机器学习代写 | COMP30027 Machine Learning

本次澳洲代写主要为机器学习相关的限时测试

Section A: Short Answer Questions [21 marks]

Answer each of the questions in this section as briefly as possible. Expect to answer each sub-
question in a couple of lines.
Question 1: Short Answer Questions [21 marks]

1. Explain the difference between “supervised” and “unsupervised” learning, and give an ex-
ample of a typical method for each. [2 marks]

2. What is the relationship between “instances” and “attributes” (also known as “features”)?
[1 mark]

3. SomeMachine Learningmethods rely on having “numerical” data. Give an example of how
we can use such a learner with “categorical” data. [1 mark]

4. When might the “softmax” function be used in a Machine Learning context? [1 mark]

5. What might it look like, if we had a Machine Learning system with low “model bias”, but
high “evaluation bias”? Why would such a situation be undesirable? [3 marks]

6. Underwhat circumstanceswould a “neural network” be equivalent to a “logistic regression”
model? [2 marks]

7. How is “active learning” similar to “self-training”, and how are they different? [2 marks]

8. How could a “linear regression” model be used for “classification”? Explain any important
data transformations in such a context. [2 marks]

9. We would like to evaluate a Machine Learning system on a development data set with 100
instances, where 80 instances are truly N and the rest are truly Y. Our system has labelled 20
of the truly N instances as Y, and 15 of the truly Y instances as N. Calculate the F-score, with
respect to the Y label. [2 marks]

10. For what kind of “structured classification” task would one typically use a “hidden Markov
model”? What assumption(s) do we make about the accompanying data when using such a
model? [3 marks]

11. Use a diagram to show how “hierarchical clustering” is different to “partitional clustering”.
Which does an “Expectation–Maximisation” method typically produce? [2 marks]

 


程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


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