Mathematics 2
Linear Algebra is the backbone of modern Data Science and Machine Learning. In this course, we will explore systems of equations, vector spaces, and matrix operations that form the basis of algorithms like PCA and Neural Networks.
Course Syllabus
Week 1: Systems of Linear Equations
Introduction to Systems of Linear Equations and Gaussian Elimination.
Vectors: The Foundation of Linear Algebra
Introduction to vectors, their representation as rows and columns, geometric interpretation, and basic operations.
Graded Assignment: Week 1
Graded assignment for Week 1: Linear Systems & Matrices.
Practice Assignment: Week 1
Practice problems for Week 1: Linear Systems.
Weekly Tutorial: Week 1
Video tutorial and summary for Week 1.
Week 2: Vector Spaces
Introduction to Vector Spaces, Subspaces, and Linear Independence.
Introduction to Vectors
Vectors in R^n and their properties.
Graded Assignment 2
Linear Algebra: Systems of Equations, Matrices, and Quadratic Models.
Graded Assignment: Week 2
Graded assignment for Week 2: Matrix Operations.
Practice Assignment: Week 2
Practice problems for Week 2: Matrix Operations.
Weekly Tutorial: Week 2
Video tutorial and summary for Week 2.
Week 3: Matrices & Determinants
Matrix operations, Determinants, and Inverse of a Matrix.
Introduction to Matrices
Basic definitions and types of matrices.
Graded Assignment: Week 3
Graded assignment for Week 3: Vector Spaces.
Practice Assignment: Week 3
Practice problems for Week 3: Vector Spaces.
Weekly Tutorial: Week 3
Video tutorial and summary for Week 3.
Week 4: Eigenvalues & Eigenvectors
Understanding Eigenvalues, Eigenvectors, and Diagonalization.
Finding Eigenvalues
Characteristic equation and finding eigenvalues.
Graded Assignment: Week 4
Graded assignment for Week 4: Orthogonality.
Practice Assignment: Week 4
Practice problems for Week 4: Orthogonality.
Weekly Tutorial: Week 4
Video tutorial and summary for Week 4.
Previous Year Questions
Previous Year Questions for Mathematics 2
Previous Year Questions
Previous Year Questions for Mathematics 2
All Chapters in this Book
Mathematics 1
Foundation level mathematics covering Sets, Relations, Functions, and Calculus.
Mathematics 2
Advanced mathematics for Data Science covering Linear Algebra, Vector Spaces, and Matrices.
Maths 2 Book: Linear Algebra
A complete textbook course on Linear Algebra for Data Science, covering Vectors, Matrices, and Vector Spaces.
Statistics 2
Advanced statistical methods including Hypothesis Testing, Regression, and ANOVA.
Stats 2 Book: Probability & Statistics
A comprehensive textbook covering probability, distributions, inference, and regression.
Stats 1 Vol 1: Descriptive Statistics
A comprehensive guide to the foundational concepts of statistics, data organisation, and probability-based counting.