Foundations of Machine Learning CST 312 KTU CS Elective Notes

 

Introduction

About Me
Syllabus
What is Machine Learning ( video)
Learn the seven steps in Machine Learning ( video)
Overview Of Machine Learning
Linear Algebra in Machine Learning

Module-1 Linear Algebra

1.Geometry of Linear Equations(video)
2.Elimination with Matrices(video)
3.Solving System of equations using Gauss Elimination Method
4.Row Echelon form and Reduced Row Echelon Form
5.Solving system of equations Python code
6. Practice problems Gauss Elimination ( contact)
7.Finding Inverse using Gauss Jordan Elimination (video)
8.Finding Inverse using Gauss Jordan Elimination-Python code
9.Vector spaces and sub spaces
10.Linear Independence
11.Linear Independence, Basis and Dimension(video)
12.Generating set basis and span
13.Rank of a Matrix
14.Linear Mapping and Matrix Representation of Linear Mapping

Module 2: Linear Algebra

1.Vector Norms
2.Inner Products
3.Length of vectors and distance
4.Angle Between vectors and orthogonality
5.Orthogonal Matrix
6.Orthonormal Basis
7.Orthogonal Compliment
8.Orthogonal Projections
9.Projection onto subspace ( video)
10.Gram-Schmidt Orthogonalization ( optional)
11.Practice Problems ( contact)
12.Determinant and Trace of a Matrix
13.Eigen value and Eigen vectors
14.Example Problems
15.Cholesky Decomposition ( optional)
16.Eigen Decomposition and Diagonalization
17.Singular Value Decomposition-SVD ( optional)
18.Matrix Approximations ( optional)

Module-3 Probability and Distributions


Module-4 Random Variables


Module-5 Limit Theorems



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