Machine Learning Techniques (KCS055) - AKTU Question Paper 2022-23
B.Tech · Semester 5 · Free PDF Download
This is the official AKTU Machine Learning Techniques Previous Year Question Paper for B.Tech Semester 5, academic session 2022-23. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.
Course:B.Tech
Semester:Semester 5
Session:2022-23
University:AKTU / UPTU
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Questions Asked in 2022-23
Machine Learning Techniques (KCS055) — extracted question text
Section AAttempt all questions in brief. 2*10 = 20
- aDiscuss model representation of artificial neuron
- bExplain general to specific ordering hypothesis in concept learning
- cDiscuss support vectors in SVM
- dCompare Artificial Intelligence and Machine Learning
- eDiscuss reinforcement learning
- fIllustrate the advantages of instance-based learning techni ques over other machine learning techniques
- gDifferentiate between Gradient Descent and Stochastic Gradi ent Descent
- hCompare ANN and Bayesian network. (i) Illustrate Markov decision model. (j) Differentiate between Q learning and Deep Learning
Section BAttempt any three of the following: 10*3 = 30
- aExplain supervised and unsupervised learning techniques
- bDiscuss linear regression and logistic regression in detail
- cDescribe the following concepts in decision tree in detail: (i) Avoiding overfitting in decision tree. (ii) Incorporating continuous valued attributes
- dExplain various types of activation functions with examples
- eIllustrate the pro cess of Q-learning and discuss the following terms: (i) Q-values or action value (ii) Rewards and Episode (iii) Temporal difference or TD update
Section CAttempt any one part of the following: 10*1 = 10
- aIllustrate the various areas in which you can apply machine learning
- bCompare regression, classification and clustering in machin e learning along with suitable real- life applications
- aDiscuss the role of Bayes theorem in machine learning. How naive Bayes algorithm is different from Bayes theorem?
- bExplain hyperplane (decision boundary) in SVM. Categorize v arious popular kernels associated with SVM
- aDemonstrate K-Nearest Neighbors algorithm for classificatio n with the help of an example
- bExplain Instance based learning. Compare locally weighted r egression and radial basis function networks
- aExplain the different layers used in convolutional neural n etwork with suitable examples
- bIllustrate backpropagation algorithm by assuming the traini ng rules for output unit weights and Hidden Unit weights
- aExplain various types of reinforcement learning techniques with suitable examples
- bHow to Identify the reproduction cycle of genetic algorithm ? Explain with suitable example
Question text is extracted from the official AKTU question paper PDF above. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.
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