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# ml作业代写 | 机器学习作业代写| CSE 6363 – Machine Learning

2019-11-27 20:40 星期三 所属： it代写 浏览：58

ml作业代写 Inclass we covered the derivation of basic learning algorithms to derive a model

## ml作业代写 案例CSE 6363 – Machine Learning

Homework 1- Spring 2019

Due Date: Feb. 8 2019, 11:59 pm

MLE and MAP

1. Inclass we covered the derivation of basic learning algorithms to derive a model for a coin flip  Consider a similar problems where we monitor the time of the occurrence of a severe computer failure (which requires a system reboot) and which occurs according to a Poisson process (i.e. it is equally likely to happen at any point in time with an arrival rate of λ ). For a Poisson process the probability of the first event to occur at time x after a restart is described by an exponential distribution:

pλ(x) = λe

ml作业代写 项目介绍We are assuming here that the different data points we measured are independent, i.e. nothing changes between reboots.

1. Derivethe performance function and the optimization result for analytic MLE optimization for a model learning algorithm that returns the MLE for the parameter λ of the model given a data set D = {k1, …kn}. Make sure you show your
2. Apply the learning algorithm from a) to the followingdataset:

D = {1.5, 3, 2.5, 2.75, 2.9, 3} .

1. Derive the optimization for a MAP approach using the conjugate prior, the Gamma

The Gamma distribution is:

pα,β

(λ) =

βα

Γ(α)

λα−1

βλ

ml作业代写 Note that α and β are constants and that there still is only one parameter, λ, to be learned. Show your derivation and the result for the data in part b) and values for α and β of 5 and 10, respectively.

### K Nearest Neighbor

1. Consider the problem where we want to predict the gender of a person from a set of input parameters, namely height, weight, and age. Assume our training data is given asfollows:

 D = { ((170, 57, 32), W ), ((192, 95, 28), M ), ((150, 45, 30), W ), ((170, 65, 29), M ), ((175, 78, 35), M ), ((185, 90, 32), M ), ((170, 65, 28), W ), ((155, 48, 31), W ), ((160, 55, 30), W ), ((182, 80, 30), M ), ((175, 69, 28), W ), ((180, 80, 27), M ), ((160, 50, 31), W ), ((175, 72, 30), M ), }

1. Using Cartesian distance as the similarity ml作业代写 measurements show the results of the gender prediction forthe following data items for values of K of 1, 3, and 5. Include the intermedia steps (i.e. distance calculation, neighbor selection, prediction).

(155, 40, 35), (170, 70, 32), (175, 70, 35), (180, 90, 20)

1. Implement the KNN algorithm for this problem. ml作业代写  Your implementation should work with different training data sets and allow to input a data point for the
2. Repeat the prediction using KNN when the age data is removed. Try to determine (using multiple target values) which data gives you better predictions. Show your intermediate

### Gaussian Na¨ıve Bayes Classification

1. Using the data from Problem 2, build a Gaussian Na¨ıve  Bayes classifier for this problem.  For this you  haveto learn Gaussian distribution parameters for each input data feature, e. for p(height|W ), p(height|M ), p(weight|W ), p(weight|M ), p(age|W ), p(age|M ).

1. Learn/derive the parameters for the Gaussian Na¨ıve Bayes Classifier and apply them to the same target as in problem 2b). Show your intermediate
2. Implement the Gaussian Na¨ıve Bayes Classifier for this
3. Repeat the experiment in part 2c) with the Gaussian Na¨ıve Bayes
4. Compare the results of the two classifiers and discuss reasons why one might perform better than the
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