2023 question paper
Machine Learning
25 questions
Q1(a). Define binary classification.20232m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Define binary classification.
Worked SolutionBinary classification is a supervised machine learning task in which an input is assigned to one of two possible classes or categories. For example, an email can be classified as either spam or not spam.
Q1(b). What is decision tree?20232m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →What is decision tree?
Q1(c). Define specific boundary.20232m
Q1(d). State Bayes theorem.20232m
Module 2: Statistical Decision Theory and Bayesian Learning
View this question on its own page →State Bayes theorem.
Q1(e). List the basic design issues to machine learning.20232m
Module 1: Introduction
View this question on its own page →List the basic design issues to machine learning.
Q1(f). Define precision and recall.20232m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Define precision and recall.
Q1(g). What is the essential difference between analytical and inductive learning methods?20232m
Module 1: Introduction
View this question on its own page →What is the essential difference between analytical and inductive learning methods?
Q1(h). What do you understand by noise in the data? How it affect the result?20232m
Module 1: Introduction
View this question on its own page →What do you understand by noise in the data? How it affect the result?
Q1(i). What is regression?20232m
Module 2: Statistical Decision Theory and Bayesian Learning
View this question on its own page →What is regression?
Q1(j). What is meant by Ensemble learning?20232m
Module 4: Hypothesis Testing and Ensemble Methods
View this question on its own page →What is meant by Ensemble learning?
Q2(a). “Machine learning cannot solve every problem”. Is this statement correct? Give justification to your answer with proper explanation.20237m
Module 1: Introduction
View this question on its own page →“Machine learning cannot solve every problem”. Is this statement correct? Give justification to your answer with proper explanation.
Q2(b). With an example, explain about classification and ranking.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →With an example, explain about classification and ranking.
Q3(a). What is binary classification? Explain scoring and ranking.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →What is binary classification? Explain scoring and ranking.
Q3(b). Differentiate between unsupervised and descriptive learning.20237m
Module 5: Clustering
View this question on its own page →Differentiate between unsupervised and descriptive learning.
Q4(a). What is the necessity of feature transformation in learning?20237m
Module 1: Introduction
View this question on its own page →What is the necessity of feature transformation in learning?
Q4(b). What is first order rule learning in machine learning? Explain with example.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →What is first order rule learning in machine learning? Explain with example.
Q5(a). Explain about principal component analysis in detail.20237m
Module 2: Statistical Decision Theory and Bayesian Learning
View this question on its own page →Explain about principal component analysis in detail.
Q5(b). Explain about the least-squares method.20237m
Module 2: Statistical Decision Theory and Bayesian Learning
View this question on its own page →Explain about the least-squares method.
Q6(a). Explain in detail about geometric model.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Explain in detail about geometric model.
Q6(b). Explain Q learning algorithm assuming deterministic rewards and actions.20237m
Module 6: Advanced Topics
View this question on its own page →Explain Q learning algorithm assuming deterministic rewards and actions.
Q7(a). Explain about grouping and Grading models.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Explain about grouping and Grading models.
Q7(b). Discuss about beyond conjunctive concepts using first-order logic.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Discuss about beyond conjunctive concepts using first-order logic.
Q8(a). Explain the principle of unsupervised and descriptive learning with respect to clustering.20237m
Module 5: Clustering
View this question on its own page →Explain the principle of unsupervised and descriptive learning with respect to clustering.
Q8(b). Discuss in detail about learning ordered Rule lists.20237m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Discuss in detail about learning ordered Rule lists.
Q9. Discuss how a multi layer network learns using a gradient descent algorithm.202314m
Module 3: Linear Classification and Advanced Methods
View this question on its own page →Discuss how a multi layer network learns using a gradient descent algorithm.