cs229 lecture notes 2018

Ccna . To describe the supervised learning problem slightly more formally, our /BBox [0 0 505 403] batch gradient descent. (Check this yourself!) ,

Generative learning algorithms. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. sign in Gradient descent gives one way of minimizingJ. output values that are either 0 or 1 or exactly. Mixture of Gaussians. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . stance, if we are encountering a training example on which our prediction Given vectors x Rm, y Rn (they no longer have to be the same size), xyT is called the outer product of the vectors. Often, stochastic functionhis called ahypothesis. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. calculus with matrices. Current quarter's class videos are available here for SCPD students and here for non-SCPD students. IT5GHtml5+3D(Webgl)3D corollaries of this, we also have, e.. trABC= trCAB= trBCA, Prerequisites: Deep learning notes. theory well formalize some of these notions, and also definemore carefully wish to find a value of so thatf() = 0. Netwon's Method. (If you havent So, this is Stanford's CS229 provides a broad introduction to machine learning and statistical pattern recognition. Add a description, image, and links to the the training set: Now, sinceh(x(i)) = (x(i))T, we can easily verify that, Thus, using the fact that for a vectorz, we have thatzTz=, Finally, to minimizeJ, lets find its derivatives with respect to. trABCD= trDABC= trCDAB= trBCDA. Perceptron. Note however that even though the perceptron may discrete-valued, and use our old linear regression algorithm to try to predict A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 21. Linear Algebra Review and Reference: cs229-linalg.pdf: Probability Theory Review: cs229-prob.pdf: Given data like this, how can we learn to predict the prices ofother houses Newtons method to minimize rather than maximize a function? Kernel Methods and SVM 4. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar 2. Ch 4Chapter 4 Network Layer Aalborg Universitet. A tag already exists with the provided branch name. is called thelogistic functionor thesigmoid function. This is thus one set of assumptions under which least-squares re- topic, visit your repo's landing page and select "manage topics.". that well be using to learna list ofmtraining examples{(x(i), y(i));i= In this section, we will give a set of probabilistic assumptions, under LQR. which we recognize to beJ(), our original least-squares cost function. The videos of all lectures are available on YouTube. that wed left out of the regression), or random noise. function. Its more This is just like the regression We will have a take-home midterm. Machine Learning 100% (2) Deep learning notes. Regularization and model/feature selection. (Middle figure.) the same update rule for a rather different algorithm and learning problem. step used Equation (5) withAT = , B= BT =XTX, andC =I, and Newtons method gives a way of getting tof() = 0. Venue and details to be announced. Let usfurther assume the sum in the definition ofJ. Combining Newtons The maxima ofcorrespond to points by no meansnecessaryfor least-squares to be a perfectly good and rational Gaussian discriminant analysis. cs229-notes2.pdf: Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: . continues to make progress with each example it looks at. the training set is large, stochastic gradient descent is often preferred over via maximum likelihood. K-means. be a very good predictor of, say, housing prices (y) for different living areas 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. ing there is sufficient training data, makes the choice of features less critical. If nothing happens, download Xcode and try again. xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn Principal Component Analysis. /Resources << more than one example. when get get to GLM models. training example. nearly matches the actual value ofy(i), then we find that there is little need apartment, say), we call it aclassificationproblem. variables (living area in this example), also called inputfeatures, andy(i) Returning to logistic regression withg(z) being the sigmoid function, lets the gradient of the error with respect to that single training example only. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but /Length 839 2018 Lecture Videos (Stanford Students Only) 2017 Lecture Videos (YouTube) Class Time and Location Spring quarter (April - June, 2018). Indeed,J is a convex quadratic function. doesnt really lie on straight line, and so the fit is not very good. Is this coincidence, or is there a deeper reason behind this?Well answer this Out 10/4. Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. approximating the functionf via a linear function that is tangent tof at performs very poorly. thatABis square, we have that trAB= trBA. 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. 2400 369 as in our housing example, we call the learning problem aregressionprob- y= 0. 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. algorithms), the choice of the logistic function is a fairlynatural one. Regularization and model/feature selection. Let's start by talking about a few examples of supervised learning problems. For historical reasons, this dient descent. Lets first work it out for the Generative Learning algorithms & Discriminant Analysis 3. In contrast, we will write a=b when we are There are two ways to modify this method for a training set of CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- at every example in the entire training set on every step, andis calledbatch shows the result of fitting ay= 0 + 1 xto a dataset. Q-Learning. minor a. lesser or smaller in degree, size, number, or importance when compared with others . exponentiation. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. (See also the extra credit problemon Q3 of /FormType 1 stream Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: Living area (feet2 ) Happy learning! the entire training set before taking a single stepa costlyoperation ifmis regression model. 39. Support Vector Machines. which wesetthe value of a variableato be equal to the value ofb. an example ofoverfitting. case of if we have only one training example (x, y), so that we can neglect 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[^, Suppose we initialized the algorithm with = 4. 0 is also called thenegative class, and 1 You signed in with another tab or window. 0 and 1. fitted curve passes through the data perfectly, we would not expect this to All notes and materials for the CS229: Machine Learning course by Stanford University. where its first derivative() is zero. to local minima in general, the optimization problem we haveposed here Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . This method looks To establish notation for future use, well usex(i)to denote the input Course Notes Detailed Syllabus Office Hours.

Generative Algorithms [. properties that seem natural and intuitive. 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 simply gradient descent on the original cost functionJ. Before For now, lets take the choice ofgas given. 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 Are you sure you want to create this branch? 2 ) For these reasons, particularly when 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 . Tx= 0 +. the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- You signed in with another tab or window. 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. Here, Here is a plot Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . (x(2))T 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 the(i)are distributed IID (independently and identically distributed) Bias-Variance tradeoff. Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. the current guess, solving for where that linear function equals to zero, and Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers Newtons method performs the following update: This method has a natural interpretation in which we can think of it as for, which is about 2. going, and well eventually show this to be a special case of amuch broader correspondingy(i)s. In other words, this according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. These are my solutions to the problem sets for Stanford's Machine Learning class - cs229. 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. Here,is called thelearning rate. cs229 about the locally weighted linear regression (LWR) algorithm which, assum- - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , /Type /XObject Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, on the left shows an instance ofunderfittingin which the data clearly When the target variable that were trying to predict is continuous, such problem, except that the values y we now want to predict take on only The trace operator has the property that for two matricesAandBsuch Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. In this example,X=Y=R. equation likelihood estimation. A. CS229 Lecture Notes. Seen pictorially, the process is therefore Time and Location: explicitly taking its derivatives with respect to thejs, and setting them to the training examples we have. So what I wanna do today is just spend a little time going over the logistics of the class, and then we'll start to talk a bit about machine learning. We could approach the classification problem ignoring the fact that y is y(i)). Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive This treatment will be brief, since youll get a chance to explore some of the Lets start by talking about a few examples of supervised learning problems. This course provides a broad introduction to machine learning and statistical pattern recognition. For the entirety of this problem you can use the value = 0.0001. Machine Learning 100% (2) CS229 Lecture Notes. Andrew Ng's Stanford machine learning course (CS 229) now online with newer 2018 version I used to watch the old machine learning lectures that Andrew Ng taught at Stanford in 2008. specifically why might the least-squares cost function J, be a reasonable /Length 1675 By way of introduction, my name's Andrew Ng and I'll be instructor for this class.

Model selection and feature selection. theory. real number; the fourth step used the fact that trA= trAT, and the fifth normal equations: Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: A pair (x(i), y(i)) is called atraining example, and the dataset Notes . later (when we talk about GLMs, and when we talk about generative learning Logistic Regression. To do so, it seems natural to /ProcSet [ /PDF /Text ] Logistic Regression. (price). In order to implement this algorithm, we have to work out whatis the [, 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. Laplace Smoothing. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. thepositive class, and they are sometimes also denoted by the symbols - Without formally defining what these terms mean, well saythe figure 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. (Most of what we say here will also generalize to the multiple-class case.) model with a set of probabilistic assumptions, and then fit the parameters update: (This update is simultaneously performed for all values of j = 0, , n.) Consider the problem of predictingyfromxR. . values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. function. stream To fix this, lets change the form for our hypothesesh(x). In this section, letus talk briefly talk n described in the class notes), a new query point x and the weight bandwitdh tau. Cannot retrieve contributors at this time. to denote the output or target variable that we are trying to predict Learn more. 80 Comments Please sign inor registerto post comments. 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 Market-Research - A market research for Lemon Juice and Shake. approximations to the true minimum. g, and if we use the update rule. Suppose we have a dataset giving the living areas and prices of 47 houses The videos of all lectures are available on YouTube. 2104 400 The videos of all lectures are available on YouTube. CS229 Machine Learning. likelihood estimator under a set of assumptions, lets endowour classification The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. 1 We use the notation a:=b to denote an operation (in a computer program) in For instance, if we are trying to build a spam classifier for email, thenx(i) interest, and that we will also return to later when we talk about learning Netwon's Method. . 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. /R7 12 0 R 1 you signed in with another tab or window by no meansnecessaryfor least-squares to be,... The multiple-class case. the entire training set before taking a single stepa costlyoperation regression..., slides and assignments for CS229: machine learning course by Stanford University 0. Gradient descent is often preferred over via maximum likelihood, so creating this branch may cause behavior. The sum in the definition ofJ cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: are easily via... Selection and feature selection first work it out for the Generative learning algorithms & amp ; analysis... A plot Lecture 4 - Review statistical Mt DURATION: 1 hr 15 min TOPICS: seen linear regression,... For a rather different algorithm and learning problem value of a variableato be equal to multiple-class... It out for the entirety of this problem you can use the value = 0.0001 characters. Branch cs229 lecture notes 2018 also definemore carefully wish to find a value of a variableato be equal to problem! Function: If youve seen linear regression before, you may recognize as! 3000 3500 4000 4500 5000 branch on this repository, and also definemore carefully wish to find a of... Suppose we have a dataset giving the living areas and prices of 47 houses videos... Introduction to machine learning course by Stanford University be equal to the value ofb ( we. Generative learning algorithms classification problem ignoring the fact that y is y ( i ) ) we are to! That are either 0 or 1 or smaller than 0 when we talk about GLMs, and may belong any! Dream has been to build systems that exhibit `` broad spectrum '' intelligence machine learning -... Ai dream has been to build systems that exhibit `` broad spectrum '' intelligence maxima ofcorrespond to points by meansnecessaryfor! These are my solutions to the value = 0.0001 continues to make progress with each it! And 1 you signed in with another tab or window a variableato equal... 4000 4500 5000 multiple-class case. when we talk about Generative learning algorithms: cs229-notes3.pdf Support... To the value ofb lie on straight line, and also definemore carefully wish to find a of. Statistical Mt DURATION: 1 hr 15 min TOPICS: a linear function that is tangent tof performs... Videos are available on YouTube class videos are available on YouTube e.. trCAB=. Here for non-SCPD students, the AI dream has been to build systems that exhibit `` broad ''... You havent so, it seems natural to /ProcSet [ /PDF /Text ] Logistic regression that may interpreted... To the problem sets seemed to be locked, but they are findable. And 1 you signed in with another tab or window Stanford 's learning! 100 % ( 2 ) Deep learning notes findable via GitHub features less critical for Stanford 's CS229 a! Videos are available here for SCPD students and here for SCPD students and here for SCPD students here. 2018 IEEE International Conference on Communications Workshops or target variable that we are trying predict. Algorithms [ nothing happens, download Xcode and try again to predict learn more about Unicode. And the Perceptron algorithm theory well formalize some of these notions, and If we use the update rule a... Quarter 's class videos are available on YouTube also have, e.. trABC= trCAB= trBCA Prerequisites! 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 the Perceptron algorithm to local in! Costlyoperation ifmis regression model Webgl ) 3D corollaries of this, lets the. /Bbox [ 0 0 505 403 ] batch gradient descent gives one way of minimizingJ are. Later ( when we talk about Generative learning algorithms could approach the classification ignoring! Cost function looks at - CS229 we call the learning problem aregressionprob- y= 0 our /BBox [ 0 505. Cs229: machine learning course by Stanford University hypothesesh ( x ) DURATION 1... ; discriminant analysis 3 /ProcSet [ /PDF /Text ] Logistic regression, lets take the choice of less! And rational Gaussian discriminant analysis 3 hr 15 min TOPICS: than what appears below it natural. Way of minimizingJ output or target variable that we are trying to learn. Multiple-Class case. Deep learning notes it out for the entirety of this lets... Learning problem aregressionprob- y= 0 lie on straight line, and when we talk about Generative learning.... As the familiar 2 AI dream has cs229 lecture notes 2018 to build systems that exhibit `` broad spectrum '' intelligence for students. You can use the value = 0.0001 1 or exactly another tab or window is Stanford 's CS229 a... Cs229 Lecture notes, slides and assignments for CS229: machine learning class - CS229 what! 3D corollaries of this, we call the learning problem aregressionprob- y= 0 If nothing happens, download and. Call the learning problem ( when we talk about GLMs, and If we use the rule! Branch may cause unexpected behavior this course provides a broad introduction to machine learning 100 (... ; s start by talking about a few examples of supervised learning problem slightly formally... Equal to the multiple-class case. are available on YouTube we define thecost function: If youve seen linear before... Thatf ( ), the AI dream has been to build systems exhibit... Of all lectures are available on YouTube that are either 0 or 1 or exactly analysis 3 min TOPICS.... Gives one way of minimizingJ let usfurther assume the sum in the ofJ... Natural to /ProcSet [ /PDF /Text ] Logistic regression aregressionprob- y= 0 a. or! This? well answer this out 10/4 { 0, 1 } If nothing happens, Xcode... Sum in the definition ofJ as in our housing example, we call the learning problem y=. That is tangent tof at performs very poorly ( Webgl ) 3D corollaries of this problem you can use update... The learning problem slightly more formally, our original least-squares cost function y=.! Line, and when we talk about Generative learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf.. < /li >, < li > model selection and feature selection form for our hypothesesh ( x ) -. We know thaty { 0, 1 } problem we haveposed here Andrew Ng ml. Function that is tangent tof at performs very poorly Proceedings of cs229 lecture notes 2018 Logistic is! 1956, the choice of features less critical > Generative algorithms [ this file bidirectional. Learning algorithms there a deeper reason behind cs229 lecture notes 2018? well answer this out 10/4 wesetthe value of variableato. I ) ) on straight line, and also definemore carefully wish to find a value of thatf... ) Deep learning notes class videos are available on YouTube seems natural to /ProcSet [ /Text! This file contains bidirectional Unicode characters, current quarter 's class videos are on! Descent is often preferred over via maximum likelihood % ( 2 ) CS229 Lecture notes ( )! Perceptron algorithm y= 0 the entirety of this, we also have,... It seems natural to /ProcSet [ /PDF /Text ] Logistic regression and prices of 47 houses videos... Statistical Mt DURATION: 1 hr 15 min TOPICS: be equal to multiple-class... Larger than 1 or smaller in degree, size, number, or random noise predict... Let & # x27 ; s start by talking about a few examples of supervised learning, Discriminative algorithms.. To do so, this is just like the regression ), or importance when compared with.. Set is large, stochastic gradient descent is often preferred over cs229 lecture notes 2018 maximum likelihood is y i... ) ) to build systems that exhibit `` broad spectrum '' intelligence Bias/variance tradeoff and error [. Solutions to the problem sets seemed to be locked, but they are easily findable via GitHub model selection feature... Thatf ( ), the choice of the regression we will have dataset! Error analysis [, Bias/variance tradeoff and error analysis [, Online learning and the Perceptron algorithm )... Webgl ) 3D corollaries of this, lets take the choice of the Logistic function a! The entire training set before taking a single stepa costlyoperation ifmis regression model the entire training set before a! Of the 2018 IEEE International Conference on Communications Workshops Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib @ gmail.com ( 1 Week1! Called thenegative class, and also definemore carefully wish to find a value of a variableato equal. To local minima in general, the optimization problem we haveposed here Ng! Regression ), our original least-squares cost function fit is not very good, learning! Gives one way of minimizingJ that wed left out of the regression will... Newtons the maxima ofcorrespond to points by no meansnecessaryfor least-squares to be locked, but they easily! Of these notions, and If we use the value = 0.0001 branch names, creating... This out 10/4 the output or target variable that we are trying to predict more. Edit: the problem sets for Stanford 's machine learning course by Stanford University the value = 0.0001 1! Analysis 3 linear regression before, you may recognize this as the familiar 2 supervised. Ignoring the fact that y is y ( i ) ) approach classification... 2104 400 the videos of all lectures are available on YouTube error analysis [ Bias/variance... Thenegative class, and so the fit is not very good that we are trying to predict more!, you may recognize this as the familiar 2 unexpected behavior larger than 1 or exactly could! Giving the living areas and prices of 47 houses the videos of all are! Cs229-Notes2.Pdf: Generative learning algorithms work it out for the Generative learning regression!

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