Lecture 7: CS217 Perceptron Training Algorithm & Neural Foundations | AI-ML | IIT Bombay | 2025

Welcome to Lecture 7 of the CS217: AI-ML Course by IIT Bombay, delivered by Prof. Pushpak Bhattacharya. This lecture introduces fundamental concepts of neural computation through perceptrons and provides a rigorous mathematical proof of the Perceptron Training Algorithm convergence.
🔎 Topics Covered:
Hardware-Software Correspondence in Brain Computing
Maslow's Hierarchy and Its Relevance to AI Systems
Historical Evolution: Symbolic AI vs Connectionist Approaches
Perceptron Model: Basic Structure and Computation
Boolean Function Computation using Perceptrons
Threshold Functions and Their Limitations
XOR Problem and Non-linear Separability
Perceptron Training Algorithm (PTA): Step-by-Step Implementation
Convergence Theorem: Detailed Mathematical Proof
Geometric and Algebraic Understanding of PTA
This lecture provides essential foundations in neural computation, combining theoretical rigor with practical understanding.
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