Define Machine Learning & Human Learning. Give at least two differences between machine learning and human learning.
State the different techniques for data pre-processing? Explain, in brief, dimensionality reduction and feature selection.
What is under-fitting in context of machine learning models? State its major causes.
Can all problems be solved using machine learning? Explain your response in detail.
How would you evaluate the success of an unsupervised learning model? What are the most popular measures of performance for an unsupervised learning model? Explain any two with example.
What is feature selection in context of machine learning? Why is it needed? List the different approaches of feature selection? Explain any one with example.
What are the different types of supervised learning? Explain them with a sample application in each area.
What are the different types of unsupervised learning? Explain them with a sample application in each area.
What are Bayesian Belief networks? Where are they used? Can they solve all types of problems?
What is decision tree? What are the different types of nodes? Explain with example.
How does the apriori principle help in reducing the calculation overhead for a market basket analysis? Explain with an example.
Explain the learning process of an ANN. Explain, with example, the challenge in assigning synaptic weights for the interconnection between neurons? How can this challenge be addressed?
Describe the structure of an artificial neuron. How is it similar to a biological neuron? Explain its main components?
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Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Machine Learning (ML) (Winter 2025, B.E. · Computer Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
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