21AI51 Dayananda Sagar University • CSE — Artificial Intelligence & Machine Learning (Semester 5)

Foundations of Machine Learning

💡 DSU Exam Strategy & Guidance

Linear & Logistic Regression, Decision Trees (ID3/C4.5 Entropy & Information Gain), SVM (Kernel Trick), Naive Bayes Classifier, and Evaluation Metrics (ROC-AUC, Precision/Recall/F1).

High-Yield Passing Strategy: Practice Information Gain numerical calculations for Decision Tree root node selection.
Most Repeated Question Topics:
Decision Tree Information Gain & Entropy CalculationsSVM Margin Maximization & Dual FormulationBias-Variance Tradeoff & Regularization (L1/L2)Naive Bayes Classification Probability NumericalK-Means Clustering Step-by-Step

🔥 Most Predictable Exam Questions (1)

Guaranteed Every Year 8 Marks Module 2: Decision Trees & ID3

Q1. Calculate the Entropy and Information Gain for selecting the root attribute from a dataset with 9 Yes and 5 No instances, evaluating feature Wind (Weak: 6 Yes, 2 No; Strong: 3 Yes, 3 No) using ID3 Decision Tree algorithm.

Key Points: Entropy(S) = -(9/14)log2(9/14) - (5/14)log2(5/14) = 0.940. Entropy(Wind=Weak) = -(6/8)log2(6/8) - (2/8)log2(2/8) = 0.811. Entropy(Wind=Strong) = -(3/6)log2(3/6) - (3/6)log2(3/6) = 1.000. Gain(S, Wind) = 0.940 - [ (8/14)*0.811 + (6/14)*1.000 ] = 0.940 - 0.892 = 0.048.

🎓 Open Source Courses & Video Playlists (2)

Stanford Online

Stanford CS229: Machine Learning by Andrew Ng

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YouTube (StatQuest)

StatQuest: Machine Learning Fundamentals

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📄 Previous Year Question Papers (1)

END_SEM • 2024

End Semester Exam 2024

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