Math 2.0

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Essential Mathematics for Machine Learning PM2Q0PC1NA

URIm2://res/PM2Q0PC1NA
Kindcourse
CreatorsS.K. Gupta (instructor), Sanjeev Kumar (instructor)
Date2021-07-10
Scopein
Idscourse 00582g326:111107137

Expressions

e1: edition

Languageen
Date2021-07-10
Contents
  • part 1: Week 1
  • session 1: Vectors in Machine Learning
  • session 2: Basics of Matrix Algebra
  • session 3: Vector Space: Definition and Examples
  • session 4: Vector Subspace: Examples and Properties
  • session 5: Basis and Dimension
  • part 2: Week 2
  • session 6: Linear Transformation
  • session 7: Norms and Spaces
  • session 8: Orthogonal Complement and Projection Mapping
  • session 9: Eigenvalues and Eigenvectors
  • session 10: Special matrices and Properties
  • part 3: Week 3
  • session 11: Spectral Decomposition
  • session 12: Singular Value Decomposition
  • session 13: SVD: Properties and Applications
  • session 14: Low Rank Approximations
  • session 15: Python Implementation of SVD and Low-rank Approximation
  • part 4: Week 4
  • session 16: Principal Component Analysis-I
  • session 17: PCA : Derivation and Examples
  • session 18: Python Implementation of PCA
  • session 19: Linear Discriminant Analysis
  • session 20: Python Implementation of LDA
  • part 5: Week 5
  • session 21: Least Square Approximation and Minimum Normed Solution
  • session 22: Linear and Multiple Regression-I
  • session 23: Linear and Multiple Regression-II
  • session 24: Logistic Regression-I
  • session 25: Logistic Regression-II
  • part 6: Week 6
  • session 26: Classification Metrics
  • session 27: Gram Schmidt process
  • session 28: Polar Decomposition
  • session 29: Minimal Polynomial and Jordan Canonical Form-I
  • session 30: Minimal Polynomial and Jordan Canonical Form-II
  • part 7: Week 7
  • session 31: Basic Concepts of Calculus-I
  • session 32: Basic Concepts of Calculus-II
  • session 33: Basic Concepts of Calculus-III
  • session 34: Basic Concepts of Calculus-IV
  • session 35: Basic Concepts of Calculus-V
  • part 8: Week 8
  • session 36: Calculus in Python
  • session 37: Convex Sets and Functions
  • session 38: Properties of Convex functions-I
  • session 39: Properties of convex functions-II
  • session 40: Introduction to Optimization
  • part 9: Week 9
  • session 41: Unconstrained Optimization
  • session 42: Constrained optimization-I
  • session 43: Constrained optimization-II
  • session 44: Steepest Descent Method
  • session 45: Newton’s and Penalty function method
  • part 10: Week 10
  • session 46: Optimization using Python
  • session 47: Operations on Sets
  • session 48: Review of Probability
  • session 49: Bayes theorem and random variable
  • session 50: Expectation and variance
  • part 11: Week 11
  • session 51: Discrete probability distributions
  • session 52: Continuous Probability distributions
  • session 53: Joint probability and covariance
  • session 54: Introduction to SVM
  • session 55: Error minimizing LPP
  • part 12: Week 12
  • session 56: Concepts of Duality
  • session 57: Hard Margin Classifier
  • session 58: Soft Margin Classifier
  • session 59: SVM using Python-I
  • session 60: SVM using Python-II

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