商业机器学习代写 QBUS6850代写

QBUS6850 Machine Learning for Business

商业机器学习代写 Question 1 (5 marks) From a bias-variance tradeoff perspective explain why bagged ensembles require stronger models than boosted ensembles.

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 商业机器学习代写

(4 marks) In your own words, describe the cold start problem of recommendation systems and provide an example

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
(5 marks) Describe the operation of the Thompson Sampling Policy in the context of a Multi-Armed bandit model.

Question 8 商业机器学习代写

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:
1. (6 marks) Select the policy which performs the best, explain your reasoning
2. (4 marks) Can you conclude that one policy is superior to the other based on this run?

Question 9

(5 marks) Outline the steps of fitting an Adaboost model and match each step to the corresponding line/s in the code shown above.