Learn · Essential Mathematics for Machine Learning
Lecture 24 of 60Week 5
Logistic Regression-I
Prof. S. K. Gupta and Prof. Sanjeev Kumar · IIT Roorkee
Credits and licence
- From
- Essential Mathematics for Machine Learning, IIT Roorkee (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Vectors in Machine Learning
- Basics of Matrix Algebra
- Vector Space: Definition and Examples
- Vector Subspace: Examples and Properties
- Basis and Dimension
- Linear Transformation
- Norms and Spaces
- Orthogonal Complement and Projection Mapping
- Eigenvalues and Eigenvectors
- Special matrices and Properties
- Spectral Decomposition
- Singular Value Decomposition
- SVD: Properties and Applications
- Low Rank Approximations
- Python Implementation of SVD and Low-rank Approximation
- Principal Component Analysis-I
- PCA : Derivation and Examples
- Python Implementation of PCA
- Linear Discriminant Analysis
- Python Implementation of LDA
- Least Square Approximation and Minimum Normed Solution
- Linear and Multiple Regression-I
- Linear and Multiple Regression-II
- Logistic Regression-I
- Logistic Regression-II
- Classification Metrics
- Gram Schmidt process
- Polar Decomposition
- Minimal Polynomial and Jordan Canonical Form-I
- Minimal Polynomial and Jordan Canonical Form-II
- Basic Concepts of Calculus-I
- Basic Concepts of Calculus-II
- Basic Concepts of Calculus-III
- Basic Concepts of Calculus-IV
- Basic Concepts of Calculus-V
- Calculus in Python
- Convex Sets and Functions
- Properties of Convex functions-I
- Properties of convex functions-II
- Introduction to Optimization
- Unconstrained Optimization
- Constrained optimization-I
- Constrained optimization-II
- Steepest Descent Method
- Newton’s and Penalty function method
- Optimization using Python
- Operations on Sets
- Review of Probability
- Bayes theorem and random variable
- Expectation and variance
- Discrete probability distributions
- Continuous Probability distributions
- Joint probability and covariance
- Introduction to SVM
- Error minimizing LPP
- Concepts of Duality
- Hard Margin Classifier
- Soft Margin Classifier
- SVM using Python-I
- SVM using Python-II