Mathematical Foundations for Machine Learning
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Curated by: NPTEL - Indian Institute of Science, Bengaluru (71 videos)
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- 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)