Problem 6.1 CS5487 Problem Set 6 Bayes Decision Theory Antoni Chan Department of Computer Science City University of Hong Kong Bayes Decision Theory BDR with unbalanced loss function Consider a two-class problem with y 2 {0, 1} and measurement x, with associated prior distribution p(y) and class-conditional densities p(x|y). (a) Consider the loss-function: 8>:0, g(x) […]
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Problem 4.1 CS5487 Problem Set 4 Mixture models and the EM algorithm Antoni Chan Department of Computer Science City University of Hong Kong Mixture models Mean and variance of a mixture model Consider a d-dimensional vector r.v. x with a mixture distribution given by cov(x) = ⇡j(⌃j + μjμTj ) E[x]E[x]T . (4.2) j=1 XK
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Problem 5.1 CS5487 Problem Set 5 Non-parametric estimation and clustering Antoni Chan Department of Computer Science City University of Hong Kong Kernel density estimators Bias and variance of the kernel density estimator In this problem, we will derive the bias and variance of the kernel density estimator. Let X = {x1, · · · ,
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CS.542 Machine Learning, Fall 2020 1. Math and Probability Basics Q1.1 Definitions [a] Give the definition of an orthogonal matrix. [b] Give the definition of an eigenvector and eigenvalue. [c] How is the probability density function different from the cumulative probability distribution? [d] What is a ‘singular’ matrix? [e] Give the definition of Baye’s Rule.
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Machine Learning Midterm Practice Problems Some of these sample problems had been used in past exams and are provided for practice, in addition to the homework problems which you should also review. A typical exam would have around 5 questions worth a total of 100 points. The exam
程序代写代做代考 chain graph algorithm GMM CS.542 Machine Learning, Fall 2019, Prof. Saenko Read More »
CS.542 Machine Learning, Fall 2020 1. Math and Probability Basics Q1.1 Definitions [a] Give the definition of an orthogonal matrix. [b] Give the definition of an eigenvector and eigenvalue. [c] How is the probability density function different from the cumulative probability distribution? [d] What is a ‘singular’ matrix? [e] Give the definition of Baye’s Rule.
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• Section A. Linear Regression (10 points) 1. (1 point) Linear regression: (A) is a form of unsupervised learning (B) is a form of supervised learning (C) can be used in classification, where the loss function does not di↵erentiate between “easy” and “hard” training samples. (D) can be used in classification but is not robust
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CS.542 Machine Learning, Fall 2019, Prof. Saenko Additional Exam Practice Problems Note: Some of these sample problems had been used in past exams and are provided for practice in addition to the midterm practice and homework problems, which you should also review. A typical exam would have around 5 questions. The exam is closed book,
CS.542 Machine Learning, Fall 2019, Prof. Saenko Machine Learning Midterm Practice Problems Some of these sample problems had been used in past exams and are provided for practice, in addition to the homework problems which you should also review. A typical exam would have around 5 questions worth a total of 100 points. The exam
程序代写代做代考 GMM chain graph algorithm CS.542 Machine Learning, Fall 2019, Prof. Saenko Read More »
CS.542 Machine Learning, Fall 2019, Prof. Saenko Machine Learning Midterm Practice Problems Some of these sample problems had been used in past exams and are provided for practice, in addition to the homework problems which you should also review. A typical exam would have around 5 questions worth a total of 100 points. The exam
程序代写代做代考 GMM chain graph algorithm CS.542 Machine Learning, Fall 2019, Prof. Saenko Read More »