MACHINE LEARNING
106403Module 2: Linear Models and Bayesian Learning
Q1c. What makes the difference between good decisions and bad decisions? (i) A good decision is based on logic (ii) A good decision considers all available data (iii) A good decision considers all alternatives (iv) A good decision applies quantitative approaches20222m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →What makes the difference between good decisions and bad decisions?
(i) A good decision is based on logic
(ii) A good decision considers all available data
(iii) A good decision considers all alternatives
(iv) A good decision applies quantitative approachesQ1d. Where does the Bayes rule can be used? (i) Solving queries (ii) Increasing complexity (iii) Decreasing complexity (iv) Answering probabilistic query20222m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →Where does the Bayes rule can be used?
(i) Solving queries
(ii) Increasing complexity
(iii) Decreasing complexity
(iv) Answering probabilistic queryQ1e. Which of the following steps/assumptions in regression modeling impacts the trade-off between under-fitting and over-fitting the most? (i) The polynomial degree (ii) Whether we learn the weights by matrix inversion or gradient descent (iii) The use of a constant-term (iv) None of the above20222m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →Which of the following steps/assumptions in regression modeling impacts the trade-off between under-fitting and over-fitting the most?
(i) The polynomial degree
(ii) Whether we learn the weights by matrix inversion or gradient descent
(iii) The use of a constant-term
(iv) None of the aboveQ3a. What is Linear Regression? Explain in detail using example and list all the assumption to be met before starting with linear regression.20237m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →What is Linear Regression? Explain in detail using example and list all the assumption to be met before starting with linear regression.
Q3b. What is regression analysis? What are the differences between linear regression and ridge regression?20227m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →What is regression analysis? What are the differences between linear regression and ridge regression?
Q5b. A patient takes a lab test and the result comes back positive. It is known that the test returns a correct positive result in only 98% of the cases and a correct negative result in only 97% of the cases. Furthermore, only 0.008 of the entire population has this disease. (i) What is the probability that this patient has cancer? (ii) What is the probability that he does not have cancer? (iii) What is the diagnosis?20237m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →A patient takes a lab test and the result comes back positive. It is known that the test returns a correct positive result in only 98% of the cases and a correct negative result in only 97% of the cases. Furthermore, only 0.008 of the entire population has this disease.
(i) What is the probability that this patient has cancer?
(ii) What is the probability that he does not have cancer?
(iii) What is the diagnosis?Q9a. Write short notes on the following: (a) Partial least square20227m
Module 2: Linear Models and Bayesian Learning
View this question on its own page →Write short notes on the following:
(a) Partial least square