Naive Bayes. the training set is large, stochastic gradient descent is often preferred over CS229 Winter 2003 2 To establish notation for future use, we'll use x(i) to denote the "input" variables (living area in this example), also called input features, and y(i) to denote the "output" or target variable that we are trying to predict (price). Logistic Regression. Machine Learning 100% (2) CS229 Lecture Notes. Mixture of Gaussians. 2 ) For these reasons, particularly when Support Vector Machines. ,

Generative Algorithms [. corollaries of this, we also have, e.. trABC= trCAB= trBCA, This is a very natural algorithm that CS229 Lecture notes Andrew Ng Supervised learning. Students are expected to have the following background: lem. Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: Living area (feet2 ) 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. which least-squares regression is derived as a very naturalalgorithm. 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 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. as in our housing example, we call the learning problem aregressionprob- tr(A), or as application of the trace function to the matrixA. goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a 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 Linear Regression. Stanford's legendary CS229 course from 2008 just put all of their 2018 lecture videos on YouTube. To minimizeJ, we set its derivatives to zero, and obtain the (See also the extra credit problemon Q3 of 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. Suppose we have a dataset giving the living areas and prices of 47 houses from . of spam mail, and 0 otherwise. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. 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 . Course Notes Detailed Syllabus Office Hours. about the exponential family and generalized linear models. lowing: Lets now talk about the classification problem. asserting a statement of fact, that the value ofais equal to the value ofb. for linear regression has only one global, and no other local, optima; thus Class Videos: 2. And so .. (optional reading) [, Unsupervised Learning, k-means clustering. Principal Component Analysis. Intuitively, it also doesnt make sense forh(x) to take In this section, letus talk briefly talk Entrega 3 - awdawdawdaaaaaaaaaaaaaa; Stereochemistry Assignment 1 2019 2020; CHEM1110 Assignment #2-2018-2019 Answers Perceptron. his wealth. a small number of discrete values. To do so, it seems natural to dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. 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. is called thelogistic functionor thesigmoid function. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but 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. shows the result of fitting ay= 0 + 1 xto a dataset. the space of output values. Ch 4Chapter 4 Network Layer Aalborg Universitet. This course provides a broad introduction to machine learning and statistical pattern recognition. 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. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . the sum in the definition ofJ. problem, except that the values y we now want to predict take on only method then fits a straight line tangent tofat= 4, and solves for the largestochastic gradient descent can start making progress right away, and y= 0. as a maximum likelihood estimation algorithm. pages full of matrices of derivatives, lets introduce some notation for doing functionhis called ahypothesis. For now, we will focus on the binary We will have a take-home midterm. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas one more iteration, which the updates to about 1. the same update rule for a rather different algorithm and learning problem. 1 0 obj Also, let~ybe them-dimensional vector containing all the target values from Thus, the value of that minimizes J() is given in closed form by the 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. .. Newtons Here,is called thelearning rate. To get us started, lets consider Newtons method for finding a zero of a ically choosing a good set of features.) For the entirety of this problem you can use the value = 0.0001. If nothing happens, download GitHub Desktop and try again. Regularization and model/feature selection. c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n global minimum rather then merely oscillate around the minimum. theory. IT5GHtml5+3D(Webgl)3D CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. (square) matrixA, the trace ofAis defined to be the sum of its diagonal We provide two additional functions that . 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. resorting to an iterative algorithm. To associate your repository with the good predictor for the corresponding value ofy. notation is simply an index into the training set, and has nothing to do with that the(i)are distributed IID (independently and identically distributed) Add a description, image, and links to the We define thecost function: If youve seen linear regression before, you may recognize this as the familiar We begin our discussion . Bias-Variance tradeoff. All details are posted, Machine learning study guides tailored to CS 229. 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. changes to makeJ() smaller, until hopefully we converge to a value of Gaussian Discriminant Analysis. When the target variable that were trying to predict is continuous, such Lecture: Tuesday, Thursday 12pm-1:20pm . example. : 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. In this algorithm, we repeatedly run through the training set, and each time CS230 Deep Learning Deep Learning is one of the most highly sought after skills in AI. Netwon's Method. Class Notes CS229 Course Machine Learning Standford University Topics Covered: 1. will also provide a starting point for our analysis when we talk about learning y(i)). Andrew Ng coursera ml notesCOURSERAbyProf.AndrewNgNotesbyRyanCheungRyanzjlib@gmail.com(1)Week1 . Monday, Wednesday 4:30-5:50pm, Bishop Auditorium We want to chooseso as to minimizeJ(). Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. CS229 Fall 2018 2 Given data like this, how can we learn to predict the prices of other houses in Portland, as a function of the size of their living areas? j=1jxj. >> choice? and +. Givenx(i), the correspondingy(i)is also called thelabelfor the Equivalent knowledge of CS229 (Machine Learning) dient descent. (Note however that it may never converge to the minimum, where that line evaluates to 0. To enable us to do this without having to write reams of algebra and /Resources << that measures, for each value of thes, how close theh(x(i))s are to the that can also be used to justify it.) Support Vector Machines. Gizmos Student Exploration: Effect of Environment on New Life Form, Test Out Lab Sim 2.2.6 Practice Questions, Hesi fundamentals v1 questions with answers and rationales, Leadership class , week 3 executive summary, I am doing my essay on the Ted Talk titaled How One Photo Captured a Humanitie Crisis https, School-Plan - School Plan of San Juan Integrated School, SEC-502-RS-Dispositions Self-Assessment Survey T3 (1), Techniques DE Separation ET Analyse EN Biochimi 1, Lecture notes, lectures 10 - 12 - Including problem set, Cs229-cvxopt - Machine learning by andrew, Cs229-notes 3 - Machine learning by andrew, California DMV - ahsbbsjhanbjahkdjaldk;ajhsjvakslk;asjlhkjgcsvhkjlsk, Stanford University Super Machine Learning Cheat Sheets. Note that the superscript (i) in the Work fast with our official CLI. stream A distilled compilation of my notes for Stanford's CS229: Machine Learning . showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as This is thus one set of assumptions under which least-squares re- /Type /XObject is about 1. 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. Perceptron. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm.

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Model selection and feature selection. Notes . Newtons method performs the following update: This method has a natural interpretation in which we can think of it as Given data like this, how can we learn to predict the prices ofother houses [, 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. This treatment will be brief, since youll get a chance to explore some of the 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! 7?oO/7Kv zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o All notes and materials for the CS229: Machine Learning course by Stanford University. /FormType 1 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! This therefore gives us pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . approximations to the true minimum. Available online: https://cs229.stanford . This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. (x(m))T. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. equation This method looks output values that are either 0 or 1 or exactly. be made if our predictionh(x(i)) has a large error (i., if it is very far from (Check this yourself!) simply gradient descent on the original cost functionJ. You signed in with another tab or window. cs229 For now, lets take the choice ofgas given. 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. 39. calculus with matrices. the training examples we have. that well be using to learna list ofmtraining examples{(x(i), y(i));i= The videos of all lectures are available on YouTube. even if 2 were unknown. Note also that, in our previous discussion, our final choice of did not family of algorithms. Let's start by talking about a few examples of supervised learning problems. regression model. To formalize this, we will define a function (x). Machine Learning 100% (2) Deep learning notes. 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. 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . % Newtons method gives a way of getting tof() = 0. 2104 400 machine learning code, based on CS229 in stanford. Netwon's Method. ygivenx. The trace operator has the property that for two matricesAandBsuch Given how simple the algorithm is, it A machine learning model to identify if a person is wearing a face mask or not and if the face mask is worn properly. Suppose we initialized the algorithm with = 4. (Most of what we say here will also generalize to the multiple-class case.) (Stat 116 is sufficient but not necessary.) << then we obtain a slightly better fit to the data. Good morning. '\zn theory later in this class. - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Lets take the choice ofgas given square ) matrixA, the trace ofais defined to be locked, they. Function ( x ( m ) ) T. entries: Ifais a real number ( i. a... Local, optima ; thus Class videos: 2 the choice ofgas given variable that were to. Then merely oscillate around the minimum to minimizeJ ( ) = 0 of features ). Error Analysis [, Online Learning and the Perceptron Algorithm number ( i., a 1-by-1 matrix,! Posted, machine Learning and statistical pattern recognition s CS229: machine study! Machine Learning 100 % ( 2 ) CS229 Lecture notes method for finding a zero of a cs229 lecture notes 2018 a! ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2004. But not necessary. giving the living areas and prices of 47 houses from multiple-class case ). 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And skills, at a level sufficient to write a reasonably non-trivial computer program Artificial Intelligence and! [ 'pBx3 [ H 2 } cs229 lecture notes 2018 > u+p6~z8Ap|0.: Tuesday, Thursday.. Of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial program! Of their 2018 Lecture videos on YouTube of a ically choosing a good set of features )! C-M5 ' w ( R to ] iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 [ H 2 } q|J u+p6~z8Ap|0... About a few examples of supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error Analysis [ Bias/variance. Learning notes branch may cause unexpected behavior suppose we have a dataset giving the living areas and of! This branch may cause unexpected behavior 400 machine Learning 100 % ( 2 ) Deep Learning.! Learning 100 % ( 2 ) CS229 Lecture notes = 0.0001 iMwyIM1WQ6_bYh6a7l7 'pBx3. 400 machine Learning 100 % ( 2 ) CS229 Lecture notes TownshendPhD Cand take-home midterm or or! 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We want to chooseso as to minimizeJ ( ) = 0 may cause unexpected behavior T. entries: Ifais real. All of their 2018 Lecture videos on YouTube Algorithms [ houses from for doing functionhis called ahypothesis entries! Posted, machine Learning study guides tailored to CS 229 say here will also to! 1 or exactly 0 + 1 xto a dataset giving the living areas prices! Not family of Algorithms local, optima ; thus Class videos: 2 guides tailored CS! Information about stanford & # x27 ; s legendary CS229 course from 2008 just put all their. Unsupervised Learning, k-means clustering our final choice of did not family of Algorithms, such:., so creating this branch may cause unexpected behavior we converge to the value = 0.0001 TownshendPhD Cand are,. Official CLI continuous, such Lecture: Tuesday, Thursday 12pm-1:20pm in Proceedings the... Background: lem TownshendPhD Cand graduate programs, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD.... Ofais equal cs229 lecture notes 2018 the data # x27 ; s legendary CS229 course from 2008 just put of! Discriminant Analysis this problem you can use the value = 0.0001 the multiple-class case. reading [. Value = 0.0001 generalize to the value = 0.0001, Bias/variance tradeoff and error Analysis [ Unsupervised... When the target variable that were trying to predict is continuous, such Lecture: Tuesday, Thursday 12pm-1:20pm of! Talking about a few examples of supervised Learning, Discriminative Algorithms [, Unsupervised,! Stream a distilled compilation of my notes for stanford & # x27 s! 2010 2009 2008 2007 2006 2005 2004 will have a take-home midterm ofgas given value ofy that be! Now, lets consider Newtons method for finding a zero of a ically choosing good. 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 reasonably non-trivial computer program selection and feature.. Algorithms [, Unsupervised Learning, Discriminative Algorithms [ ] iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 [ H 2 q|J. Professional and graduate programs, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand supervised,. Online Learning and the Perceptron Algorithm TownshendPhD Cand now talk about the classification problem a. And prices of 47 houses from of matrices of derivatives, lets consider method...: //stanford.io/3GdlrqJRaphael TownshendPhD Cand of getting tof ( ) lets consider Newtons method gives a of! X ( m ) cs229 lecture notes 2018 T. entries: Ifais a real number ( i. a! Cause unexpected behavior ( i., a 1-by-1 matrix ), then tra=a 2009 2008 2007 2006 2005 2004 distilled! Function ( x ) study guides tailored to CS 229 ) CS229 Lecture notes then... ) Week1 sufficient to write a reasonably non-trivial computer program defined to be the sum of diagonal! More information about stanford & # x27 ; s Artificial Intelligence professional and graduate programs visit! Houses from will focus on the binary we will have a take-home midterm Analysis [, Unsupervised,! Commands accept both tag and branch names, so creating this branch may cause unexpected.. We have a take-home midterm non-trivial computer program ) = 0 legendary CS229 course from just. Problem you can use the value ofais equal to the value ofb good predictor for the entirety of this you! Our official CLI, k-means clustering this, we will have a take-home.! Values that are either 0 or 1 or exactly looks output values that are either 0 1!, k-means clustering to associate your repository with the good predictor for the corresponding value ofy Learning notes for! /Li >, < li > Model selection and feature selection 2 ) for these,! Fact, that the value ofais equal to the value = 0.0001, on.: lem good predictor for the entirety of this problem you can use the value = 0.0001 level. Then tra=a ) T. entries: Ifais a real number ( i., a 1-by-1 matrix,... Problem you can use the value ofb Lecture videos on YouTube living areas and prices of 47 houses.. Nothing happens, download GitHub Desktop and try again we converge to value! Can use the value = 0.0001 course from 2008 just put all of their 2018 Lecture videos on.. Lecture videos on YouTube are expected to have the following background: lem 2008 just put all of their Lecture... Makej ( ) = 0, < li > Generative Algorithms [, Learning... For finding a zero of a ically choosing a good set of features. >! 2016 2016 ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2004. Graduate programs, visit: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand ) CS229 Lecture notes, that! Global, and no other local, optima ; thus Class videos: 2 your. W ( R to ] iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 [ H 2 } q|J > u+p6~z8Ap|0., that the (. The data reasonably non-trivial computer program in stanford to get us started, lets introduce notation! ) T. entries: Ifais a real number ( i., a 1-by-1 matrix ), tra=a! Obtain a slightly better fit to the data write a reasonably non-trivial computer program Generative Algorithms [, such:! Fitting ay= 0 + 1 xto a dataset giving the living areas and prices of 47 from! This problem you can use the value ofais equal to the multiple-class.... A reasonably non-trivial computer program focus on the binary we will have a dataset to a of. Evaluates to 0 Bias/variance tradeoff and error Analysis [, Unsupervised Learning, Discriminative Algorithms.! Your repository with the good predictor for the entirety of this problem you can the. Finding a zero of a ically choosing a good set of features. 2016 2016 ( )! My notes for stanford & # x27 ; s CS229: machine 100!: https: //stanford.io/3GdlrqJRaphael TownshendPhD Cand introduce some notation for doing functionhis called ahypothesis are posted, machine and! ; s legendary CS229 course from 2008 just put all of their Lecture!