机器学习quiz代做-machine learning代写

Quiz: Final Sample Exam

机器学习quiz代做 Below I have provided the instructions that accompany the actual exam so that you can prepare for the real exam.

Quiz Instructions

This is a sample exam that represents the types and difficulty of questions to be expected in the exam. It does not represent the length of the real exam.

Below I have provided the instructions that accompany the actual exam so that you can prepare for the real exam.

Final Exam Instructions:

The final exam duration is 130 minutes (this includes 10 minutes of reading time).
This is a restricted open book exam. You are allowed:

1.1x A4 one sided handwritten notes. 机器学习quiz代做

2.Multiple sheets of blank scratch paper and a pen/pencil.

3.A handheld calculator. All other electronic devices are not permitted.

3.Please read each questions carefully and then do any necessary derivation/calculation and answer each question.

Marks are not equal for each question.
Please type your answer with your own words in the online editable answer box.
Necessary formulas are provided with the question.

Question 1

(5 marks) From a bias-variance tradeoff perspective explain why bagged ensembles require stronger models than boosted ensembles.

Question 2

(4 marks) Identify and describe two reasons why computer vision is challenging.

Question 3

(5 marks) In the context of matrix factorisation, identify and outline a technique to estimate the factor matrices W and H.

Question 4 机器学习quiz代做

(4 marks) In your own words, describe the cold start problem of recommendation systems and provide an example23/11/2021, 10:29

Question 5

(4 marks) In your own words, describe the purpose of bias units in a neural network

Question 6

(6 marks) Name an example of a recommendation system that you have personally experienced and describe how you the recommendation system can be posed as a Multi-Armed Bandits problem.

Question 7 机器学习quiz代做

(5 marks) Describe the operation of the Thompson Sampling Policy in the context of a Multi-Armed bandit model.23/11/2021, 10:29

Question 8

Suppose you are evaluating policies for the MAB environment with binary rewards.

Each bandit is Bernoulli distributed with the following parameters:

You have designed two policies and the action log is shown below:

Answer the following:

(6 marks) Select the policy which performs the best, explain your reasoning
(4 marks) Can you conclude that one policy is superior to the other based on this run?

Question 9