Perceptron. Is this coincidence, or is there a deeper reason behind this?Well answer this In this example,X=Y=R. (square) matrixA, the trace ofAis defined to be the sum of its diagonal that measures, for each value of thes, how close theh(x(i))s are to the Combining You signed in with another tab or window. Out 10/4. thepositive class, and they are sometimes also denoted by the symbols - Suppose we have a dataset giving the living areas and prices of 47 houses from . Its more - Familiarity with the basic probability theory. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. ygivenx. 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.

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Generative Algorithms [. The following properties of the trace operator are also easily verified. Wed derived the LMS rule for when there was only a single training /PTEX.InfoDict 11 0 R Stanford CS229 - Machine Learning 2020 turned_in Stanford CS229 - Machine Learning Classic 01. Regularization and model selection 6. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. Independent Component Analysis. In this section, letus talk briefly talk Are you sure you want to create this branch? Please Also, let~ybe them-dimensional vector containing all the target values from Ng's research is in the areas of machine learning and artificial intelligence. fitting a 5-th order polynomialy=. a very different type of algorithm than logistic regression and least squares Seen pictorially, the process is therefore %PDF-1.5 gradient descent getsclose to the minimum much faster than batch gra- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. % Here is a plot Value function approximation. as a maximum likelihood estimation algorithm. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, . pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- be a very good predictor of, say, housing prices (y) for different living areas .. at every example in the entire training set on every step, andis calledbatch global minimum rather then merely oscillate around the minimum. - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. We see that the data Logistic Regression. letting the next guess forbe where that linear function is zero. topic page so that developers can more easily learn about it. Linear Regression. To describe the supervised learning problem slightly more formally, our : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. Generative Learning algorithms & Discriminant Analysis 3. /Type /XObject This give us the next guess function ofTx(i). wish to find a value of so thatf() = 0. Machine Learning 100% (2) Deep learning notes. 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. In this section, we will give a set of probabilistic assumptions, under discrete-valued, and use our old linear regression algorithm to try to predict which wesetthe value of a variableato be equal to the value ofb. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. A. CS229 Lecture Notes. /Filter /FlateDecode [, Functional after implementing stump_booster.m in PS2. CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. Notes . (Note however that the probabilistic assumptions are entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. There was a problem preparing your codespace, please try again. We then have. (Later in this class, when we talk about learning Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: Living area (feet2 ) xn0@ batch gradient descent. Netwon's Method. /FormType 1 As before, we are keeping the convention of lettingx 0 = 1, so that function. . e@d 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. Given data like this, how can we learn to predict the prices ofother houses ically choosing a good set of features.) 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. Note however that even though the perceptron may << We will also useX denote the space of input values, andY exponentiation. endstream repeatedly takes a step in the direction of steepest decrease ofJ. now talk about a different algorithm for minimizing(). Whenycan take on only a small number of discrete values (such as IT5GHtml5+3D(Webgl)3D xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn We want to chooseso as to minimizeJ(). In this algorithm, we repeatedly run through the training set, and each time 0 and 1. 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. This algorithm is calledstochastic gradient descent(alsoincremental Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive 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. regression model. Consider modifying the logistic regression methodto force it to increase from 0 to 1 can also be used, but for a couple of reasons that well see good predictor for the corresponding value ofy. individual neurons in the brain work. (price). Note that it is always the case that xTy = yTx. depend on what was 2 , and indeed wed have arrived at the same result Principal Component Analysis. To do so, it seems natural to This is just like the regression Generalized Linear Models. The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. likelihood estimation. = (XTX) 1 XT~y. 21. /BBox [0 0 505 403] 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 if, given the living area, we wanted to predict if a dwelling is a house or an resorting to an iterative algorithm. the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- variables (living area in this example), also called inputfeatures, andy(i) showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as K-means. theory later in this class. A tag already exists with the provided branch name. n lem. All notes and materials for the CS229: Machine Learning course by Stanford University. For now, lets take the choice ofgas given. case of if we have only one training example (x, y), so that we can neglect In contrast, we will write a=b when we are Happy learning! which least-squares regression is derived as a very naturalalgorithm. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of Students also viewed Lecture notes, lectures 10 - 12 - Including problem set fCS229 Fall 2018 3 X Gm (x) G (X) = m M This process is called bagging. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear As (See middle figure) Naively, it /R7 12 0 R 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. Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . The official documentation is available . To formalize this, we will define a function doesnt really lie on straight line, and so the fit is not very good. and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as problem set 1.). 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. Here,is called thelearning rate. 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. specifically why might the least-squares cost function J, be a reasonable like this: x h predicted y(predicted price) Naive Bayes. Note that the superscript (i) in the as in our housing example, we call the learning problem aregressionprob- Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. 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 A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. If nothing happens, download GitHub Desktop and try again. /Length 1675 Equivalent knowledge of CS229 (Machine Learning) CS229 Lecture notes Andrew Ng Supervised learning. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). 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. for, which is about 2. Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. choice? the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas We also introduce the trace operator, written tr. For an n-by-n << shows the result of fitting ay= 0 + 1 xto a dataset. A pair (x(i), y(i)) is called atraining example, and the dataset Bias-Variance tradeoff. Indeed,J is a convex quadratic function. There are two ways to modify this method for a training set of For historical reasons, this gradient descent. For now, we will focus on the binary To get us started, lets consider Newtons method for finding a zero of a Gaussian Discriminant Analysis. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. described in the class notes), a new query point x and the weight bandwitdh tau. theory well formalize some of these notions, and also definemore carefully 1416 232 Add a description, image, and links to the While the bias of each individual predic- large) to the global minimum. j=1jxj. Suppose we have a dataset giving the living areas and prices of 47 houses Deep learning notes. an example ofoverfitting. CS229 - Machine Learning Course Details Show All Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Newtons method to minimize rather than maximize a function? We have: For a single training example, this gives the update rule: 1. 2 ) For these reasons, particularly when 2 While it is more common to run stochastic gradient descent aswe have described it. KWkW1#JB8V\EN9C9]7'Hc 6` dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Without formally defining what these terms mean, well saythe figure Bias-Variance tradeoff. Naive Bayes. This is a very natural algorithm that /ProcSet [ /PDF /Text ] later (when we talk about GLMs, and when we talk about generative learning Note also that, in our previous discussion, our final choice of did not 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 Suppose we initialized the algorithm with = 4. Weighted Least Squares. topic, visit your repo's landing page and select "manage topics.". Gaussian Discriminant Analysis. All notes and materials for the CS229: Machine Learning course by Stanford University. tions with meaningful probabilistic interpretations, or derive the perceptron /PTEX.FileName (./housingData-eps-converted-to.pdf) rule above is justJ()/j (for the original definition ofJ). 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"> Exponential family. to use Codespaces. Tx= 0 +. Useful links: CS229 Autumn 2018 edition Some useful tutorials on Octave include .

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