Important RGPV Question
AL-405 (Machine Learning)
IV Sem, AI
UNIT 1- INTRODUCTION TO MACHINE LEARNING, SCOPE AND LIMITATIONS
Q.1) List and explain perspectives and issues in/of machine learning.
RGPV June 2022, 2023
Q.2) Distinguish between supervised learning and unsupervised learning with examples.
RGPV June 2023
Q.3) Explain hypothesis testing with examples.
RGPV June 2023
Q.4) Write short note on cross-validation.
RGPV June 2023
Q.5) Write and explain different types of machine learning.
Q.6) Distinguish between supervised machine learning and reinforce- ment learning.
RGPV June 2022
Q.7) What are the basic design issues and approaches to machine learning?
RGPV May 2022 (VI-Sem)
Q.8) Explain the concept of hypothesis space and inductive bias in brief
RGPV June 2022
Q.9) Describe issues in machine learning.
RGPV June 2022
Q.10) Explain in detail principal component analysis for dimension reduction.
RGPV May 2022 (VI-Sem)
Q.11) Explain the machine learning concept by taking an example. Describe the perspective and issues in machine learning.
RGPV Dec 2020 (VI-Sem)
Q.12) Explain cross-validation and the types of cross-validation.
Q.13) Describe how principle component analysis is carried out to reduce the dimensionality of data sets.
RGPV Dec 2020 (VI-Sem)
Q.14) What are data reduction strategies? Explain.
Q.15) Why sampling is used as a data reduction technique? What is the advantage of sampling for data reduction?
Q.16) Describe how principle component analysis is carried out to reduce the dimensionality of data sets.
RGPV Dec 2020 (VI-Sem)
Q.17) Differences between supervised and unsupervised learning.
RGPV May 2019 (VIII-Sem)
Q.18) Explain in detail about multiple hypothesis testing.
RGPV May 2019 (VI-Sem)
Q.19) Write short note on multiple hypothesis testing.
RGPV Nov 2018 (VI-Sem)
Q.20) What is feature extraction? Discuss advantages of the feature extraction process in data analytics.
RGPV Nov 2018
UNIT 2- NEURAL NETWORKS – FROM BIOLOGY TO SIMULATION
Q.1) Define backpropagation and write propagation with examples. an algorithm for back propagation.
RGPV June 2023
Q.2) What is a perceptron? Explain the working of a perceptron with neat diagram.
RGPV June 2023
Q.3) Write short note on training and validation.
RGPV June 2023
Q.10. Explain parallel processing perception learning in neural networks.
RGPV June 2022
Q.4) Explain the multilayer perceptron model with a neat diagram.
RGPV June 2022
Q.5) Describe the characteristics of backpropagation algorithm.
RGPV June 2022
Q.6) Define artificial neural network. Explain the biological learning system.
RGPV June 2022
Q.7) Explain the exponential linear unit (ELU) in detail.
Q.8) Write a short note on neural networks.
Q.9) Explain in detail activation function.
Q.10) Explain training and testing of data with example.
RGPV May 2019
Q.11) Write short note on applications of neural network.
RGPV June 2010, 2011, May 2018
Q.12) Discuss over-fitting and under-fitting with the help of a suitable example.
RGPV Nov 2018
Q.13) Discuss perceptron training algorithm.
RGPV Dec 2016
Q.14) What is the role of activation function in artificial neural network!
RGPV June 2016 [IT]
Q.15) Give the characteristics, benefits and uses of neural network.
RGPV June 2015
Q.16) Explain the neural network architectures.
RGPV June 2014
Q.17) Give any three applications of neural network.
RGPV June 2014
Q.18) Briefly explain the applications of neural networks. What are the drawbacks of neural net?
RGPV Dec 2009
UNIT 3- SUPERVISED LEARNING TECHNIQUES
Q.1) Define decision tree. Explain decision tree algorithm with example
RGPV June 2023
Q.2) . Explain how support vector machine can be used for classification linearly separable data.
RGPV June 2023
Q.3) Differentiate between linear and non-linear SVM classifiers.
Q.4) What is linear regression? Explain in detail with example and list all the assumptions to be met before starting with linear regression.
RGPV June 2023
Q.5) Differentiate between regression and classification.
RGPV June 2023
Q.6) Define decision tree learning. List and explain appropriate problems in decision tree learning.
RGPV June 2022
Q.7) List and explain the issues in decision tree learning.
RGPV June 2022
Q.8) What is the goal of support vector machine (SVM) ? How to compute the margin ?
RGPV June 2022
Q.9) Describe logistic regression.
RGPV June 2022
Q.10) Compare the advantages/disadvantages of eager classification and lazy classification.
Q.11) Discuss key idea of the support vector machines (SVMs).
RGPV Nov 2018
Q.12) Explain the working of logistic regression algorithm with the help of a suitable example.
RGPV Nov 2018
Q.13) Explain naive Bayes classifier with example.
RGPV May 2019 (VIII-Sem)
Q.14) Describe margin and hard support vector machines (SVM).
Q.15) How to build a rule based classifier by extracting IF-THEN rules from a decision tree?
Q.16) Describe the CLOUD algorithm and discuss its advantages over
SLIQ.
Q.17) Explain briefly the model of Naive Bayes classifier.
UNIT 4- UNSUPERVISED LEARNING, CLUSTERING -K-MEANS
Q.1) Write short note on cluster.
RGPV June 2023
Q.2) What is Gaussian mixture density estimation with example ?
RGPV June 2023
Q.3) Explain expectation maximization algorithm.
RGPV June 2023
Q.4) Explain the term EM (Expectation-Maximization) in detail.
Q.5) What is the importance of similarity metric in clustering?
Q.6) Explain the methods used to determine the number of clusters.
Q.7) Distinguish between global and local normalization in STIRR.
Q.8) Illustrate k-means clustering algorithm.
RGPV June 2022
Q.9) Describe the requirements of clustering algorithms.
RGPV June 2022
Q.10) How efficient is the k-medoids algorithm on large data set?
Q.11) Differentiate between hierarchical clustering and partition clustering.
UNIT 5- DESIGN AND ANALYSIS OF MACHINE LEARNING EXPERIMENTS
Q.1) Explain resample methods of machine learning.
RGPV June 2023
Q.2) Write some techniques for comparing machine learning models across multiple datasets.
Q.3) Write short note on factors.
RGPV June 2023
Q.4) What are the guidelines for machine learning experiments? Explain.
Q.5) What factors are considered when comparing multiple algorithms in machine learning?
Q.6) Explain the concept of measuring classifier performance.
RGPV June 2022
Q.7) Explain resampling methods in machine learning.
RGPV June 2022
Q.8) Explain hypothesis testing.
Q.9) Discuss about the three basic principles of experimental design.
— Best of Luck for Exam —
