Astra

1 Essential Mathematics for Machine Learning

2 Lecture 01 Introduction to Course and Vectors

3 Lecture 02 Vector Spaces Definition and Examples

4 Lecture 03 Vector Subspaces Examples and Properties

5 Lecture 03 Vector Subspaces Examples and Properties

6 Lecture 04 Vector Subspaces Basis and Dimension

6 Lecture 05 Linear Transformations

7 Lecture 06 Norms and Spaces

8 Lecture 07 Orthogonal Complements and Projection Operator

9 Lecture 08 Eigenpairs and Properties

10 Lecture 09 Special Matrices and Properties

11 Lecture 10 Least Square Approximation and Minimum Norm Solution

12 Lecture 11 Singular Value Decomposition

13 Lecture 12 SVD Properties and Applications

14 Lecture 13 Low Rank Approximation

15 Lecture 14 Gram Schmidt process

16 Lecture 15 Polar Decomposition

17 Lecture 16 Principal Component Analysis-I

18 Lecture 17 PCA-II Derivation and Examples

19 Lecture 18 Linear Discriminant Analysis

20 Lecture 19 Minimal Polynomial and Jordan Canonical Form-I

21 Lecture 20 Minimal Polynomial and Jordan Canonical Form-II

22 Lecture 21 Basic Concepts of Calculus-I

23 Lecture 22 Basic Concepts of Calculus-II

24 Lecture 23 Convex Sets and Functions

25 Lecture 24 Properties of Convex Functions-I

26 Lecture 25 Properties of Convex Functions-II

27 Lecture 26 Unconstrained Optimization

28 Lecture 27 Constrained Optimization-I

29 Lecture 28 Constrained Optimization-II

30 Lecture 29 Steepest Descent Method

31 Lecture 30 Newton's and Penalty Function Methods

32 Lecture 31 Review of Probability

33 Lecture 32 Bayes' Theorem and Random Variables

34 Lecture 33 Expectation and Variance

35 Lecture 34 Few Probability Distributions

36 Lecture 35 Joint Probability Distributions and Covariance

37 Lecture 36 Introduction to Support Vector Machines

38 Lecture 37 Error Minimizing LPP

39 Lecture 38 Concepts of Duality

40 Lecture 39 Hard Margin Classifier

41 Lecture 40 Soft Margin Classifier