Mathematical Foundations for Machine Learning

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Curated by: NPTEL - Indian Institute of Science, Bengaluru (71 videos)

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Currently Playing: Lec 24 Principal Component Analysis (PCA)

PCA, Covariance matrix, eigenvalues and eigenvectors, Principal components, data compression

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Tracks in this Playlist

• 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)