Mathematical Foundations for Machine Learning

Lec 70 Course Summary, Credits & Acknowledgments

Lec 69 Logistic Regression

Lec 68 Linear Regression : Line of best fit

Lec 67 Classification, Clustering Techniques

Lec 66 Classification and a simple binary classifier

Lec 65 Data and Patterns

Lec 64 Back Propogation Algorithm

Lec 63 Multilayer Layer Perceptron

Lec 62 Neural Networks , Perceptron

Lec 61 Variants of the Gradient Descent Algorithm

Lec 60 Minimizing the Cost Function: Gradent Descent Algorithm

Lec 59 Revisiting Least squares, Principal Component Analysis

Lec 58 Constrained optimization, Lagrange Multiplier

Lec 57 Constrained Optimization, Optimal solutions, Saddle point

Lec 56 Optimization Overview

Lec 55 Matrix derivatives

Lec 54 Rules for Partial Derivatives, Jacobian and Hessian

Lec 53 Gradient and Directional Derivatives

Lec 52 Multivariate functions

Lec 51 Differentiation Rules

Lec 50 Functions, Derivatives, Infinite Series

Lec 49 Covariance Matrix & its properties

Lec 48 Sample Geometry

Lec 47 Central Limit Theorem

Lec 46 Chebychev Inequality

Lec 45 Markov Inequality

Lec 44 Joint Moments of Continuous random Variables and Conditioning of Random Variables

Lec 43 Correlation and Covariance

Lec 42 Independence and Correlation

Lec 41 Joint Moments of Random Variables

Lec 40 Joint Distributions and Marginals

Lec 39 Moments and Variance

Lec 38 Expected Value of Random Variable

Lec 37 Continuous Random Variables

Lec 36 Types of Discrete Random Variables and Their Probability Distributions

Lec 35 Discrete Random Variables and Probability Mass Function (PMF)

Lec 34 Random Experiment and Random Variables

Lec 33 Probability - A Measure Theoretic Insight

Lec 32 Total Probability Theorem and Bayes' Theorem

Lec 31 Mutually Exclusive Events, Independent Events and Conditional Probability

Lec 30 Introduction to Probability - 2

Lec 29 Introduction to Probability - 1

Lec 28 Consolidating Week1 to Week 4

Lec 27 Support Vector Machine (SVM)

Lec 26 Singular Value Decomposition (SVD) Interpretation

Lec 25 Singular Value Decomposition (SVD)

Lec 24 Principal Component Analysis (PCA)

Lec 23 Least Square Fitting and Pseudo Inverse - 2

Lec 22 Least Square Fitting and Pseudo Inverse - 1

Lec 21 Real Symmetric Matrices

Lec 20 Approximating a vector in any given subspace

Lec 19 Construction of Orthogonal Basis

Lec 18 Orthogonal Projections

Lec 17 Orthonormal Basis

Lec 16 Orthogonality on Vector Space

Lec 15 Dot Product on Vector Space

Lec 14 Diagonalizability, Invariant subspaces

Lec 13 Diagonalization of Matrix

Lec 12 Multiplicity of Eigen Values

Lec 11 Eigenvalues and Eigenvectors

Lec 10 Examples of Linear Transformation

Lec 09 Matrix representation of Linear Transformation

Lec 08 Subspaces associated with Linear Transformations

Lec 07 Linear Transformation - Intuition

Lec 06 Basis and Dimension

Lec 05 Combining Vectors and Linear Independence

Lec 04 Vector Spaces and Subspaces

Lec 03 Fields, Vector Spaces

Lec 02 Fields and Their Properties – Algebraic Tools for ML

Lec 01 Why this course?

Mathematical Foundations for Machine Learning (Introduction Video)