Welcome to Lecture 50 of the course "Machine Learning Techniques" by Prof. Arun Rajkumar.
Full Course: https://study.iitm.ac.in/ds/course_pages/BSCS2007.html
Video Overview
This lecture introduces the perceptron learning algorithm, a fundamental discriminative model for binary classification. Building on the earlier discussion of generative and discriminative approaches, we first highlight the limitations of generative models such as Naive Bayes and motivate the direct modeling of conditional probability P(Y|X). The perceptron algorithm is then presented as a classic solution for linearly separable data, with a detailed analysis of its update rule. The session concludes with an exploration of the linear separability assumption and convergence analysis.
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#MachineLearning #DiscriminativeModels #BinaryClassification #Perceptron #LinearSeparability #SupervisedLearning #Algorithm #Classification #NaiveBayes #GenerativeModels #DataScience
#ConvergenceAnalysis #LinearlySeparableData #MLFundamentals #NeuralNetworksBasics #PatternClassification
