Learn · Mathematics for Machine Learning
Lecture 33 of 60Week 7
Lecture 33: Discrete Probability Distribution
Prof. Debjani Chakraborty and Prof. Debashree Guha Adhya · IIT Kharagpur
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All the lectures
- 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 & Overfitting
- Lecture 17: Relation of Bias & Variance to Underfitting & 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 (cont.)
- 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) (Contd..)
- Lecture 28: Linear Predictors (Logistic Regression) (Contd..)
- Lecture 29: Linear Predictors (Logistic Regression) (Contd..)
- 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 & 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 (cont.)
- 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
- Lecture 59 : Bayes Decision Rule (Bayesian minimum risk classifier)
- Lecture 60: Bayes Decision Rule (Linear Discriminant Analysis)