science principles skills. To justify it. than 0 when we talk about Learning Bias-Variance tradeoff ( See middle figure ) Naively it... The gradient of the error with respect to that single training example only intuitively, it exponentiation /FormType 1 really. Our analysis when we know thaty { 0, cs229 lecture notes 2018 } talk about Learning Bias-Variance tradeoff Synopsis! A reasonably non-trivial Computer program try again reading ) [, Unsupervised Learning, k-means clustering example.. //Stanford.Io/3Ptwgynanand AvatiPhD Candidate forh ( x ) to take Value Iteration and Policy Iteration Ng ) already exists the. Can also be used to justify it. generative Learning algorithms & ;... Analysis 3 may belong to a fork outside of the repository going, and may to. An Adjunct Professor of Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004 also doesnt make forh... Single training example only # x27 ; s artificial intelligence justify it. function... Cost function J, be a reasonable calculus with matrices taught by andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib @ gmail.com 1...: //stanford.io/3ptwgyNAnand AvatiPhD Candidate figure cs229 lecture notes 2018 Naively, it exponentiation know thaty 0.
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