Mathematics for Machine Learning BZCEY9T8E6

URI	`m2://res/BZCEY9T8E6`
Kind	course
Creators	Debashree Guha Adhya (instructor), Debjani Chakraborty (instructor)
Date	2024-07-08
Scope	in
Ids	course 03w5sq511:111105489

Expressions

e1: edition

Language	en
Date	2024-07-08

m1: page, IIT Kharagpur (record). nptel 111105489
m2: page, 2024-07-08 (record). nptel 111105489

Contents

part 1: Week 1
session 1: Lecture 1: Introduction to Machine Learning
session 2: Lecture 2: Few Instances of Learning
session 3: Lecture 3: Loss Function Optimization
session 4: Lecture 4: Phases of Machine Learning Systems
session 5: Lecture 5: Single-layer and multi-layer neurons
part 2: Week 2
session 6: Lecture 6: Introduction to Convexity
session 7: Lecture 7: Study on Convex Function
session 8: Lecture 8: Formation of Convex Hull
session 9: Lecture 9: Optimization of Functions
session 10: Lecture 10: Convex Optimization Problem
part 3: Week 3
session 11: Lecture 11: Directional derivative, Gradient, Jacobian, Firest Order Taylor Formula
session 12: Lecture 12: Gradient, Jacobian, First order Taylor formula
session 13: Lecture 13: Gradient Descent Optimization Technique
session 14: Lecture 14: Gradient Descent, Interpretation from Taylor approximation
session 15: Lecture 15: Sub-Gradient Descent Optimization Technique
part 4: Week 4
session 16: Lecture 16: Introduction to Underfitting & Overfitting
session 17: Lecture 17: Relation of Bias & Variance to Underfitting & Overfitting
session 18: Lecture 18: Bias-Variance Trade-off
session 19: Lecture 19: Ridge Regression
session 20: Lecture 20: LASSO Regression
part 5: Week 5
session 21: Lecture 21: Stochastic Gradient descent
session 22: Lecture 22: Stochastic Gradient descent (cont.)
session 23: Lecture 23: Variants of Gradient descent
session 24: Lecture 24: Momentum based Gradient descent
session 25: Lecture 25: Accelerated Gradient descent
part 6: Week 6
session 26: Lecture 26: Linear Predictors (Logistic Regression)
session 27: Lecture 27: Linear Predictors (Logistic Regression) (Contd..)
session 28: Lecture 28: Linear Predictors (Logistic Regression) (Contd..)
session 29: Lecture 29: Linear Predictors (Logistic Regression) (Contd..)
session 30: Lecture 30: Linear Predictors (Support Vector Machines)
part 7: Week 7
session 31: Lecture 31: Introduction to Theory of Estimation
session 32: Lecture 32: Probability Theory
session 33: Lecture 33: Discrete Probability Distribution
session 34: Lecture 34: Continuous Probability Distribution
session 35: Lecture 35: Maximum likelihood Estimation
part 8: Week 8
session 36: Lecture 36: MLE for Discrete Probability Distribution
session 37: Lecture 37: MLE for Continuous Probability Distribution
session 38: Lecture 38: Properties of Estimators
session 39: Lecture 39: MLE for Linear and Logistic Regression
session 40: Lecture 40: MLE for Naïve Bayes Model
part 9: Week 9
session 41: Lecture 41: Eigen value & eigen vector, Linearly independent vectors
session 42: Lecture 42: Orthogonal Basis, Eigen value decomposition
session 43: Lecture 43: Orthogonal Basis, Eigen value decomposition Continued
session 44: Lecture 44: Principal Component Analysis
session 45: Lecture 45: Principal Component Analysis Continued
part 10: Week 10
session 46: Lecture 46: Introduction to Dynamical System
session 47: Lecture 47: Dynamical System and Control
session 48: Lecture 48: Discrete Fourier Transform
session 49: Lecture 49: Inverse Fourier Transform
session 50: Lecture 50: Applications: Discrete Fourier Transform
part 11: Week 11
session 51: Lecture 51: Introduction to Mixture models
session 52: Lecture 52: Introduction to Gaussian Mixture Models
session 53: Lecture 53: EM Algorithm
session 54: Lecture 54: EM Algorithm (cont.)
session 55: Lecture 55: A brief application to predict HMM parameters
part 12: Week 12
session 56: Lecture 56 : Conditional probability, Bayes theorem
session 57: Lecture 57 : Bayes Decision Rule
session 58: Lecture 58 : Bayes Decision Rule
session 59: Lecture 59 : Bayes Decision Rule (Bayesian minimum risk classifier)
session 60: Lecture 60: Bayes Decision Rule (Linear Discriminant Analysis)

Catalogue records

nptel: 111105489 (manifestation)
nptel_video: 111105489 (manifestation)