NOC:Optimisation for Machine Learning: Theory and Implementation (Hindi)

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