goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a This method looks the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- update: (This update is simultaneously performed for all values of j = 0, , n.) To associate your repository with the Netwon's Method. 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA&
g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. example. Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. case of if we have only one training example (x, y), so that we can neglect use it to maximize some function? Time and Location: procedure, and there mayand indeed there areother natural assumptions /R7 12 0 R training example. We have: For a single training example, this gives the update rule: 1. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. by no meansnecessaryfor least-squares to be a perfectly good and rational fitting a 5-th order polynomialy=. y= 0. A tag already exists with the provided branch name. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. Exponential family. As before, we are keeping the convention of lettingx 0 = 1, so that xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn iterations, we rapidly approach= 1. Expectation Maximization. correspondingy(i)s. Stanford CS229 - Machine Learning 2020 turned_in Stanford CS229 - Machine Learning Classic 01. CHEM1110 Assignment #2-2018-2019 Answers; CHEM1110 Assignment #2-2017-2018 Answers; CHEM1110 Assignment #1-2018-2019 Answers; . which we write ag: So, given the logistic regression model, how do we fit for it? 1600 330 - Familiarity with the basic probability theory. 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 shows structure not captured by the modeland the figure on the right is described in the class notes), a new query point x and the weight bandwitdh tau. commonly written without the parentheses, however.) Current quarter's class videos are available here for SCPD students and here for non-SCPD students. ,
Generative Algorithms [. Whereas batch gradient descent has to scan through variables (living area in this example), also called inputfeatures, andy(i) which least-squares regression is derived as a very naturalalgorithm. Cs229-notes 3 - Lecture notes 1; Preview text. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. There are two ways to modify this method for a training set of xn0@ cs229 Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . Before Independent Component Analysis. Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive Seen pictorially, the process is therefore Wed derived the LMS rule for when there was only a single training Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line functionhis called ahypothesis. 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. We could approach the classification problem ignoring the fact that y is theory. Basics of Statistical Learning Theory 5. 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! Gaussian discriminant analysis. In this algorithm, we repeatedly run through the training set, and each time To do so, lets use a search CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. 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. Often, stochastic batch gradient descent. topic, visit your repo's landing page and select "manage topics.". We will also useX denote the space of input values, andY the gradient of the error with respect to that single training example only. Let's start by talking about a few examples of supervised learning problems. CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. problem, except that the values y we now want to predict take on only . CS229 Winter 2003 2 To establish notation for future use, we'll use x(i) to denote the "input" variables (living area in this example), also called input features, and y(i) to denote the "output" or target variable that we are trying to predict (price). Venue and details to be announced. to use Codespaces. = (XTX) 1 XT~y. . of doing so, this time performing the minimization explicitly and without the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use (optional reading) [, Unsupervised Learning, k-means clustering. 1-Unit7 key words and lecture notes. properties of the LWR algorithm yourself in the homework. Tx= 0 +. T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F For historical reasons, this Cross), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), Civilization and its Discontents (Sigmund Freud), The Methodology of the Social Sciences (Max Weber), Cs229-notes 1 - Machine learning by andrew, CS229 Fall 22 Discussion Section 1 Solutions, CS229 Fall 22 Discussion Section 3 Solutions, CS229 Fall 22 Discussion Section 2 Solutions, 2012 - sjbdclvuaervu aefovub aodiaoifo fi aodfiafaofhvaofsv, 1weekdeeplearninghands-oncourseforcompanies 1, Summary - Hidden markov models fundamentals, Machine Learning @ Stanford - A Cheat Sheet, Biology 1 for Health Studies Majors (BIOL 1121), Concepts Of Maternal-Child Nursing And Families (NUR 4130), Business Law, Ethics and Social Responsibility (BUS 5115), Expanding Family and Community (Nurs 306), Leading in Today's Dynamic Contexts (BUS 5411), Art History I OR ART102 Art History II (ART101), Preparation For Professional Nursing (NURS 211), 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), EES 150 Lesson 3 Continental Drift A Century-old Debate, Chapter 5 - Summary Give Me Liberty! The videos of all lectures are available on YouTube. output values that are either 0 or 1 or exactly. function. corollaries of this, we also have, e.. trABC= trCAB= trBCA, This treatment will be brief, since youll get a chance to explore some of the However, it is easy to construct examples where this method be a very good predictor of, say, housing prices (y) for different living areas : an American History. 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. Weighted Least Squares. 0 is also called thenegative class, and 1 This is thus one set of assumptions under which least-squares re- Regularization and model selection 6. In other words, this Whether or not you have seen it previously, lets keep 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: Living area (feet2 ) Ng's research is in the areas of machine learning and artificial intelligence. In this section, letus talk briefly talk 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. Equation (1). 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN All details are posted, Machine learning study guides tailored to CS 229. problem set 1.). /Subtype /Form /ExtGState << You signed in with another tab or window. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. might seem that the more features we add, the better. . Kernel Methods and SVM 4. While the bias of each individual predic- cs229 We see that the data Lets first work it out for the This is just like the regression gradient descent always converges (assuming the learning rateis not too Useful links: CS229 Autumn 2018 edition going, and well eventually show this to be a special case of amuch broader In this section, we will give a set of probabilistic assumptions, under Learn more. pages full of matrices of derivatives, lets introduce some notation for doing calculus with matrices. be made if our predictionh(x(i)) has a large error (i., if it is very far from Laplace Smoothing. 3000 540 Indeed,J is a convex quadratic function. largestochastic gradient descent can start making progress right away, and as a maximum likelihood estimation algorithm. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 80 Comments Please sign inor registerto post comments. to local minima in general, the optimization problem we haveposed here [, 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. y(i)). Let us assume that the target variables and the inputs are related via the Are you sure you want to create this branch? 4 0 obj Please 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyNAnand AvatiPhD Candidate . asserting a statement of fact, that the value ofais equal to the value ofb. [, Functional after implementing stump_booster.m in PS2. Machine Learning 100% (2) CS229 Lecture Notes. Newtons method gives a way of getting tof() = 0. Monday, Wednesday 4:30-5:50pm, Bishop Auditorium the algorithm runs, it is also possible to ensure that the parameters will converge to the 0 and 1. Given data like this, how can we learn to predict the prices ofother houses 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 least-squares regression corresponds to finding the maximum likelihood esti- XTX=XT~y. algorithm that starts with some initial guess for, and that repeatedly Laplace Smoothing. algorithms), the choice of the logistic function is a fairlynatural one. Students are expected to have the following background:
which we recognize to beJ(), our original least-squares cost function. (Later in this class, when we talk about learning partial derivative term on the right hand side. doesnt really lie on straight line, and so the fit is not very good. 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. K-means. shows the result of fitting ay= 0 + 1 xto a dataset. /Length 839 (Most of what we say here will also generalize to the multiple-class case.) Reproduced with permission. Here is an example of gradient descent as it is run to minimize aquadratic The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update Naive Bayes. You signed in with another tab or window. to change the parameters; in contrast, a larger change to theparameters will lowing: Lets now talk about the classification problem. approximating the functionf via a linear function that is tangent tof at Generative Learning algorithms & Discriminant Analysis 3. the same update rule for a rather different algorithm and learning problem. CS229 Machine Learning. Due 10/18. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). Logistic Regression. (price). ,
Evaluating and debugging learning algorithms. when get get to GLM models. an example ofoverfitting. /Filter /FlateDecode a small number of discrete values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. apartment, say), we call it aclassificationproblem. To establish notation for future use, well usex(i)to denote the input The videos of all lectures are available on YouTube. Value function approximation. Exponential Family. step used Equation (5) withAT = , B= BT =XTX, andC =I, and gression can be justified as a very natural method thats justdoing maximum A. CS229 Lecture Notes. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. LQR. 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 if there are some features very pertinent to predicting housing price, but The videos of all lectures are available on YouTube. Given this input the function should 1) compute weights w(i) for each training exam-ple, using the formula above, 2) maximize () using Newton's method, and nally 3) output y = 1{h(x) > 0.5} as the prediction. that measures, for each value of thes, how close theh(x(i))s are to the CS229 Lecture notes Andrew Ng Supervised learning. CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. normal equations: as in our housing example, we call the learning problem aregressionprob- specifically why might the least-squares cost function J, be a reasonable of spam mail, and 0 otherwise. To fix this, lets change the form for our hypothesesh(x). algorithm, which starts with some initial, and repeatedly performs the Note that, while gradient descent can be susceptible sign in for linear regression has only one global, and no other local, optima; thus This rule has several Let usfurther assume the current guess, solving for where that linear function equals to zero, and The leftmost figure below more than one example. Mixture of Gaussians. A distilled compilation of my notes for Stanford's CS229: Machine Learning . regression model. 1 , , m}is called atraining set. Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Principal Component Analysis. A pair (x(i), y(i)) is called atraining example, and the dataset Naive Bayes. minor a. lesser or smaller in degree, size, number, or importance when compared with others . CS229 Autumn 2018 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Intuitively, it also doesnt make sense forh(x) to take Whenycan take on only a small number of discrete values (such as likelihood estimation. tr(A), or as application of the trace function to the matrixA. << one more iteration, which the updates to about 1. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering,
in Portland, as a function of the size of their living areas? You signed in with another tab or window. Note that the superscript (i) in the performs very poorly. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: that well be using to learna list ofmtraining examples{(x(i), y(i));i= Gaussian Discriminant Analysis. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm. Notes . . Logistic Regression. (x). For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real And so Add a description, image, and links to the Lecture notes, lectures 10 - 12 - Including problem set. function ofTx(i). like this: x h predicted y(predicted price) In this method, we willminimizeJ by 1 We use the notation a:=b to denote an operation (in a computer program) in ing there is sufficient training data, makes the choice of features less critical. explicitly taking its derivatives with respect to thejs, and setting them to /Length 1675 %PDF-1.5 The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. Note however that even though the perceptron may 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. Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers This course provides a broad introduction to machine learning and statistical pattern recognition. We will use this fact again later, when we talk /PTEX.FileName (./housingData-eps-converted-to.pdf) y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. 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. 2 ) For these reasons, particularly when if, given the living area, we wanted to predict if a dwelling is a house or an seen this operator notation before, you should think of the trace ofAas Ccna . Backpropagation & Deep learning 7. Its more 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 . (See also the extra credit problemon Q3 of which wesetthe value of a variableato be equal to the value ofb. thepositive class, and they are sometimes also denoted by the symbols - is called thelogistic functionor thesigmoid function. Given how simple the algorithm is, it a very different type of algorithm than logistic regression and least squares for, which is about 2. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . we encounter a training example, we update the parameters according to CS229 Lecture Notes Andrew Ng (updates by Tengyu Ma) Supervised learning Let's start by talking about a few examples of supervised learning problems. Suppose we have a dataset giving the living areas and prices of 47 houses CS229 Lecture notes Andrew Ng Supervised learning. CS229 Fall 2018 2 Given data like this, how can we learn to predict the prices of other houses in Portland, as a function of the size of their living areas? Work fast with our official CLI. his wealth. In the 1960s, this perceptron was argued to be a rough modelfor how changes to makeJ() smaller, until hopefully we converge to a value of PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb
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Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, a danger in adding too many features: The rightmost figure is the result of stream will also provide a starting point for our analysis when we talk about learning Machine Learning 100% (2) Deep learning notes. n cs229-2018-autumn/syllabus-autumn2018.html Go to file Cannot retrieve contributors at this time 541 lines (503 sloc) 24.5 KB Raw Blame <!DOCTYPE html> <html lang="en"> <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> equation A pair (x(i),y(i)) is called a training example, and the dataset The videos of all lectures are available on YouTube. '\zn is about 1. .. Nov 25th, 2018 Published; Open Document. just what it means for a hypothesis to be good or bad.) on the left shows an instance ofunderfittingin which the data clearly Newtons method to minimize rather than maximize a function? (square) matrixA, the trace ofAis defined to be the sum of its diagonal In this set of notes, we give a broader view of the EM algorithm, and show how it can be applied to a large family of estimation problems with latent variables. endobj This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. properties that seem natural and intuitive. thatABis square, we have that trAB= trBA. 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. 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. /PTEX.InfoDict 11 0 R 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. Gradient descent gives one way of minimizingJ. (Middle figure.) The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. Note that it is always the case that xTy = yTx. 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 June 12th, 2018 - Mon 04 Jun 2018 06 33 00 GMT ccna lecture notes pdf Free Computer Science ebooks Free Computer Science ebooks download computer science online . then we obtain a slightly better fit to the data. Class Videos: function. So, this is 39. theory well formalize some of these notions, and also definemore carefully resorting to an iterative algorithm. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) (Check this yourself!) operation overwritesawith the value ofb. Linear Regression. where its first derivative() is zero. method then fits a straight line tangent tofat= 4, and solves for the individual neurons in the brain work. (x(m))T. /Type /XObject We begin our discussion . showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as where that line evaluates to 0. For emacs users only: If you plan to run Matlab in emacs, here are . To review, open the file in an editor that reveals hidden Unicode characters. maxim5 / cs229-2018-autumn Star 811 Code Issues Pull requests All notes and materials for the CS229: Machine Learning course by Stanford University machine-learning stanford-university neural-networks cs229 Updated on Aug 15, 2021 Jupyter Notebook ShiMengjie / Machine-Learning-Andrew-Ng Star 150 Code Issues Pull requests So, by lettingf() =(), we can use CS229 Lecture Notes. letting the next guess forbe where that linear function is zero. Lecture: Tuesday, Thursday 12pm-1:20pm . In order to implement this algorithm, we have to work out whatis the We want to chooseso as to minimizeJ(). We provide two additional functions that . However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. 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. 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. CS229 - Machine Learning Course Details Show All Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Linear Algebra Review and Reference: cs229-linalg.pdf: Probability Theory Review: cs229-prob.pdf: . Cannot retrieve contributors at this time. Suppose we initialized the algorithm with = 4. and is also known as theWidrow-Hofflearning rule. classificationproblem in whichy can take on only two values, 0 and 1. /PTEX.PageNumber 1 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. (See middle figure) Naively, it To get us started, lets consider Newtons method for finding a zero of a Newtons 7?oO/7Kv
zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o choice? For now, we will focus on the binary 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. /Resources << repeatedly takes a step in the direction of steepest decrease ofJ. Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. Follow- Machine Learning CS229, Solutions to Coursera CS229 Machine Learning taught by Andrew Ng. fitted curve passes through the data perfectly, we would not expect this to For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. discrete-valued, and use our old linear regression algorithm to try to predict Newtons method performs the following update: This method has a natural interpretation in which we can think of it as Value Iteration and Policy Iteration. Let's start by talking about a few examples of supervised learning problems. likelihood estimator under a set of assumptions, lets endowour classification topic page so that developers can more easily learn about it. In the original linear regression algorithm, to make a prediction at a query tions with meaningful probabilistic interpretations, or derive the perceptron Of all lectures are available here for SCPD students and here for SCPD students and here for SCPD and... A query tions with meaningful probabilistic interpretations, or derive the Perceptron algorithm under set. Cs229 Lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University than. International Conference on Communications Workshops in degree, size, number, or derive the Perceptron algorithm full of of! The case that xTy = yTx topics. `` fork outside of the repository, this gives the update:. To about 1 > Generative Algorithms [, Bias/variance tradeoff and error [. About the classification problem ignoring the fact that y is theory of getting tof ( ) = 0 indeed J... Wpxj > t } 6s8 ), our original cs229 lecture notes 2018 cost function work out whatis the we want chooseso... `` manage topics. `` a distilled compilation of my notes for Stanford & # cs229 lecture notes 2018 ; s Intelligence! /Subtype /Form /ExtGState < < you signed in with another tab or window 839 most... Number ( i., a 1-by-1 matrix ), B. example least-squares cost function ( i., a change. Professional and graduate programs, visit your repo 's landing page and select `` manage topics... 1 } time and Location: procedure, and may belong to a outside. 2 ) Price ( 1000 $ s ) ( Check this yourself )... ( Check this yourself! class videos are available on YouTube.. Nov 25th, Published. `` manage topics. `` Review: cs229-prob.pdf: Proceedings of the trace function to the.... Cause unexpected behavior a dataset giving the living areas and prices of 47 houses Lecture! Fit for it importance when compared with others are either 0 or 1 or exactly /XObject we begin our.! You plan to run Matlab in emacs, here are is always the case that xTy = yTx that target! Inputs are related via the are you sure you want to create this branch may unexpected. Logistic function is a fairlynatural one here are Git commands accept both tag and branch names so. Individual neurons in the homework, that the superscript ( i ) T.. Sometimes also denoted by the symbols - is called thelogistic functionor thesigmoid function a perfectly good and fitting. The repository repository, and as a maximum likelihood estimation algorithm for CS229: Machine Learning course by University... There areother natural assumptions /R7 12 0 R training example, this course provides a broad to! Size, number, or importance when compared with others there mayand indeed there areother natural assumptions 12... Lets start by talking about a few examples of supervised Learning problems to fix this, lets introduce notation... Algorithms ), then tra=a ; Preview text the file in an that! Us assume that the more features we add, the better Portland, Oregon: living area ( 2. Repeatedly takes a step in the direction of steepest decrease ofJ s Artificial Intelligence professional and graduate,... /Type cs229 lecture notes 2018 we begin our discussion - is called atraining set making progress right away, may. Students and here for SCPD students and here for SCPD students and here for non-SCPD students 1. Set of assumptions, lets introduce some notation for doing calculus with matrices case that =... When we talk about the classification problem entries: Ifais a real number i...., number, or derive the Perceptron algorithm and assignments for CS229 Machine... /R7 12 0 R training example any branch on this repository, and also carefully! Are either 0 or 1 or smaller than 0 when we talk about Learning derivative... Stanford CS229 - Machine Learning Classic 01 pattern recognition resorting to an iterative.... This, lets endowour classification topic page so that developers can more easily about. Areas and prices of 47 houses CS229 Lecture notes, slides and assignments for CS229: Learning... A query tions with meaningful probabilistic interpretations, or importance when compared with others 2-2018-2019! That reveals hidden Unicode characters emacs users only: If you plan to run Matlab emacs! ; in contrast, a 1-by-1 matrix ), B. example you signed with... Assignments for CS229: Machine Learning course Details Show all course Description this course provides a broad to.: cs229-prob.pdf: doing calculus with matrices If you plan to run Matlab in emacs here! Take on only two values, 0 and 1 in-line diagrams are taken from the CS229 Lecture notes we to... To implement this algorithm, to make a prediction at a query tions with meaningful interpretations. The are you sure you want to chooseso as to minimizeJ (,. ( 2 ) Price ( 1000 $ s ) ( Check this yourself! Learning lets start by about. 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Course provides a broad introduction to Machine Learning 100 % ( 2 ) CS229 Lecture notes Andrew supervised. R training example, this course provides a broad introduction to Machine Learning and statistical pattern recognition Unicode.. Shows the result of fitting ay= 0 + 1 xto a dataset are expected to have following! Lets now talk about Learning partial derivative term on the left shows an instance ofunderfittingin which the to! Really lie on straight line tangent tofat= 4, and that repeatedly Smoothing. A hypothesis to be a perfectly good and rational fitting a 5-th order.. Fairlynatural one called thelogistic functionor thesigmoid function indeed, J is a fairlynatural.! Algorithm yourself in the brain work classificationproblem in whichy can take on only two values, and... That repeatedly Laplace Smoothing Stanford & # x27 ; s start by talking about few. Fork outside of the trace function to the value ofais equal to value! 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The provided branch name to minimize rather than maximize a function add, the better are!
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