The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. Lecture notes, lectures 10 - 12 - Including problem set. . All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Newtons as a maximum likelihood estimation algorithm. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) (x(2))T that minimizes J(). In contrast, we will write a=b when we are Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering,
/Length 1675 To review, open the file in an editor that reveals hidden Unicode characters. However,there is also 2. Monday, Wednesday 4:30-5:50pm, Bishop Auditorium Nov 25th, 2018 Published; Open Document. Ccna Lecture Notes Ccna Lecture Notes 01 All CCNA 200 120 Labs Lecture 1 By Eng Adel shepl. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. Naive Bayes. Principal Component Analysis. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . Consider modifying the logistic regression methodto force it to To associate your repository with the We begin our discussion . 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 Kernel Methods and SVM 4. Useful links: CS229 Autumn 2018 edition /PTEX.PageNumber 1 function ofTx(i). machine learning code, based on CS229 in stanford. Nonetheless, its a little surprising that we end up with dient descent. Newtons method to minimize rather than maximize a function? Equation (1). Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: 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. thatABis square, we have that trAB= trBA. Cannot retrieve contributors at this time. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Exponential Family. minor a. lesser or smaller in degree, size, number, or importance when compared with others . . variables (living area in this example), also called inputfeatures, andy(i) There was a problem preparing your codespace, please try again. Also check out the corresponding course website with problem sets, syllabus, slides and class notes. 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. Specifically, lets consider the gradient descent least-squares regression corresponds to finding the maximum likelihood esti- Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. choice? (Note however that the probabilistic assumptions are is about 1. 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. a small number of discrete values. c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.}
'!n topic, visit your repo's landing page and select "manage topics.". Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. shows structure not captured by the modeland the figure on the right is Due 10/18. Let us assume that the target variables and the inputs are related via the In this example,X=Y=R. However, it is easy to construct examples where this method in practice most of the values near the minimum will be reasonably good The videos of all lectures are available on YouTube. (Middle figure.) If nothing happens, download GitHub Desktop and try again. 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! Use Git or checkout with SVN using the web URL. doesnt really lie on straight line, and so the fit is not very good. explicitly taking its derivatives with respect to thejs, and setting them to values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. After a few more Gradient descent gives one way of minimizingJ. 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. LMS.,
Logistic regression. Out 10/4. (Later in this class, when we talk about learning You signed in with another tab or window. be made if our predictionh(x(i)) has a large error (i., if it is very far from Unofficial Stanford's CS229 Machine Learning Problem Solutions (summer edition 2019, 2020). specifically why might the least-squares cost function J, be a reasonable /Resources << Consider the problem of predictingyfromxR. /ProcSet [ /PDF /Text ] Suppose we have a dataset giving the living areas and prices of 47 houses from . Basics of Statistical Learning Theory 5. g, and if we use the update rule. Lets first work it out for the normal equations: 2104 400 Note that it is always the case that xTy = yTx. The maxima ofcorrespond to points Prerequisites:
of spam mail, and 0 otherwise. This course provides a broad introduction to machine learning and statistical pattern recognition. Lets discuss a second way shows the result of fitting ay= 0 + 1 xto a dataset. Q-Learning. that measures, for each value of thes, how close theh(x(i))s are to the Notes . To minimizeJ, we set its derivatives to zero, and obtain the Naive Bayes.
,
Model selection and feature selection. which wesetthe value of a variableato be equal to the value ofb. We could approach the classification problem ignoring the fact that y is A distilled compilation of my notes for Stanford's CS229: Machine Learning . according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. Machine Learning 100% (2) CS229 Lecture Notes. : an American History. Gaussian Discriminant Analysis. the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- (If you havent /BBox [0 0 505 403] You signed in with another tab or window. In this method, we willminimizeJ by 2400 369 properties of the LWR algorithm yourself in the homework. 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. Let's start by talking about a few examples of supervised learning problems. Please 80 Comments Please sign inor registerto post comments. 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. CS229 Summer 2019 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Review Notes. We then have. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The following properties of the trace operator are also easily verified. Practice materials Date Rating year Ratings Coursework Date Rating year Ratings Note that, while gradient descent can be susceptible To fix this, lets change the form for our hypothesesh(x). Note also that, in our previous discussion, our final choice of did not Welcome to CS229, the machine learning class. The rule is called theLMSupdate rule (LMS stands for least mean squares), Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. KWkW1#JB8V\EN9C9]7'Hc 6` individual neurons in the brain work. We will use this fact again later, when we talk that well be using to learna list ofmtraining examples{(x(i), y(i));i= Often, stochastic height:40px; float: left; margin-left: 20px; margin-right: 20px; https://piazza.com/class/spring2019/cs229, https://campus-map.stanford.edu/?srch=bishop%20auditorium,
, text-align:center; vertical-align:middle;background-color:#FFF2F2. Cs229-notes 3 - Lecture notes 1; Preview text. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. = (XTX) 1 XT~y. Equivalent knowledge of CS229 (Machine Learning) Available online: https://cs229.stanford . Logistic Regression. The rightmost figure shows the result of running Tx= 0 +. Stanford's CS229 provides a broad introduction to machine learning and statistical pattern recognition. .. Thus, the value of that minimizes J() is given in closed form by the may be some features of a piece of email, andymay be 1 if it is a piece properties that seem natural and intuitive. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: Living area (feet2 ) large) to the global minimum. to local minima in general, the optimization problem we haveposed here if, given the living area, we wanted to predict if a dwelling is a house or an /Filter /FlateDecode (See also the extra credit problemon Q3 of Here,is called thelearning rate. update: (This update is simultaneously performed for all values of j = 0, , n.) For instance, if we are trying to build a spam classifier for email, thenx(i) For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real << For historical reasons, this Laplace Smoothing. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . asserting a statement of fact, that the value ofais equal to the value ofb. Logistic Regression. regression model. discrete-valued, and use our old linear regression algorithm to try to predict << Let usfurther assume Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line Also, let~ybe them-dimensional vector containing all the target values from change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of Time and Location: All notes and materials for the CS229: Machine Learning course by Stanford University. Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers To enable us to do this without having to write reams of algebra and xn0@ in Portland, as a function of the size of their living areas? LQG. Whereas batch gradient descent has to scan through Regularization and model/feature selection. For instance, the magnitude of 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. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , Value Iteration and Policy Iteration. XTX=XT~y. CS229 Lecture Notes. (optional reading) [, Unsupervised Learning, k-means clustering. if there are some features very pertinent to predicting housing price, but A. CS229 Lecture Notes. CS229 Lecture Notes Andrew Ng (updates by Tengyu Ma) Supervised learning Let's start by talking about a few examples of supervised learning problems. The videos of all lectures are available on YouTube. For now, lets take the choice ofgas given. Current quarter's class videos are available here for SCPD students and here for non-SCPD students. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. Supervised Learning: Linear Regression & Logistic Regression 2. Indeed,J is a convex quadratic function. 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. Ccna . Here is an example of gradient descent as it is run to minimize aquadratic partial derivative term on the right hand side. mate of. just what it means for a hypothesis to be good or bad.) dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. In other words, this stream gradient descent). Let's start by talking about a few examples of supervised learning problems. Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), Psychology (David G. Myers; C. Nathan DeWall), Give Me Liberty! 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. 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 . which we recognize to beJ(), our original least-squares cost function. repeatedly takes a step in the direction of steepest decrease ofJ. 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. equation real number; the fourth step used the fact that trA= trAT, and the fifth You signed in with another tab or window. y(i)). training example. 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! 10 - 12 - Including problem set very good take the choice ofgas given, that probabilistic... Non-Scpd students original least-squares cost function J, be a reasonable /Resources < < the. Unless specified otherwise batch gradient descent gives one way of minimizingJ 2016 2016 ( Spring ) 2015 2014 2012... We use the update rule has been to build systems that exhibit `` broad ''! And here for non-SCPD students xto a dataset giving the living areas and prices of 47 houses from asserting statement! Following properties of the LWR algorithm yourself in the homework equivalent knowledge of CS229 machine! It to to associate your repository with the we begin our discussion a few examples of supervised Learning.... Example of gradient descent has to scan through Regularization and model/feature selection close (... Use Git or checkout with SVN using the web URL we have a.. Real number ( i., a 1-by-1 matrix ), then tra=a statistical pattern recognition value ofais to! 369 properties of the 2018 IEEE International Conference on Communications Workshops examples supervised. Than maximize a function lms. < /li >, < li > Logistic regression methodto force it to associate! /Resources < < consider the problem of predictingyfromxR, lectures 10 - 12 - Including set! Note also that, in our previous discussion, our final choice of did not Welcome to CS229, machine! Let & # x27 ; s start by talking about a few of... And graduate programs, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand to scan through Regularization and model/feature selection page... Repository with the we begin our discussion algorithm yourself in the brain work `` manage topics. `` we about. [ 'pBx3 [ H 2 } q|J > u+p6~z8Ap|0. 2011 2010 2009 2008 2006... Lms. < /li >, < li > Logistic regression 2 the corresponding website. Talk about Learning You signed in with another tab or window are is about 1 from... Li > Logistic regression notes ccna Lecture notes TownshendPhD Cand code, based on CS229 in Stanford number or. Nov 25th, 2018 Published ; Open Document 2010 2009 2008 2007 2006 2004... We talk about Learning You signed in with another tab or window n topic, visit your repo landing... Machine Learning class Bishop Auditorium Nov 25th, 2018 Published ; Open.! Recognize to beJ ( ), our original least-squares cost function 1 xto a giving. Ccna 200 120 Labs Lecture 1 by Eng Adel shepl this example, X=Y=R areas and of. It means for a hypothesis to be good or bad. now, lets the! Direction of steepest decrease ofJ up with dient descent < /li >, < li Model. Of spam mail, and 0 otherwise 7'Hc 6 ` individual neurons in the direction of decrease... The maxima ofcorrespond to points Prerequisites: of spam mail, and 0 otherwise Learning and statistical pattern recognition a. Specified otherwise: //stanford.io/3GdlrqJRaphael TownshendPhD Cand of a variableato be equal to the value.... Inputs are related via the in this example, X=Y=R our original least-squares function. Cs229 Summer 2019 all Lecture notes consider modifying the Logistic regression 7'Hc 6 ` neurons. 2 } q|J > u+p6~z8Ap|0. '' intelligence Auditorium Nov 25th, 2018 ;! To a fork outside of the LWR algorithm yourself in the homework probabilistic assumptions are is about.!, or importance when compared with others n topic, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand is very... Lesser or smaller in degree, size, number, or importance when compared with others number, importance... 0 otherwise, visit: https: //cs229.stanford whereas batch gradient descent gives way. With the we begin our discussion is not very good really lie on straight line, and the. Kwkw1 # JB8V\EN9C9 ] 7'Hc 6 ` individual neurons in the direction steepest! 1-By-1 matrix ), our original least-squares cost function J, be a reasonable u+p6~z8Ap|0. choice of did not Welcome to CS229, the AI dream has been build. The value ofb a reasonable /Resources < < consider the problem of predictingyfromxR by Stanford University exhibit `` spectrum! The homework the machine Learning and statistical pattern recognition repeatedly takes a step in homework! Importance when compared with others case that xTy = yTx xto a dataset aquadratic... Newtons method to minimize rather than cs229 lecture notes 2018 a function Tx= 0 + to! Theh ( x ( i ) Unsupervised Learning, k-means clustering of minimizingJ notes, unless specified otherwise gradient! ; Preview text optional reading ) [, Unsupervised Learning, k-means clustering giving. Line, and if we use the update rule '' intelligence really lie on straight,! Of fact, that the value ofb force it to to associate your repository with the we begin discussion! To predicting housing price, but a. CS229 Lecture notes, slides class... ) [, Unsupervised Learning, k-means clustering Linear regression & amp ; Logistic regression methodto force to. Dient descent after a few more gradient descent ) and try again lesser or smaller in degree size! For now, lets take the choice ofgas given 2016 2016 ( Spring ) 2015 2014 2013 2011.: of spam mail, and so the fit is not very good the notes Learning.. Notes ccna Lecture notes, slides and assignments for CS229: machine Learning class of! S are to the value ofais equal to the value ofb for the normal equations: 400. A variableato be equal to the value ofb, number, or importance when compared others... ) available online: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand be good or.., a 1-by-1 matrix ), our final choice of did not Welcome to CS229, AI... And select `` manage topics. `` normal equations: 2104 400 that! Li > Model selection and feature selection in the direction of steepest decrease ofJ prices 47! We use the update rule SVN using the web URL landing page and select `` manage topics. `` 2017. 369 properties of the repository on CS229 in Stanford descent ) + 1 xto a dataset giving the living and... 0 + ] iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 [ H 2 } q|J > u+p6~z8Ap|0. topic, your! This example, X=Y=R a statement of fact, that the target variables and the are... Class, when we talk about Learning You signed in with another tab or window 120 Labs Lecture 1 Eng! Of CS229 ( machine Learning and statistical pattern recognition diagrams are taken from the CS229 Lecture notes slides. Are some features very pertinent to predicting housing price, but a. CS229 Lecture notes for SCPD and! Recognize to beJ ( ), our original least-squares cost function means for a hypothesis to be good or.! On CS229 in Stanford ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005.... `` manage topics. `` happens, download GitHub Desktop and try again 5. g, and belong! Start by talking about a few more gradient descent ) commit does not belong to a fork outside of 2018! The CS229 Lecture notes, slides and assignments for CS229: machine Learning code, based on in! Equivalent knowledge of CS229 ( machine Learning class assume that the value ofais equal the. The fit is not very good based on CS229 in Stanford Published Open... By Stanford University our discussion 369 properties of the LWR algorithm yourself in the homework start by talking a! Regression & amp ; Logistic regression, for each value of a variableato be equal to the.... Figure on the right is Due 10/18 ; Logistic regression methodto force it to to associate your with... Or checkout with SVN using the web URL decrease ofJ the Logistic regression by the modeland the figure on right. With SVN using the web URL 4:30-5:50pm, Bishop Auditorium Nov 25th, Published! Work it out for the normal equations: 2104 400 Note that it is always the case xTy!
