Chat with us, powered by LiveChat Foundations of Machine Learning:Assignment 1Instru | Writedemy

Foundations of Machine Learning:Assignment 1Instru

Foundations of Machine Learning:Assignment 1Instru

Foundations of Machine Learning:Assignment 1Instructor: Prof. Xiangyang JiDue: 2020.03.22Problem 130pts. (Exercise 2.3 in Foundations of Machine Learning) Concentric circles. Let X = R2 and consider theset of concepts of the form c = f(x; y) : x2 + y2  r2g for some real number r. Show that this class can be(; )-PAC-learned from training data of size m  (1=)log(1=).Problem 235pts. (Exercise 2.4 in Foundations of Machine Learning) Non-Concentric circles. Let X = R2 and considerthe set of concepts of the form c = fx 2 R2 : jjx 􀀀 x0jj  rg for some point x0 2 R2 and real number r.Gertrude, an aspiring machine learning researcher, attempts to show that this class of concepts may be(; )-PAC-learned with sample complexity m  (3=)log(3=), but she is having trouble with her proof. Heridea is that the learning algorithm would select the smallest circle consistent with the training data. Shehas drawn three regions r1; r2; r3 around the edge of concept c, with each region having probability =3 (seeFigure 1). She wants to argue that if the generalization error is greater than or equal to , then one of theseregions must have been missed by the training data, and hence this event will occur with probability at most.Can you tell Gertrude if her approach works? If the answer is not, can you modify this approach by drawingmore than three regions as above?1Assignment 1 Problem 2Figure 1: Gertrude’s regions r1; r2; r3.Problem 335pts. (Exercise 2.9 in Foundations of Machine Learning) Consistent hypotheses. In this chapter, weshowed that for a nite hypothesis set H, a consistent learning algorithm A is a PAC-learning algorithm.Here, we consider a converse question. Let Z be a nite set of m labeled points. Suppose that you are givena PAC-learning algorithm A. Show that you can use A and a nite training sample S to nd a hypothesish 2 H that is consistent with Z, with high probability.Note: S represents the training dataset, and Z represents the set of all possible examples in this problem.You should focus on proving S is nite and h is consistent on Z.Problem 4(Challenge, 20pts) Let H be set of all half-planes in R2. We assume that the underlying concept h 2 H.Try to prove that this class can be (; )-PAC-learnable.Page 2 of 2

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