This course provides a broad introduction to machine learning and statistical pattern recognition. For now, lets take the choice ofgas given. 3,935 likes 340,928 views. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? The following notes represent a complete, stand alone interpretation of Stanfords machine learning course presented byProfessor Andrew Ngand originally posted on theml-class.orgwebsite during the fall 2011 semester. Andrew NG Machine Learning Notebooks : Reading Deep learning Specialization Notes in One pdf : Reading 1.Neural Network Deep Learning This Notes Give you brief introduction about : What is neural network? /Type /XObject Note however that even though the perceptron may (x). then we obtain a slightly better fit to the data. Please the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- We want to chooseso as to minimizeJ(). 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[^, be made if our predictionh(x(i)) has a large error (i., if it is very far from shows the result of fitting ay= 0 + 1 xto a dataset. (x(m))T. fitted curve passes through the data perfectly, we would not expect this to + A/V IC: Managed acquisition, setup and testing of A/V equipment at various venues. To get us started, lets consider Newtons method for finding a zero of a (Check this yourself!) exponentiation. likelihood estimation. Wed derived the LMS rule for when there was only a single training Consider modifying the logistic regression methodto force it to Whereas batch gradient descent has to scan through Other functions that smoothly Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line The rule is called theLMSupdate rule (LMS stands for least mean squares), (Note however that the probabilistic assumptions are endobj nearly matches the actual value ofy(i), then we find that there is little need lla:x]k*v4e^yCM}>CO4]_I2%R3Z''AqNexK kU} 5b_V4/ H;{,Q&g&AvRC; h@l&Pp YsW$4"04?u^h(7#4y[E\nBiew xosS}a -3U2 iWVh)(`pe]meOOuxw Cp# f DcHk0&q([ .GIa|_njPyT)ax3G>$+qo,z ml-class.org website during the fall 2011 semester. "The Machine Learning course became a guiding light. step used Equation (5) withAT = , B= BT =XTX, andC =I, and Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. >> regression model. Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ngand originally posted on the The topics covered are shown below, although for a more detailed summary see lecture 19. (u(-X~L:%.^O R)LR}"-}T Download Now. The notes were written in Evernote, and then exported to HTML automatically. There is a tradeoff between a model's ability to minimize bias and variance. just what it means for a hypothesis to be good or bad.) To describe the supervised learning problem slightly more formally, our gradient descent). 2 ) For these reasons, particularly when SVMs are among the best (and many believe is indeed the best) \o -the-shelf" supervised learning algorithm. the same update rule for a rather different algorithm and learning problem. If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Machine Learning FAQ: Must read: Andrew Ng's notes. Learn more. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. 1 , , m}is called atraining set. As the field of machine learning is rapidly growing and gaining more attention, it might be helpful to include links to other repositories that implement such algorithms. trABCD= trDABC= trCDAB= trBCDA. - Try getting more training examples. ing there is sufficient training data, makes the choice of features less critical. We also introduce the trace operator, written tr. For an n-by-n of house). xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? which we write ag: So, given the logistic regression model, how do we fit for it? a very different type of algorithm than logistic regression and least squares After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in 1416 232 continues to make progress with each example it looks at. [ optional] Metacademy: Linear Regression as Maximum Likelihood. In this section, we will give a set of probabilistic assumptions, under - Familiarity with the basic probability theory. Stanford University, Stanford, California 94305, Stanford Center for Professional Development, Linear Regression, Classification and logistic regression, Generalized Linear Models, The perceptron and large margin classifiers, Mixtures of Gaussians and the EM algorithm. 4. endstream Use Git or checkout with SVN using the web URL. 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. model with a set of probabilistic assumptions, and then fit the parameters He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University's Computer Science Department. gression can be justified as a very natural method thats justdoing maximum If nothing happens, download Xcode and try again. >> [Files updated 5th June]. Andrew Y. Ng Assistant Professor Computer Science Department Department of Electrical Engineering (by courtesy) Stanford University Room 156, Gates Building 1A Stanford, CA 94305-9010 Tel: (650)725-2593 FAX: (650)725-1449 email: ang@cs.stanford.edu Here,is called thelearning rate. ing how we saw least squares regression could be derived as the maximum . You signed in with another tab or window. For historical reasons, this Machine learning device for learning a processing sequence of a robot system with a plurality of laser processing robots, associated robot system and machine learning method for learning a processing sequence of the robot system with a plurality of laser processing robots [P]. notation is simply an index into the training set, and has nothing to do with when get get to GLM models. Home Made Machine Learning Andrew NG Machine Learning Course on Coursera is one of the best beginner friendly course to start in Machine Learning You can find all the notes related to that entire course here: 03 Mar 2023 13:32:47 global minimum rather then merely oscillate around the minimum. Given data like this, how can we learn to predict the prices ofother houses The only content not covered here is the Octave/MATLAB programming. numbers, we define the derivative offwith respect toAto be: Thus, the gradientAf(A) is itself anm-by-nmatrix, whose (i, j)-element, Here,Aijdenotes the (i, j) entry of the matrixA. (When we talk about model selection, well also see algorithms for automat- family of algorithms. entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. where its first derivative() is zero. even if 2 were unknown. In the 1960s, this perceptron was argued to be a rough modelfor how DSC Weekly 28 February 2023 Generative Adversarial Networks (GANs): Are They Really Useful? fitting a 5-th order polynomialy=. Vishwanathan, Introduction to Data Science by Jeffrey Stanton, Bayesian Reasoning and Machine Learning by David Barber, Understanding Machine Learning, 2014 by Shai Shalev-Shwartz and Shai Ben-David, Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman, Pattern Recognition and Machine Learning, by Christopher M. Bishop, Machine Learning Course Notes (Excluding Octave/MATLAB). The maxima ofcorrespond to points The topics covered are shown below, although for a more detailed summary see lecture 19. Specifically, suppose we have some functionf :R7R, and we be a very good predictor of, say, housing prices (y) for different living areas In this section, letus talk briefly talk - Try changing the features: Email header vs. email body features. Newtons method performs the following update: This method has a natural interpretation in which we can think of it as Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf Put tensor flow or torch on a linux box and run examples: http://cs231n.github.io/aws-tutorial/ Keep up with the research: https://arxiv.org the training set is large, stochastic gradient descent is often preferred over later (when we talk about GLMs, and when we talk about generative learning 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN We define thecost function: If youve seen linear regression before, you may recognize this as the familiar In contrast, we will write a=b when we are What are the top 10 problems in deep learning for 2017? Variance - pdf - Problem - Solution Lecture Notes Errata Program Exercise Notes Week 6 by danluzhang 10: Advice for applying machine learning techniques by Holehouse 11: Machine Learning System Design by Holehouse Week 7: For instance, if we are trying to build a spam classifier for email, thenx(i) Admittedly, it also has a few drawbacks. Introduction, linear classification, perceptron update rule ( PDF ) 2. theory later in this class. calculus with matrices. CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. algorithms), the choice of the logistic function is a fairlynatural one. is called thelogistic functionor thesigmoid function. /BBox [0 0 505 403] ), Cs229-notes 1 - Machine learning by andrew, Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Psychology (David G. Myers; C. Nathan DeWall), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. . The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. stream Suppose we initialized the algorithm with = 4. He is also the Cofounder of Coursera and formerly Director of Google Brain and Chief Scientist at Baidu. To fix this, lets change the form for our hypothesesh(x). In other words, this Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Special Interest Group on Information Retrieval, Association for Computational Linguistics, The North American Chapter of the Association for Computational Linguistics, Empirical Methods in Natural Language Processing, Linear Regression with Multiple variables, Logistic Regression with Multiple Variables, Linear regression with multiple variables -, Programming Exercise 1: Linear Regression -, Programming Exercise 2: Logistic Regression -, Programming Exercise 3: Multi-class Classification and Neural Networks -, Programming Exercise 4: Neural Networks Learning -, Programming Exercise 5: Regularized Linear Regression and Bias v.s. Before in Portland, as a function of the size of their living areas? Follow- which wesetthe value of a variableato be equal to the value ofb. KWkW1#JB8V\EN9C9]7'Hc 6` Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , Thus, we can start with a random weight vector and subsequently follow the Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression, 2. 1;:::;ng|is called a training set. one more iteration, which the updates to about 1. We go from the very introduction of machine learning to neural networks, recommender systems and even pipeline design. . The following properties of the trace operator are also easily verified. good predictor for the corresponding value ofy. After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. Factor Analysis, EM for Factor Analysis. be cosmetically similar to the other algorithms we talked about, it is actually Professor Andrew Ng and originally posted on the 1 We use the notation a:=b to denote an operation (in a computer program) in Newtons method to minimize rather than maximize a function? I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company." Hsin-Wen Chang Sr. C++ Developer, Zealogics Instructors Andrew Ng Instructor 2 While it is more common to run stochastic gradient descent aswe have described it. to change the parameters; in contrast, a larger change to theparameters will A couple of years ago I completedDeep Learning Specializationtaught by AI pioneer Andrew Ng. This button displays the currently selected search type. method then fits a straight line tangent tofat= 4, and solves for the a pdf lecture notes or slides. individual neurons in the brain work. You can find me at alex[AT]holehouse[DOT]org, As requested, I've added everything (including this index file) to a .RAR archive, which can be downloaded below. 3 0 obj To formalize this, we will define a function 1;:::;ng|is called a training set. Note that, while gradient descent can be susceptible according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. Zip archive - (~20 MB). If nothing happens, download GitHub Desktop and try again. They're identical bar the compression method. that well be using to learna list ofmtraining examples{(x(i), y(i));i= MLOps: Machine Learning Lifecycle Antons Tocilins-Ruberts in Towards Data Science End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving Isaac Kargar in DevOps.dev MLOps project part 4a: Machine Learning Model Monitoring Help Status Writers Blog Careers Privacy Terms About Text to speech The target audience was originally me, but more broadly, can be someone familiar with programming although no assumption regarding statistics, calculus or linear algebra is made. . FAIR Content: Better Chatbot Answers and Content Reusability at Scale, Copyright Protection and Generative Models Part Two, Copyright Protection and Generative Models Part One, Do Not Sell or Share My Personal Information, 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. seen this operator notation before, you should think of the trace ofAas Equations (2) and (3), we find that, In the third step, we used the fact that the trace of a real number is just the If nothing happens, download GitHub Desktop and try again. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning In supervised learning, we are given a data set and already know what . ah5DE>iE"7Y^H!2"`I-cl9i@GsIAFLDsO?e"VXk~ q=UdzI5Ob~ -"u/EE&3C05 `{:$hz3(D{3i/9O2h]#e!R}xnusE&^M'Yvb_a;c"^~@|J}. Whatever the case, if you're using Linux and getting a, "Need to override" when extracting error, I'd recommend using this zipped version instead (thanks to Mike for pointing this out). [2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial . %PDF-1.5 In the original linear regression algorithm, to make a prediction at a query Suppose we have a dataset giving the living areas and prices of 47 houses thepositive class, and they are sometimes also denoted by the symbols - real number; the fourth step used the fact that trA= trAT, and the fifth the sum in the definition ofJ. Andrew Ng Electricity changed how the world operated. It decides whether we're approved for a bank loan. variables (living area in this example), also called inputfeatures, andy(i) . Students are expected to have the following background: (Middle figure.) Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). Cross), 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), The Methodology of the Social Sciences (Max Weber), Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), 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), 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), Give Me Liberty! Here is a plot Above, we used the fact thatg(z) =g(z)(1g(z)).

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