Lec 5 | Law of Total Probability | Probability for Machine Learning #probability #machinelearning

Welcome to *ScholarsGT – Foundations for Machine Learning!* In this lecture, we’ll understand the *Law of Total Probability* step by step, building intuition before applying the mathematical formula. Before deriving the formula, we’ll understand two important concepts: *Mutually Exclusive Events:* Events that cannot occur together. *Exhaustive Events:* A collection of events that covers all possible outcomes in the sample space. Next, we’ll derive the formula for the Law of Total Probability and solve a numerical example to understand how it works in practice. 📌 *What you’ll learn in this video:* 1. What are mutually exclusive events? 2. What are exhaustive events? 3. How do these events partition a sample space? 4. Derivation of the Law of Total Probability. 5. The formula and its intuitive meaning. 6. A numerical example to connect the intuition with the formula. 📐 *Law of Total Probability:* This theorem helps us calculate the probability of an event by considering all the possible ways it can occur. 🎯 *Why is this important for Machine Learning?* Probability is a foundation for understanding Bayesian reasoning, probabilistic models, statistical inference, and many machine learning algorithms. This video is part of the *Foundations for Machine Learning* playlist on ScholarsGT, where we build mathematical intuition for AI and ML concepts. 👍 If this explanation helps you, like the video, share it with fellow learners, and subscribe to *ScholarsGT* for more lectures on Mathematics, Machine Learning, Deep Learning, and AI. #LawOfTotalProbability #Probability #MachineLearning #mtech #btech #machinelearningforbeginners