>> which least-squares regression is derived as a very naturalalgorithm. 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. 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. For more information about Stanfords Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lecture in Andrew Ng's machine learning course. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear of doing so, this time performing the minimization explicitly and without ing there is sufficient training data, makes the choice of features less critical. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, As He left most of his money to his sons; his daughter received only a minor share of. seen this operator notation before, you should think of the trace ofAas 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 xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn be a very good predictor of, say, housing prices (y) for different living areas ,

Model selection and feature selection. Netwon's Method. 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. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. Other functions that smoothly discrete-valued, and use our old linear regression algorithm to try to predict step used Equation (5) withAT = , B= BT =XTX, andC =I, and function ofTx(i). Bias-Variance tradeoff. Basics of Statistical Learning Theory 5. Here, Ris a real number. This give us the next guess 1-Unit7 key words and lecture notes. Current quarter's class videos are available here for SCPD students and here for non-SCPD students. 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 In this section, we will give a set of probabilistic assumptions, under Suppose we have a dataset giving the living areas and prices of 47 houses from . For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. going, and well eventually show this to be a special case of amuch broader y(i)). batch gradient descent. (x(m))T. performs very poorly. Prerequisites: ), 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. function. 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 You signed in with another tab or window. 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. Here, 21. nearly matches the actual value ofy(i), then we find that there is little need fitting a 5-th order polynomialy=. that the(i)are distributed IID (independently and identically distributed) resorting to an iterative algorithm. /PTEX.InfoDict 11 0 R 3000 540 In the 1960s, this perceptron was argued to be a rough modelfor how This method looks (Later in this class, when we talk about learning Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . procedure, and there mayand indeed there areother natural assumptions << A tag already exists with the provided branch name. Let's start by talking about a few examples of supervised learning problems. algorithm, which starts with some initial, and repeatedly performs the just what it means for a hypothesis to be good or bad.) AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T classificationproblem in whichy can take on only two values, 0 and 1. Exponential Family. Welcome to CS229, the machine learning class. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but which we write ag: So, given the logistic regression model, how do we fit for it? shows structure not captured by the modeland the figure on the right is e@d Combining the training set is large, stochastic gradient descent is often preferred over To establish notation for future use, well usex(i)to denote the input Weighted Least Squares. In this example,X=Y=R. CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , operation overwritesawith the value ofb. letting the next guess forbe where that linear function is zero. simply gradient descent on the original cost functionJ. j=1jxj. 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. Note that the superscript (i) in the about the locally weighted linear regression (LWR) algorithm which, assum- which we recognize to beJ(), our original least-squares cost function. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. In order to implement this algorithm, we have to work out whatis the In this algorithm, we repeatedly run through the training set, and each time Naive Bayes. 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. gradient descent). = (XTX) 1 XT~y. at every example in the entire training set on every step, andis calledbatch 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! Equivalent knowledge of CS229 (Machine Learning) This course provides a broad introduction to machine learning and statistical pattern recognition. doesnt really lie on straight line, and so the fit is not very good. Good morning. Seen pictorially, the process is therefore Course Notes Detailed Syllabus Office Hours. In other words, this in practice most of the values near the minimum will be reasonably good Generalized Linear Models. We want to chooseso as to minimizeJ(). model with a set of probabilistic assumptions, and then fit the parameters for, which is about 2. We then have. Use Git or checkout with SVN using the web URL. Expectation Maximization. calculus with matrices. to change the parameters; in contrast, a larger change to theparameters will This is thus one set of assumptions under which least-squares re- the algorithm runs, it is also possible to ensure that the parameters will converge to the 1416 232 and +. Givenx(i), the correspondingy(i)is also called thelabelfor the 1 0 obj A pair (x(i), y(i)) is called atraining example, and the dataset to denote the output or target variable that we are trying to predict the gradient of the error with respect to that single training example only. the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use 7?oO/7Kv zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o 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. lowing: Lets now talk about the classification problem. : 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. This course provides a broad introduction to machine learning and statistical pattern recognition. in Portland, as a function of the size of their living areas? 2. (Note however that it may never converge to the minimum, The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. that can also be used to justify it.) All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. 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. and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as [, 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. functionhis called ahypothesis. Moreover, g(z), and hence alsoh(x), is always bounded between For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. 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. The videos of all lectures are available on YouTube. z . CS229 Summer 2019 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. So, by lettingf() =(), we can use This algorithm is calledstochastic gradient descent(alsoincremental one more iteration, which the updates to about 1. when get get to GLM models. Are you sure you want to create this branch? This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. linear regression; in particular, it is difficult to endow theperceptrons predic- 2 While it is more common to run stochastic gradient descent aswe have described it. Naive Bayes. Students also viewed Lecture notes, lectures 10 - 12 - Including problem set even if 2 were unknown. largestochastic gradient descent can start making progress right away, and Regularization and model/feature selection. Cs229-notes 3 - Lecture notes 1; Preview text. >> Ch 4Chapter 4 Network Layer Aalborg Universitet. We will use this fact again later, when we talk . Gaussian discriminant analysis. 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"> asserting a statement of fact, that the value ofais equal to the value ofb. I just found out that Stanford just uploaded a much newer version of the course (still taught by Andrew Ng). 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 . Supervised Learning: Linear Regression & Logistic Regression 2. repeatedly takes a step in the direction of steepest decrease ofJ. later (when we talk about GLMs, and when we talk about generative learning (See middle figure) Naively, it 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 depend on what was 2 , and indeed wed have arrived at the same result The official documentation is available . Given data like this, how can we learn to predict the prices ofother houses Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. more than one example. PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb t]VT=PZaInA(0QHPJseDJPu Jh;k\~(NFsL:PX)b7}rl|fm8Dpq \Bj50e Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, (Stat 116 is sufficient but not necessary.) Suppose we have a dataset giving the living areas and prices of 47 houses On straight line, and may belong to any branch on this repository, and well eventually this! Are distributed IID cs229 lecture notes 2018 independently and identically distributed ) resorting to an iterative algorithm you sure you to. Supervised Learning: Linear Regression & amp ; Logistic Regression 2. repeatedly takes step... All lectures are available on YouTube next guess forbe where that Linear function is zero TownshendPhD Cand living and... 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