Lecture 1 - Why this course ?
Lecture 2 - Fields and Their Properties - Algebraic Tools for ML
Lecture 3 - Fields, Vector Spaces
Lecture 4 - Vector Spaces and Subspaces
Lecture 5 - Combining Vectors and Linear Independence
Lecture 6 - Basis and Dimension
Lecture 7 - Linear Transformation - Intuition
Lecture 8 - Subspaces associated with Linear Transformations
Lecture 9 - Matrix representation of Linear Transformation
Lecture 10 - Examples of Linear Transformation
Lecture 11 - Eigenvalues and Eigenvectors
Lecture 12 - Multiplicity of Eigen Values
Lecture 13 - Diagonalization of Matrix
Lecture 14 - Diagonalizability, Invariant subspaces
Lecture 15 - Dot Product on Vector Space
Lecture 16 - Orthogonality on Vector Space
Lecture 17 - Orthonormal Basis
Lecture 18 - Orthogonal Projections
Lecture 19 - Construction of Orthogonal Basis
Lecture 20 - Approximating a vector in any given subspace
Lecture 21 - Real Symmetric Matrices
Lecture 22 - Least Square Fitting and Pseudo Inverse - 1
Lecture 23 - Least Square Fitting and Pseudo Inverse - 2
Lecture 24 - Principal Component Analysis (PCA)
Lecture 25 - Singular Value Decomposition (SVD)
Lecture 26 - Singular Value Decomposition (SVD) Interpretation
Lecture 27 - Support Vector Machine (SVM)
Lecture 28 - Consolidating Week 1 to Week 4
Lecture 29 - Introduction to Probability - 1
Lecture 30 - Introduction to Probability - 2
Lecture 31 - Mutually Exclusive Events, Independent Events and Conditional Probability
Lecture 32 - Total Probability Theorem and Bayes' Theorem
Lecture 33 - Probability - A Measure Theoretic Insight
Lecture 34 - Random Experiment and Random Variables
Lecture 35 - Discrete Random Variables and Probability Mass Function (PMF)
Lecture 36 - Types of Discrete Random Variables and Their Probability Distributions
Lecture 37 - Continuous Random Variables
Lecture 38 - Expected Value of Random Variable
Lecture 39 - Moments and Variance
Lecture 40 - Joint Distributions and Marginals
Lecture 41 - Joint Moments of Random Variables
Lecture 42 - Independence and Correlation
Lecture 43 - Correlation and Covariance
Lecture 44 - Joint Moments of Continuous random Variables and Conditioning of Random Variables
Lecture 45 - Markov Inequality
Lecture 46 - Chebychev Inequality
Lecture 47 - Central Limit Theorem
Lecture 48 - Sample Geometry
Lecture 49 - Covariance Matrix and its properties
Lecture 50 - Functions, Derivatives, Infinite Series
Lecture 51 - Differentiation Rules
Lecture 52 - Multivariate functions
Lecture 53 - Gradient and Directional Derivatives
Lecture 54 - Rules for Partial Derivatives, Jacobian and Hessian
Lecture 55 - Matrix derivatives
Lecture 56 - Optimization Overview
Lecture 57 - Constrained Optimization, Optimal solutions, Saddle point
Lecture 58 - Constrained optimization, Lagrange Multiplier
Lecture 59 - Revisiting Least squares, Principal Component Analysis
Lecture 60 - Minimizing the Cost Function: Gradent Descent Algorithm
Lecture 61 - Variants of the Gradient Descent Algorithm
Lecture 62 - Neural Networks, Perceptron
Lecture 63 - Multilayer Layer Perceptron
Lecture 64 - Back Propogation Algorithm
Lecture 65 - Data and Patterns
Lecture 66 - Classification and a simple binary classifier
Lecture 67 - Classification, Clustering Techniques
Lecture 68 - Linear Regression : Line of best fit
Lecture 69 - Logistic Regression
Lecture 70 - Course Summary, Credits and Acknowledgments
