17CS73 Machine Learning Question With Answers Module 4
MODULE 4 – BAYESIAN LEARNING
1. Define the Bayesian theorem? What is the relevance and features of the Bayesian theorem? Explain the practical difficulties of the Bayesian theorem.
2. Define is Maximum a Posteriori (MAP) Maximum Likelihood (ML) Hypothesis. Derive the relation for hMAP and hML using the Bayesian theorem.
3. Consider a medical diagnosis problem in which there are two alternative hypotheses: 1. that the patient has a particular form of cancer (+) and 2. That the patient does not (-). A patient takes a lab test and the result comes back positive. The test returns a correct positive result in only 98% of the cases in which the disease is actually present, and a correct negative result in only 97% of the cases in which the disease is not present. Furthermore, .008 of the entire population have this cancer. Determine whether the patient has Cancer or not using the MAP hypothesis.
4. Explain Brute force Bayes Concept Learning.
5. What are Consistent Learners?
6. Discuss Maximum Likelihood and Least Square Error Hypothesis.
7. Describe Maximum Likelihood Hypothesis for predicting probabilities.
8. Explain the Gradient Search to Maximize Likelihood in a Neural Net.
9. Describe the concept of MDL. Obtain the equation for hMDL.
10. Explain Naïve Bayes Classifier with a simple Example.
11. What are Bayesian Belief nets? Where are they used?
12. Explain Bayesian belief network and conditional independence with an example.
13. Explain Gradient Ascent Training of Bayesian Networks.
14. Explain the concept of EM Algorithm. Discuss what are Gaussian Mixtures
Summary:
Her you find the Machine Learning Question With Answers Module 4 – BAYESIAN LEARNING.