NOC:Mathematics for Machine Learning

Lecture 1 - Introduction to Machine Learning

Lecture 2 - Few Instances of Learning

Lecture 3 - Loss Function Optimization

Lecture 4 - Phases of Machine Learning Systems

Lecture 5 - Single-layer and multi-layer neurons

Lecture 6 - Introduction to Convexity

Lecture 7 - Study on Convex Function

Lecture 8 - Formation of Convex Hull

Lecture 9 - Optimization of Functions

Lecture 10 - Convex Optimization Problem

Lecture 11 - Directional derivative, Gradient, Jacobian, Firest Order Taylor Formula

Lecture 12 - Gradient, Jacobian, First order Taylor formula

Lecture 13 - Gradient Descent Optimization Technique

Lecture 14 - Gradient Descent, Interpretation from Taylor approximation

Lecture 15 - Sub-Gradient Descent Optimization Technique

Lecture 16 - Introduction to Underfitting and Overfitting

Lecture 17 - Relation of Bias and Variance to Underfitting and Overfitting

Lecture 18 - Bias-Variance Trade-off

Lecture 19 - Ridge Regression

Lecture 20 - LASSO Regression

Lecture 21 - Stochastic Gradient descent

Lecture 22 - Stochastic Gradient descent (Continued...)

Lecture 23 - Variants of Gradient descent

Lecture 24 - Momentum based Gradient descent

Lecture 25 - Accelerated Gradient descent

Lecture 26 - Linear Predictors (Logistic Regression)

Lecture 27 - Linear Predictors (Logistic Regression) (Continued....)

Lecture 28 - Linear Predictors (Logistic Regression) (Continued....)

Lecture 29 - Linear Predictors (Logistic Regression) (Continued....)

Lecture 30 - Linear Predictors (Support Vector Machines)

Lecture 31 - Introduction to Theory of Estimation

Lecture 32 - Probability Theory

Lecture 33 - Discrete Probability Distribution

Lecture 34 - Continuous Probability Distribution

Lecture 35 - Maximum likelihood Estimation

Lecture 36 - MLE for Discrete Probability Distribution

Lecture 37 - MLE for Continuous Probability Distribution

Lecture 38 - Properties of Estimators

Lecture 39 - MLE for Linear and Logistic Regression

Lecture 40 - MLE for Naïve Bayes Model

Lecture 41 - Eigen value and eigen vector, Linearly independent vectors

Lecture 42 - Orthogonal Basis, Eigen value decomposition

Lecture 43 - Orthogonal Basis, Eigen value decomposition (Continued...)

Lecture 44 - Principal Component Analysis

Lecture 45 - Principal Component Analysis (Continued...)

Lecture 46 - Introduction to Dynamical System

Lecture 47 - Dynamical System and Control

Lecture 48 - Discrete Fourier Transform

Lecture 49 - Inverse Fourier Transform

Lecture 50 - Applications: Discrete Fourier Transform

Lecture 51 - Introduction to Mixture models

Lecture 52 - Introduction to Gaussian Mixture Models

Lecture 53 - EM Algorithm

Lecture 54 - EM Algorithm (Continued...)

Lecture 55 - A brief application to predict HMM parameters

Lecture 56 - Conditional probability, Bayes theorem

Lecture 57 - Bayes Decision Rule

Lecture 58 - Bayes Decision Rule (Continued...)

Lecture 59 - Bayes Decision Rule (Bayesian minimum risk classifier)

Lecture 60 - Bayes Decision Rule (Linear Discriminant Analysis)