NPTEL : NOC:Essential Mathematics for Machine Learning (Mathematics)

Co-ordinators : Prof. S.K. Gupta, Dr. Sanjeev Kumar


Lecture 1 - Vectors in Machine Learning

Lecture 2 - Basics of Matrix Algebra

Lecture 3 - Vector Space: Definition and Examples

Lecture 4 - Vector Subspace: Examples and Properties

Lecture 5 - Basis and Dimension

Lecture 6 - Linear Transformations

Lecture 7 - Norms and Spaces

Lecture 8 - Orthogonal Complement and Projection Mapping

Lecture 9 - Eigenvalues and Eigenvectors

Lecture 10 - Special matrices and Properties

Lecture 11 - Spectral Decomposition

Lecture 12 - Singular Value Decomposition

Lecture 13 - SVD: Properties and Applications

Lecture 14 - Low Rank Approximations

Lecture 15 - Python Implementation of SVD and Low - rank Approximation

Lecture 16 - Principal Component Analysis - I

Lecture 17 - PCA: Derivation and Examples

Lecture 18 - Python Implementation of PCA

Lecture 19 - Linear Discriminant Analysis

Lecture 20 - Python Implementation of LDA

Lecture 21 - Least Square Approximation and Minimum Normed Solution

Lecture 22 - Linear and Multiple Regression - I

Lecture 23 - Linear and Multiple Regression - II

Lecture 24 - Logistic Regression - I

Lecture 25 - Logistic Regression - II

Lecture 26 - Classification Metrics

Lecture 27 - Gram Schmidt Process

Lecture 28 - Polar Decomposition

Lecture 29 - Minimal Polynomial and Jordan Canonical Form - I

Lecture 30 - Minimal Polynomial and Jordan Canonical Form - II

Lecture 31 - Basic Concepts of Calculus - I

Lecture 32 - Basic Concepts of Calculus - II

Lecture 33 - Basic Concepts of Calculus - III

Lecture 34 - Basic Concepts of Calculus - IV

Lecture 35 - Basic Concepts of Calculus - V

Lecture 36 - Calculus in Python

Lecture 37 - Convex Sets and Functions

Lecture 38 - Properties of convex functions - I

Lecture 39 - Properties of Convex functions - II

Lecture 40 - Introduction to Optimization

Lecture 41 - Unconstrained Optimization

Lecture 42 - Constrained Optimization - I

Lecture 43 - Constrained Optimization - II

Lecture 44 - Steepest Descent method

Lecture 45 - Newton's and Penalty function method

Lecture 46 - Optimization using Python

Lecture 47 - Operations on Sets

Lecture 48 - Review on Probability

Lecture 49 - Bayes' theorem and Random variables

Lecture 50 - Expectation and Variance

Lecture 51 - Discrete probability distributions

Lecture 52 - Continuous probability distributions

Lecture 53 - Joint probability distribution and covariance

Lecture 54 - Introduction to SVM

Lecture 55 - Error Minimizing LPP

Lecture 56 - Concepts of Duality

Lecture 57 - Hard Margin classifier

Lecture 58 - Soft margin classifier

Lecture 59 - SVM using Python - I

Lecture 60 - SVM using Python - II