#8 Introduction to Probability Theory Discrete & Continuous Random Variables

Machine Learning for Engineering & Science Applications | IIT Madras - #8 Introduction to Probability Theory Discrete & Continuous Random Variables

Unlock the Future: Master AI & Machine Learning with NPTEL-IITM’s Comprehensive Course! Dive into Neural Networks, Deep Learning, Probabilities, and Optimization Techniques tailored for Engineering & Science Applications. Your AI journey starts here!

4.0 (2)

22 learners

What you'll learn

Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.

This course includes

32 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Machine Learning for Engineering and Science Applications Prof. Ganapathy Balaji Srinivasan Lecture Online Course Video NPTEL

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture introduces probability theory, a mathematical framework for representing uncertainty. It covers the concept of random variables, distinguishing between discrete and continuous types. The lecture explains sample spaces, probability distributions, and their applications in modeling random phenomena. NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications. To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198 #ProbabilityTheory #RandomVariables #DiscreteRandomVariables #ContinuousRandomVariables #SampleSpace #ProbabilityDistribution

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