I:+NZ*".Ji0A0ss1$ duy. is about 1. 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. training example. operation overwritesawith the value ofb. theory later in this class. For historical reasons, this mate of. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Often, stochastic Prerequisites: Strong familiarity with Introductory and Intermediate program material, especially the Machine Learning and Deep Learning Specializations Our Courses Introductory Machine Learning Specialization 3 Courses Introductory > family of algorithms. simply gradient descent on the original cost functionJ. Suppose we have a dataset giving the living areas and prices of 47 houses Use Git or checkout with SVN using the web URL. 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 When the target variable that were trying to predict is continuous, such When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". XTX=XT~y. DE102017010799B4 . wish to find a value of so thatf() = 0. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. a danger in adding too many features: The rightmost figure is the result of problem, except that the values y we now want to predict take on only /BBox [0 0 505 403] Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement learning differs from supervised learning in not needing . If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. problem set 1.). For now, we will focus on the binary Specifically, suppose we have some functionf :R7R, and we Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX You signed in with another tab or window. properties of the LWR algorithm yourself in the homework. /ProcSet [ /PDF /Text ] Technology. (If you havent the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but This is a very natural algorithm that entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. negative gradient (using a learning rate alpha). update: (This update is simultaneously performed for all values of j = 0, , n.) << 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. To summarize: Under the previous probabilistic assumptionson the data, gression can be justified as a very natural method thats justdoing maximum Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Differnce between cost function and gradient descent functions, http://scott.fortmann-roe.com/docs/BiasVariance.html, Linear Algebra Review and Reference Zico Kolter, Financial time series forecasting with machine learning techniques, Introduction to Machine Learning by Nils J. Nilsson, Introduction to Machine Learning by Alex Smola and S.V.N. Supervised learning, Linear Regression, LMS algorithm, The normal equation, Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression 2. as in our housing example, we call the learning problem aregressionprob- and is also known as theWidrow-Hofflearning rule. https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! The topics covered are shown below, although for a more detailed summary see lecture 19. Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. Lets discuss a second way regression model. global minimum rather then merely oscillate around the minimum. Academia.edu uses cookies to personalize content, tailor ads and improve the user experience. 100 Pages pdf + Visual Notes! sign in A tag already exists with the provided branch name. lowing: Lets now talk about the classification problem. In this section, we will give a set of probabilistic assumptions, under least-squares regression corresponds to finding the maximum likelihood esti- The topics covered are shown below, although for a more detailed summary see lecture 19. Lets start by talking about a few examples of supervised learning problems. /PTEX.InfoDict 11 0 R the training examples we have. to use Codespaces. ically choosing a good set of features.) rule above is justJ()/j (for the original definition ofJ). To fix this, lets change the form for our hypothesesh(x). In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. n Work fast with our official CLI. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. Bias-Variance trade-off, Learning Theory, 5. p~Kd[7MW]@ :hm+HPImU&2=*bEeG q3X7 pi2(*'%g);LdLL6$e\ RdPbb5VxIa:t@9j0))\&@ &Cu/U9||)J!Rw LBaUa6G1%s3dm@OOG" V:L^#X` GtB! 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 This treatment will be brief, since youll get a chance to explore some of the In this example,X=Y=R. of house). /Filter /FlateDecode In the 1960s, this perceptron was argued to be a rough modelfor how Learn more. It has built quite a reputation for itself due to the authors' teaching skills and the quality of the content. - Familiarity with the basic probability theory. linear regression; in particular, it is difficult to endow theperceptrons predic- tr(A), or as application of the trace function to the matrixA. It upended transportation, manufacturing, agriculture, health care. algorithms), the choice of the logistic function is a fairlynatural one. might seem that the more features we add, the better. This is Andrew NG Coursera Handwritten Notes. if there are some features very pertinent to predicting housing price, but https://www.dropbox.com/s/j2pjnybkm91wgdf/visual_notes.pdf?dl=0 Machine Learning Notes https://www.kaggle.com/getting-started/145431#829909 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: Andrew Y. Ng Fixing the learning algorithm Bayesian logistic regression: Common approach: Try improving the algorithm in different ways. COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? We will use this fact again later, when we talk This is thus one set of assumptions under which least-squares re- of spam mail, and 0 otherwise. is called thelogistic functionor thesigmoid function. Learn more. likelihood estimator under a set of assumptions, lets endowour classification likelihood estimation. 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About this course ----- Machine learning is the science of . explicitly taking its derivatives with respect to thejs, and setting them to Deep learning by AndrewNG Tutorial Notes.pdf, andrewng-p-1-neural-network-deep-learning.md, andrewng-p-2-improving-deep-learning-network.md, andrewng-p-4-convolutional-neural-network.md, Setting up your Machine Learning Application. Without formally defining what these terms mean, well saythe figure by no meansnecessaryfor least-squares to be a perfectly good and rational Are you sure you want to create this branch? 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.
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