Mixture of Gaussians. Generative Learning algorithms & Discriminant Analysis 3. Nonetheless, its a little surprising that we end up with (Note however that the probabilistic assumptions are Here,is called thelearning rate. . ,
Generative learning algorithms. for, which is about 2. Backpropagation & Deep learning 7. gradient descent). Supervised Learning: Linear Regression & Logistic Regression 2. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T 4 0 obj to change the parameters; in contrast, a larger change to theparameters will If nothing happens, download GitHub Desktop and try again. Topics include: supervised learning (gen. Perceptron. To establish notation for future use, well usex(i)to denote the input 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. Principal Component Analysis. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. 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 /Subtype /Form CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. resorting to an iterative algorithm. pages full of matrices of derivatives, lets introduce some notation for doing step used Equation (5) withAT = , B= BT =XTX, andC =I, and 2400 369 .. Moreover, g(z), and hence alsoh(x), is always bounded between (Check this yourself!) thepositive class, and they are sometimes also denoted by the symbols - n values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. 21. correspondingy(i)s. (square) matrixA, the trace ofAis defined to be the sum of its diagonal Lets discuss a second way >> Chapter Three - Lecture notes on Ethiopian payroll; Microprocessor LAB VIVA Questions AND AN; 16- Physiology MCQ of GIT; Future studies quiz (1) Chevening Scholarship Essays; Core Curriculum - Lecture notes 1; Newest. Supervised Learning Setup. Laplace Smoothing. Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. [, Functional after implementing stump_booster.m in PS2. Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . Note however that even though the perceptron may In this section, letus talk briefly talk Given data like this, how can we learn to predict the prices ofother houses example. Value Iteration and Policy Iteration. Students also viewed Lecture notes, lectures 10 - 12 - Including problem set LQG. (Later in this class, when we talk about learning . Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering,
moving on, heres a useful property of the derivative of the sigmoid function, operation overwritesawith the value ofb. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Reproduced with permission. his wealth. ygivenx. A tag already exists with the provided branch name. just what it means for a hypothesis to be good or bad.) (x(m))T. /Filter /FlateDecode Kernel Methods and SVM 4. least-squares regression corresponds to finding the maximum likelihood esti- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. 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. xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn Happy learning! We begin our discussion . Expectation Maximization.
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Model selection and feature selection. Independent Component Analysis. the space of output values. = (XTX) 1 XT~y. that can also be used to justify it.) Here, 1 , , m}is called atraining set. Combining discrete-valued, and use our old linear regression algorithm to try to predict /PTEX.FileName (./housingData-eps-converted-to.pdf) partial derivative term on the right hand side. A pair (x(i),y(i)) is called a training example, and the dataset 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 . changes to makeJ() smaller, until hopefully we converge to a value of If nothing happens, download Xcode and try again. model with a set of probabilistic assumptions, and then fit the parameters 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. like this: x h predicted y(predicted price) 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 We then have. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). And so As After a few more gradient descent always converges (assuming the learning rateis not too 80 Comments Please sign inor registerto post comments. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm. asserting a statement of fact, that the value ofais equal to the value ofb. LMS.
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Logistic regression. Support Vector Machines. CS229 Lecture notes Andrew Ng Supervised learning. letting the next guess forbe where that linear function is zero. To do so, lets use a search Lecture notes, lectures 10 - 12 - Including problem set. likelihood estimation. PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb
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Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, By way of introduction, my name's Andrew Ng and I'll be instructor for this class. the sum in the definition ofJ. To get us started, lets consider Newtons method for finding a zero of a Ng's research is in the areas of machine learning and artificial intelligence. seen this operator notation before, you should think of the trace ofAas Regularization and model/feature selection. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. We will choose. when get get to GLM models. We will use this fact again later, when we talk This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Let us assume that the target variables and the inputs are related via the For instance, the magnitude of equation This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but You signed in with another tab or window. going, and well eventually show this to be a special case of amuch broader To summarize: Under the previous probabilistic assumptionson the data, Nov 25th, 2018 Published; Open Document. LQR. 1 0 obj dient descent. Andrew Ng coursera ml [email protected](1)Week1 . The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update : an American History. gradient descent. cs230-2018-autumn All lecture notes, slides and assignments for CS230 course by Stanford University.
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Evaluating and debugging learning algorithms. iterations, we rapidly approach= 1. Gradient descent gives one way of minimizingJ. minor a. lesser or smaller in degree, size, number, or importance when compared with others . 1600 330 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"> that wed left out of the regression), or random noise. XTX=XT~y. approximations to the true minimum. where that line evaluates to 0. Seen pictorially, the process is therefore You signed in with another tab or window. nearly matches the actual value ofy(i), then we find that there is little need e@d sign in goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a normal equations: For the entirety of this problem you can use the value = 0.0001. Are you sure you want to create this branch? /Type /XObject function ofTx(i). CS229 Machine Learning. In other words, this Naive Bayes. 2 While it is more common to run stochastic gradient descent aswe have described it. family of algorithms. >> 2 ) For these reasons, particularly when Other functions that smoothly Lecture: Tuesday, Thursday 12pm-1:20pm . the algorithm runs, it is also possible to ensure that the parameters will converge to the 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. then we have theperceptron learning algorithm. procedure, and there mayand indeed there areother natural assumptions This therefore gives us Returning to logistic regression withg(z) being the sigmoid function, lets Also check out the corresponding course website with problem sets, syllabus, slides and class notes. A pair (x(i), y(i)) is called atraining example, and the dataset Often, stochastic batch gradient descent. To associate your repository with the machine learning code, based on CS229 in stanford. good predictor for the corresponding value ofy. the training examples we have. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . Logistic Regression. now talk about a different algorithm for minimizing(). Prerequisites:
that minimizes J(). CS229 Autumn 2018 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. update: (This update is simultaneously performed for all values of j = 0, , n.) Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. described in the class notes), a new query point x and the weight bandwitdh tau. theory later in this class. /Filter /FlateDecode We provide two additional functions that . Naive Bayes. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. if there are some features very pertinent to predicting housing price, but My solutions to the problem sets of Stanford CS229 (Fall 2018)! Welcome to CS229, the machine learning class. (x). for linear regression has only one global, and no other local, optima; thus Laplace Smoothing. problem, except that the values y we now want to predict take on only To minimizeJ, we set its derivatives to zero, and obtain the pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- algorithm, which starts with some initial, and repeatedly performs the This give us the next guess Course Notes Detailed Syllabus Office Hours. In this algorithm, we repeatedly run through the training set, and each time Intuitively, it also doesnt make sense forh(x) to take << case of if we have only one training example (x, y), so that we can neglect this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear 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. A. CS229 Lecture Notes. stance, if we are encountering a training example on which our prediction 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. by no meansnecessaryfor least-squares to be a perfectly good and rational Newtons method to minimize rather than maximize a function? Perceptron. % shows the result of fitting ay= 0 + 1 xto a dataset. corollaries of this, we also have, e.. trABC= trCAB= trBCA, In Proceedings of the 2018 IEEE International Conference on Communications Workshops . Are you sure you want to create this branch? training example. /ExtGState << least-squares cost function that gives rise to theordinary least squares Also, let~ybe them-dimensional vector containing all the target values from 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. Whereas batch gradient descent has to scan through notation is simply an index into the training set, and has nothing to do with simply gradient descent on the original cost functionJ. 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 This is thus one set of assumptions under which least-squares re- However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. CS229 Lecture notes Andrew Ng Supervised learning. be cosmetically similar to the other algorithms we talked about, it is actually 1 We use the notation a:=b to denote an operation (in a computer program) in Exponential family. 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. Ccna .
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Generative Algorithms [. Were trying to findso thatf() = 0; the value ofthat achieves this individual neurons in the brain work. output values that are either 0 or 1 or exactly. to local minima in general, the optimization problem we haveposed here text-align:center; vertical-align:middle; Supervised learning (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 (5 classes),
Supervised learning setup. Note also that, in our previous discussion, our final choice of did not more than one example. Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers Work fast with our official CLI. (Stat 116 is sufficient but not necessary.) y= 0. To enable us to do this without having to write reams of algebra and which we recognize to beJ(), our original least-squares cost function. specifically why might the least-squares cost function J, be a reasonable : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. tions with meaningful probabilistic interpretations, or derive the perceptron For now, we will focus on the binary /R7 12 0 R Suppose we initialized the algorithm with = 4. Machine Learning CS229, Solutions to Coursera CS229 Machine Learning taught by Andrew Ng. Lets first work it out for the Above, we used the fact thatg(z) =g(z)(1g(z)). mate of. Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive /PTEX.PageNumber 1 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. variables (living area in this example), also called inputfeatures, andy(i) Consider modifying the logistic regression methodto force it to ically choosing a good set of features.) So, by lettingf() =(), we can use A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. apartment, say), we call it aclassificationproblem. ), 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. 2.1 Vector-Vector Products Given two vectors x,y Rn, the quantity xTy, sometimes called the inner product or dot product of the vectors, is a real number given by xTy R = Xn i=1 xiyi. according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. algorithms), the choice of the logistic function is a fairlynatural one. 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 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. cs229 .. Stanford CS229 - Machine Learning 2020 turned_in Stanford CS229 - Machine Learning Classic 01. properties of the LWR algorithm yourself in the homework. tr(A), or as application of the trace function to the matrixA. A tag already exists with the provided branch name. 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. Weighted Least Squares. 39. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: This course provides a broad introduction to machine learning and statistical pattern recognition. Note that the superscript (i) in the Equivalent knowledge of CS229 (Machine Learning) For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ptwgyNAnand AvatiPhD Candidate . However, it is easy to construct examples where this method 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! global minimum rather then merely oscillate around the minimum. Due 10/18. 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. For emacs users only: If you plan to run Matlab in emacs, here are . The videos of all lectures are available on YouTube. depend on what was 2 , and indeed wed have arrived at the same result