NOC:Optimisation for Machine Learning: Theory and Implementation (Hindi)
Media Storage Type : 32 GB USB Stick
NPTEL Subject Matter Expert : Prof. Pravesh Biyani
NPTEL Co-ordinating Institute : IIT Madras
NPTEL Lecture Count : 36
NPTEL Course Size : 3.3 GB
NPTEL PDF Text Transcription : Available and Included
NPTEL Subtitle Transcription : Available and Included (SRT)
Lecture Titles:
Lecture 1 - Basics of Linear Algebra: Linear Independence
Lecture 2 - Linear Algebra: Rank of a matrix
Lecture 3 - Linear Algebra - Subspaces of a matrix - 1
Lecture 4 - Linear Algebra - Subspaces of a matrix - 2
Lecture 5 - Linear Algebra - Null space
Lecture 6 - Linear Algebra - Eigen Vectors/Values of a matrix - 1
Lecture 7 - Linear Algebra - Eigen Vectors/Values of a matrix - 2
Lecture 8 - Programming Eigen Decomposition using Python
Lecture 9 - Singular Value Decomposition - 1
Lecture 10 - Singular Value Decomposition - 2
Lecture 11 - Principal Component Analysis - 1
Lecture 12 - Principal Component Analysis - 2
Lecture 13 - Principal Component Analysis - 3
Lecture 14 - Principal Component Analysis - Coding
Lecture 15 - Machine Learning - Overview
Lecture 16 - Optimisation Problems
Lecture 17 - Gradient of a Vector Valued Function - 1
Lecture 18 - Gradient of a Vector Valued Function - 2
Lecture 19 - Neural Netowrks - Overview
Lecture 20 - Neural Netowrks - Backpropagation
Lecture 21 - Optimisation - Introduction to optimisation problems
Lecture 22 - Optimisation - Relaxation and approximate convergence
Lecture 23 - Optimisation - First Order Optimality Condition
Lecture 24 - Optimisation - Second Order Optimality Condition
Lecture 25 - Proof of Second Order Optimality Condition, Gradient Methods
Lecture 26 - Gradient Descent - 2
Lecture 27 - Variants of Gradient Descent - 1
Lecture 28 - Variants of Gradient Descent - 2
Lecture 29 - Variants of Gradient Descent - 3
Lecture 30 - Convex Sets
Lecture 31 - Convex Functions
Lecture 32 - Duality and Lagrangian - Part 1
Lecture 33 - Duality and Lagrangian - Part 2
Lecture 34 - Duality and Lagrangian - Part 3
Lecture 35 - Coding: Introduction to Pytorch
Lecture 36 - Guest Lectuer: Support Vector Machine