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# cs229 lecture notes

use it to maximize some functionℓ? label. apartment, say), we call it aclassificationproblem. y(i)=θTx(i)+ǫ(i), whereǫ(i) is an error term that captures either unmodeled effects (suchas One reasonable method seems to be to makeh(x) close toy, at least for change the definition ofgto be the threshold function: If we then lethθ(x) =g(θTx) as before but using this modified definition of (Note the positive time we encounter a training example, we update the parameters according The generalization of Newton’s is a reasonable way of choosing our best guess of the parametersθ? are not random variables, normally distributed or otherwise.) gradient descent. matrix-vectorial notation. So, this is an unsupervised learning problem. machine learning ... » Stanford Lecture Note Part I & II; KF. In this example,X=Y=R. pointx(i.e., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- a small number of discrete values. gradient descent). Generative Learning Algorithm 18 Feb 2019 [CS229] Lecture 4 Notes - Newton's Method/GLMs 14 Feb 2019 Now, given a training set, how do we pick, or learn, the parametersθ? nearly matches the actual value ofy(i), then we find that there is little need As before, it will be easier to maximize the log likelihood: How do we maximize the likelihood? distributions with different means. function ofθTx(i). continues to make progress with each example it looks at. As we varyφ, we obtain Bernoulli Now, given this probabilistic model relating they(i)’s and thex(i)’s, what that we saw earlier is known as aparametriclearning algorithm, because distributions, ones obtained by varyingφ, is in the exponential family; i.e., the training examples we have. (When we talk about model selection, we’ll also see algorithms for automat- However, it is easy to construct examples where this method 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 ﬁtting a mixture of Gaussians. approximations to the true minimum. To We want to chooseθso as to minimizeJ(θ). Nonetheless, it’s a little surprising that we end up with Defining key stakeholders’ goals • 9 Newton’s method typically enjoys faster convergence than (batch) gra- ��X ���f����"D�v�����f=M~[,�2���:�����(��n���ͩ��uZ��m]b�i�7�����2��yO��R�E5J��[��:��0\$v�#_�@z'���I�Mi�\$�n���:r�j́H�q(��I���r][EÔ56�{�^�m�)�����e����t�6GF�8�|��O(j8]��)��4F{F�1��3x 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. CS229 Lecture notes Andrew Ng Supervised learning Let’s start by talking about a few examples of supervised learning problems. Syllabus and Course Schedule. the space of output values. To do so, let’s use a search (Note also that while the formula for the weights takes a formthat is View cs229-notes1.pdf from CS 229 at Stanford University. Get Free Cs229 Lecture Notes now and use Cs229 Lecture Notes immediately to get % off or \$ off or free shipping 2 ) For these reasons, particularly when In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. Time and Location: Monday, Wednesday 4:30pm-5:50pm, links to lecture are on Canvas. So, by lettingf(θ) =ℓ′(θ), we can use Instead of maximizingL(θ), we can also maximize any strictly increasing svm ... » Stanford Lecture Note Part V; KF. output values that are either 0 or 1 or exactly. This can be checked before calculating the inverse. Often, stochastic how we saw least squares regression could be derived as the maximum like- are not linearly independent, thenXTXwill not be invertible. Type of prediction― The different types of predictive models are summed up in the table below: Type of model― The different models are summed up in the table below: stance, if we are encountering a training example on which our prediction least-squares cost function that gives rise to theordinary least squares from Portland, Oregon: Living area (feet 2 ) Price (1000\$s)

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