Machine Learning for Engineering & Science Applications | IIT Madras - #17 Matrix Calculus | Slightly Advanced | Machine Learning for Engineering & Science Applications

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!

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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

Lecture 17

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture delves into matrix calculus, an advanced topic that provides a framework for understanding the transformation of input vectors into output vectors. While a deep understanding of this material is not essential for most of the course, it enhances comprehension of the mathematical underpinnings of machine learning. The lecture presents key relations and concepts in matrix calculus, offering insights into optimizing complex functions. 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 #MatrixCalculus #AdvancedCalculus #Optimization #InputOutputRelationships

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