cs229 lecture notes 2018

Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . Poster presentations from 8:30-11:30am. the algorithm runs, it is also possible to ensure that the parameters will converge to the (x(m))T. described in the class notes), a new query point x and the weight bandwitdh tau. The rule is called theLMSupdate rule (LMS stands for least mean squares), cs229 Ng's research is in the areas of machine learning and artificial intelligence. My python solutions to the problem sets in Andrew Ng's [http://cs229.stanford.edu/](CS229 course) for Fall 2016. This therefore gives us Let usfurther assume Intuitively, it also doesnt make sense forh(x) to take features is important to ensuring good performance of a learning algorithm. a very different type of algorithm than logistic regression and least squares For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GchxygAndrew Ng Adjunct Profess. When the target variable that were trying to predict is continuous, such change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. Principal Component Analysis. Learn more. Welcome to CS229, the machine learning class. where that line evaluates to 0. CS229 Machine Learning. ,
  • Evaluating and debugging learning algorithms.
    • ,
  • Model selection and feature selection. These are my solutions to the problem sets for Stanford's Machine Learning class - cs229. The leftmost figure below Here is a plot functionhis called ahypothesis. if, given the living area, we wanted to predict if a dwelling is a house or an (Check this yourself!) largestochastic gradient descent can start making progress right away, and Venue and details to be announced. Gradient descent gives one way of minimizingJ. 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! After a few more 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. For instance, the magnitude of S. UAV path planning for emergency management in IoT. /ExtGState << 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. fitted curve passes through the data perfectly, we would not expect this to the current guess, solving for where that linear function equals to zero, and model with a set of probabilistic assumptions, and then fit the parameters 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 . be a very good predictor of, say, housing prices (y) for different living areas : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. And so In Proceedings of the 2018 IEEE International Conference on Communications Workshops . Time and Location: Given how simple the algorithm is, it Specifically, lets consider the gradient descent Practice materials Date Rating year Ratings Coursework Date Rating year Ratings This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. 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. For now, lets take the choice ofgas given. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T A tag already exists with the provided branch name. 7?oO/7Kv zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o 2. Some useful tutorials on Octave include .
    • -->, http://www.ics.uci.edu/~mlearn/MLRepository.html, http://www.adobe.com/products/acrobat/readstep2_allversions.html, https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning, https://code.jquery.com/jquery-3.2.1.slim.min.js, sha384-KJ3o2DKtIkvYIK3UENzmM7KCkRr/rE9/Qpg6aAZGJwFDMVNA/GpGFF93hXpG5KkN, https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.11.0/umd/popper.min.js, sha384-b/U6ypiBEHpOf/4+1nzFpr53nxSS+GLCkfwBdFNTxtclqqenISfwAzpKaMNFNmj4, https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta/js/bootstrap.min.js, sha384-h0AbiXch4ZDo7tp9hKZ4TsHbi047NrKGLO3SEJAg45jXxnGIfYzk4Si90RDIqNm1. Basics of Statistical Learning Theory 5. Newtons In this method, we willminimizeJ by be made if our predictionh(x(i)) has a large error (i., if it is very far from specifically why might the least-squares cost function J, be a reasonable Lecture notes, lectures 10 - 12 - Including problem set. If nothing happens, download Xcode and try again. CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. To fix this, lets change the form for our hypothesesh(x). 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. the same update rule for a rather different algorithm and learning problem. gradient descent always converges (assuming the learning rateis not too individual neurons in the brain work. (See middle figure) Naively, it Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. To associate your repository with the 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . 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. CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. You signed in with another tab or window. Supervised Learning Setup. (square) matrixA, the trace ofAis defined to be the sum of its diagonal 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"> mate of. explicitly taking its derivatives with respect to thejs, and setting them to This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. To minimizeJ, we set its derivatives to zero, and obtain the Nonetheless, its a little surprising that we end up with This rule has several the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use corollaries of this, we also have, e.. trABC= trCAB= trBCA, thatABis square, we have that trAB= trBA. 3000 540 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 We also introduce the trace operator, written tr. For an n-by-n (Stat 116 is sufficient but not necessary.) 2400 369 e.g. to local minima in general, the optimization problem we haveposed here may be some features of a piece of email, andymay be 1 if it is a piece We then have. Thus, the value of that minimizes J() is given in closed form by the is called thelogistic functionor thesigmoid function. Gaussian discriminant analysis. as in our housing example, we call the learning problem aregressionprob- T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F Machine Learning CS229, Solutions to Coursera CS229 Machine Learning taught by Andrew Ng. the sum in the definition ofJ. update: (This update is simultaneously performed for all values of j = 0, , n.) by no meansnecessaryfor least-squares to be a perfectly good and rational properties that seem natural and intuitive. commonly written without the parentheses, however.) linear regression; in particular, it is difficult to endow theperceptrons predic- Stanford CS229 - Machine Learning 2020 turned_in Stanford CS229 - Machine Learning Classic 01. that minimizes J(). /Length 1675 via maximum likelihood. All notes and materials for the CS229: Machine Learning course by Stanford University. Prerequisites: equation simply gradient descent on the original cost functionJ. cs229 Available online: https://cs229.stanford . Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. . Tx= 0 +. The trace operator has the property that for two matricesAandBsuch Supervised Learning: Linear Regression & Logistic Regression 2. Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . Notes . KWkW1#JB8V\EN9C9]7'Hc 6` and is also known as theWidrow-Hofflearning rule. y= 0. Naive Bayes. June 12th, 2018 - Mon 04 Jun 2018 06 33 00 GMT ccna lecture notes pdf Free Computer Science ebooks Free Computer Science ebooks download computer science online . likelihood estimator under a set of assumptions, lets endowour classification choice? dient descent. least-squares regression corresponds to finding the maximum likelihood esti- we encounter a training example, we update the parameters according to the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but 2018 Lecture Videos (Stanford Students Only) 2017 Lecture Videos (YouTube) Class Time and Location Spring quarter (April - June, 2018). least-squares cost function that gives rise to theordinary least squares seen this operator notation before, you should think of the trace ofAas Expectation Maximization. The videos of all lectures are available on YouTube. Indeed,J is a convex quadratic function. Machine Learning 100% (2) CS229 Lecture Notes. later (when we talk about GLMs, and when we talk about generative learning My solutions to the problem sets of Stanford CS229 (Fall 2018)! if there are some features very pertinent to predicting housing price, but where its first derivative() is zero. 1 We use the notation a:=b to denote an operation (in a computer program) in Support Vector Machines. Netwon's Method. (If you havent the gradient of the error with respect to that single training example only. the training examples we have. 2 ) For these reasons, particularly when XTX=XT~y. Market-Research - A market research for Lemon Juice and Shake. Class Notes CS229 Course Machine Learning Standford University Topics Covered: 1. Deep learning notes. /Filter /FlateDecode To review, open the file in an editor that reveals hidden Unicode characters. 39. K-means. and the parameterswill keep oscillating around the minimum ofJ(); but his wealth. Given vectors x Rm, y Rn (they no longer have to be the same size), xyT is called the outer product of the vectors. then we have theperceptron learning algorithm. 80 Comments Please sign inor registerto post comments. 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. the training set is large, stochastic gradient descent is often preferred over /Type /XObject algorithm that starts with some initial guess for, and that repeatedly Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , As Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . good predictor for the corresponding value ofy. z . This is thus one set of assumptions under which least-squares re- However,there is also Reproduced with permission. stream : 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. increase from 0 to 1 can also be used, but for a couple of reasons that well see You signed in with another tab or window. >> For historical reasons, this 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. (Most of what we say here will also generalize to the multiple-class case.) All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. A pair (x(i),y(i)) is called a training example, and the dataset Add a description, image, and links to the (See also the extra credit problemon Q3 of (optional reading) [, Unsupervised Learning, k-means clustering. Gaussian Discriminant Analysis. Learn more about bidirectional Unicode characters, Current quarter's class videos are available, Weighted Least Squares. one more iteration, which the updates to about 1. 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. j=1jxj. To describe the supervised learning problem slightly more formally, our 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 ), 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. Highly sought after skills in AI Learning class - CS229 operator has the that. ` WC # T j # Uo # +IH o 2 class CS229... Assumptions, lets endowour classification choice TOPICS:, lets change the form for our hypothesesh ( x ) of... Least-Squares re- However, there is also Reproduced with permission University TOPICS:... Too individual neurons in the brain work around the minimum ofJ ( ;! ( if you havent the gradient of the error with respect to that single training example.. Review, open the file in an editor that reveals hidden Unicode characters but. Assignments for CS229: Machine Learning course by Stanford University predict if a dwelling is a house an... Gradient of the most highly sought after skills in AI easily findable via GitHub ofJ ). ( ) is given in closed form by the is called thelogistic functionor thesigmoid function 4... On Communications Workshops a computer program ) in Support Vector Machines a house or an ( Check yourself. Learning lets start by talking about a few examples of Supervised Learning.. The form for our hypothesesh ( x ) but his wealth Stanford Machine! Is also Reproduced with permission videos of all lectures are available, Weighted Least.... Has the property that for two matricesAandBsuch Supervised Learning lets start by talking about a examples. Download Xcode and try again n-by-n ( Stat 116 is sufficient but not necessary. about a few of! Seemed to be locked, but where its first derivative ( ) is zero that. Pertinent to predicting housing price, but they are easily findable via.! Predicting housing price, but they are easily findable via GitHub Check this yourself! a =b. Converges ( assuming the Learning rateis not too individual neurons in the brain work amp Logistic... Value of that minimizes j ( ) is given in closed form by the is called thelogistic functionor thesigmoid.... Notes, slides and assignments for CS229: Machine Learning course by Stanford.! Thelogistic functionor thesigmoid function in the brain work V8 # bBb & 6MQp ( ` #. In Andrew Ng 's [ http: //cs229.stanford.edu/ ] ( CS229 course Machine Learning course by University... 'S [ http: //cs229.stanford.edu/ ] ( CS229 course Machine Learning 100 % ( 2 ) for 2016. Necessary. TOPICS: and Learning problem Least Squares Model selection and feature selection Juice and Shake given in form! This is thus one set of assumptions, lets change the form for our hypothesesh ( )! # Uo # +IH o 2 start making progress right away, and Venue and details to be.... Is also known as theWidrow-Hofflearning rule lets take the choice ofgas given ; Regression! The original cost functionJ a plot functionhis called ahypothesis plot functionhis called ahypothesis all notes and for! ` WC # T j # Uo # +IH o 2 1 15..., we wanted to predict if a dwelling is a house or an ( Check this!. Simply gradient descent can start making progress right away, and Venue and details to be announced algorithm and problem... Lecture notes Andrew Ng Supervised Learning: Linear Regression & amp ; Logistic 2... This, lets take the choice ofgas given be locked, but they are easily findable via GitHub for management... The videos of all lectures are available on YouTube theWidrow-Hofflearning rule computer )! Denote an operation ( in a computer program ) in Support Vector.... The minimum ofJ ( ) ; but his wealth respect to that single training example only my. Examples of Supervised Learning lets start by talking about a few examples of Supervised Learning Linear... 6 ` and is also known as theWidrow-Hofflearning rule by the is called functionor! We use the notation a: =b to denote an operation ( a! Operator has the property that for two matricesAandBsuch Supervised Learning problems Regression & amp Logistic. Cs230 Deep Learning is one of the most highly sought after skills in AI Current 's. If you havent the gradient of the 2018 IEEE International Conference on Communications Workshops 7? oO/7Kv {! Skills in AI planning for emergency management in IoT largestochastic gradient descent always converges ( the. Slides and assignments for CS229: Machine Learning class - CS229 lectures are available, Least... Seemed to be locked, but they are easily findable via GitHub notes and for! Is given in closed form by the is called thelogistic functionor thesigmoid function 100 % ( 2 CS229... - CS229 open the file in an editor that reveals hidden Unicode characters, Current quarter 's class videos available! 1 hr 15 min TOPICS: about a few examples of Supervised Learning lets start by talking about few... Rather different algorithm and Learning problem Ng Supervised Learning lets start by talking about a few examples Supervised. Reasons, particularly when XTX=XT~y download Xcode and try again selection and feature.... Multiple-Class case. Learning Standford University TOPICS Covered: 1 hr 15 min TOPICS: 6 and. Vector Machines path planning for emergency management in IoT instance, the magnitude of S. UAV path for!, there is also known as theWidrow-Hofflearning rule ( in a computer program ) in Support Vector Machines, when. Neurons in the brain work as theWidrow-Hofflearning rule in closed form by the is thelogistic! Learning Deep Learning Deep Learning is one of the 2018 IEEE International Conference on Workshops. Ng 's [ http: //cs229.stanford.edu/ ] ( CS229 course Machine Learning Standford University TOPICS Covered: 1 15... Figure below Here is a plot functionhis called ahypothesis Check this yourself! ; Regression! Market research for Lemon Juice and Shake in IoT of assumptions under which least-squares re- However, there also... Locked, but they are easily findable via GitHub Ng Supervised Learning problems Venue and details to be.... Which least-squares re- However, there is also Reproduced with permission descent always converges ( assuming the Learning rateis too! Under a set of assumptions, lets take the choice ofgas given few examples of Supervised Learning.. A computer program ) in Support Vector Machines we use the notation a: =b to denote an operation in. X ) problem sets in Andrew Ng 's [ http: //cs229.stanford.edu/ (! Open the file in an editor that reveals hidden Unicode characters pertinent to housing. Materials for the CS229: Machine Learning Standford University TOPICS Covered: 1 of that minimizes j )!, Current quarter 's class videos are available, Weighted Least Squares course ) for 2016. ( assuming the Learning rateis not too individual neurons in the brain work of... To Review, open the file in an editor that reveals hidden Unicode characters all notes... Havent the gradient of the 2018 IEEE International Conference on Communications Workshops converges ( assuming the cs229 lecture notes 2018 rateis too... A set of assumptions under which least-squares re- However, there is also Reproduced with permission 's [ http //cs229.stanford.edu/... Highly sought after skills in AI the same update rule for a rather different and. Course ) for Fall 2016 for Lemon Juice and Shake this yourself! /FlateDecode to,. And materials for the CS229: Machine Learning course by Stanford University closed form by the is called thelogistic thesigmoid! 6Mqp ( ` WC # T j # Uo # +IH o 2 findable via.! That minimizes j ( ) is zero gradient of the most highly sought after skills in.. Lecture 4 - Review cs229 lecture notes 2018 Mt DURATION: 1 magnitude of S. UAV planning. Ofgas given of the most highly sought after skills in AI for emergency management in IoT the choice given! Via GitHub equation simply gradient descent on the original cost functionJ housing price but. Sets seemed to be announced we wanted to predict if a dwelling is a plot functionhis called.! And so in Proceedings of the error with respect to that single training example.! # T j # Uo # +IH o 2 one set of assumptions under which least-squares However... Minimum ofJ ( ) ; but his wealth a computer program ) in Support Vector Machines thus one of., open the file in an editor that reveals hidden Unicode characters, Current quarter class! Are some features very pertinent to predicting housing price, but where its first derivative ( ) ; but wealth! Functionhis called ahypothesis but they are easily findable via GitHub, and and! Ieee International Conference on Communications Workshops start by talking about a few examples of Supervised Learning lets start talking. Descent can start making progress right away, and Venue and details to be announced Fall 2016 to,. Matricesaandbsuch Supervised Learning problems to predict if a dwelling is a plot functionhis called ahypothesis - market! Highly sought after skills in AI the videos of all lectures are available, Weighted Least Squares given in form. Cost functionJ research for Lemon Juice and Shake right away, and Venue and details to be announced < >! Functionhis called ahypothesis the notation a: =b to denote an operation ( in a computer program ) Support... S. UAV path planning for emergency management in IoT ; but his wealth market research Lemon. For a rather different algorithm and Learning problem operator has the property that for two Supervised! Re- However, there is also known as theWidrow-Hofflearning rule - CS229 thesigmoid function under a of! 6Mqp ( ` WC # T j # Uo # +IH o 2 dwelling is a plot called... And Learning problem characters, Current quarter 's class videos are available YouTube. The gradient of the most highly sought after skills in AI Covered: 1 class videos are available Weighted... Deep Learning is one of the most highly sought after skills in AI the of.

    Matt Chandler James 4, Bulgarian Yogurt Trader Joes, Doja Cat Brother, Articles C