CS3491 Artificial Intelligence and Machine Learning Previous Year Question Papers - Anna University

UNIT I: PROBLEM SOLVING

Part A (2 Marks)

• Define Artificial Intelligence and list its applications.
• What are the components of a well-defined problem?
• Differentiate Informed and Uninformed search.
• What is a Heuristic function?

Part B (13/15 Marks)

• Explain A* Search algorithm with an example and discuss optimality.
• Compare BFS and DFS (time and space complexity).
• Explain Constraint Satisfaction Problems (Map Coloring / 8-Queens).
• Discuss Hill Climbing and its drawbacks (Local maxima, plateaus).

UNIT II: KNOWLEDGE REPRESENTATION AND REASONING

Part A (2 Marks)

• What is First Order Logic (FOL)?
• Define Unification and Resolution.
• What is a Hidden Markov Model (HMM)?
• State Bayes' Rule and its significance.

Part B (13/15 Marks)

• Explain Resolution in Predicate Logic with proof.
• Discuss Bayesian Networks for uncertain knowledge.
• Compare Forward and Backward Chaining.
• Explain Exact Inference using Variable Elimination.

UNIT III: MACHINE LEARNING FUNDAMENTALS

Part A (2 Marks)

• Differentiate Supervised and Unsupervised learning.
• What is Hypothesis Space?
• Define Overfitting and Underfitting.
• What is Regularization?

Part B (13/15 Marks)

• Explain Version Space and Candidate Elimination Algorithm.
• Discuss Inductive Learning and bias.
• Explain Linear Regression with Gradient Descent.
• Describe PAC Learning framework.

UNIT IV: SUPERVISED LEARNING

Part A (2 Marks)

• What is a Decision Tree?
• Role of Entropy in selecting root node.
• Define Support Vectors in SVM.
• What is Kernel Trick?

Part B (13/15 Marks)

• Explain ID3 algorithm using Information Gain.
• Discuss K-Nearest Neighbors (KNN) with example.
• Explain SVM (linear & non-linear cases).
• Describe Neural Networks and Backpropagation.

UNIT V: UNSUPERVISED LEARNING AND ADVANCED TOPICS

Part A (2 Marks)

• What is Clustering?
• Define Principal Component Analysis (PCA).
• What is a Centroid in K-Means?
• Differentiate Hard and Soft Clustering.

Part B (13/15 Marks)

• Explain K-Means algorithm with numerical example.
• Discuss Hierarchical Clustering methods.
• Explain PCA for dimensionality reduction.
• Describe Reinforcement Learning and Q-Learning.

Most Repeated / High-Weight Questions

A* search and problem solving, knowledge representation and reasoning, decision trees with entropy calculation, support vector machines, neural networks and backpropagation, K-means clustering, PCA for dimensionality reduction.