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ML

Unit-2

Lecture Videos

ML , syllabus Wise Unit-2 Lecture Videos
Decision Tree Learning:
Decision Tree Learning Part 2
Representing Concepts as Decision Trees
Decision Tree Learning Representing concepts as decision trees
Recursive Induction of Decision Trees
Picking the Best Splitting Attribute
Entropy and Information Gain
Searching for Simple Trees and Computational Complexity
Occam's Razor
Occam's Razor - rational principles explained
Overfitting
Outliers and Noisy Data
Pruning
Experimental Evaluation of Learning Algorithms:
Measuring the Accuracy of Learned Hypotheses
Comparing Learning Algorithms
Cross-Validation
Learning Curves
Statistical Hypothesis Testing

Unit-2 Syllabus

UNIT II: Decision Tree Learning Representing concepts as decision trees, Recursive induction of decision trees, Picking the best splitting attribute: entropy and information gain, Searching for simple trees and computational complexity, Occam's razor, Over fitting, noisy data, and pruning. Experimental Evaluation of Learning Algorithms: Measuring the accuracy of learned hypotheses. Comparing learning algorithms: cross-validation, learning curves, and statistical hypothesis testing.