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September 26, 2023

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Description

Introduction to Machine Learning. Instructor: Dr. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras.

With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms (from nptel.ac.in)

Course Curriculum

    • Lecture 01 – A Brief Introduction to Machine Learning Unlimited
    • Lecture 02 – Supervised Learning Unlimited
    • Lecture 03 – Unsupervised Learning Unlimited
    • Lecture 04 – Reinforcement Learning Unlimited
    • Lecture 05 – Probability Basics 1 Unlimited
    • Lecture 06 – Probability Basics 2 Unlimited
    • Lecture 07 – Linear Algebra 1 Unlimited
    • Lecture 08 – Linear Algebra 2 Unlimited
    • Lecture 09 – Statistical Decision Theory: Regression Unlimited
    • Lecture 10 – Statistical Decision Theory: Classification Unlimited
    • Lecture 11 – Bias-Variance Unlimited
    • Lecture 12 – Linear Regression Unlimited
    • Lecture 13 – Multivariate Regression Unlimited
    • Lecture 14 – Subset Selection 1 Unlimited
    • Lecture 15 – Subset Selection 2 Unlimited
    • Lecture 16 – Shrinkage Methods Unlimited
    • Lecture 17 – Principal Components Regression Unlimited
    • Lecture 18 – Partial Least Squares Unlimited
    • Lecture 19 – Linear Classification Unlimited
    • Lecture 20 – Logistic Regression Unlimited
    • Lecture 21 – Linear Discriminant Analysis 1 Unlimited
    • Lecture 22 – Linear Discriminant Analysis 2 Unlimited
    • Lecture 23 – Linear Discriminant Analysis 3 Unlimited
    • Lecture 24 – Weka Tutorial Unlimited
    • Lecture 25 – Optimization Unlimited
    • Lecture 26 – Perceptron Learning Unlimited
    • Lecture 27 – Support Vector Machines – Formulation Unlimited
    • Lecture 28 – Support Vector Machines – Interpretation and Analysis Unlimited
    • Lecture 29 – Support Vector Machines for Linearly Non-separable Data Unlimited
    • Lecture 30 – SVM Kernels Unlimited
    • Lecture 31 – SVM – Hinge Loss Formulation Unlimited
    • Lecture 32 – Early Methods Unlimited
    • Lecture 33 – Backpropagation I Unlimited
    • Lecture 34 – Backpropagation II Unlimited
    • Lecture 35 – Initialization, Training and Validation Unlimited
    • Lecture 36 – Maximum Likelihood Estimate Unlimited
    • Lecture 37 – Priors and the MAP Estimate Unlimited
    • Lecture 38 – Bayesian Parameter Estimation Unlimited
    • Lecture 39 – Decision Trees: Introduction Unlimited
    • Lecture 40 – Regression Trees Unlimited
    • Lecture 41 – Stopping Criteria and Pruning Unlimited
    • Lecture 42 – Decision Trees for Classification – Loss Functions Unlimited
    • Lecture 43 – Categorical Attributes Unlimited
    • Lecture 44 – Multiway Splits Unlimited
    • Lecture 45 – Missing Values, Imputation and Surrogate Splits Unlimited
    • Lecture 46 – Instability, Smoothness and Repeated Subtrees Unlimited
    • Lecture 47 – Decision Trees: Tutorial Unlimited
    • Lecture 48 – Evaluation and Evaluation Measures I Unlimited
    • Lecture 49 – Bootstrapping and Cross Validation Unlimited
    • Lecture 50 – 2 Class Evaluation Measures Unlimited
    • Lecture 51 – The ROC Curve Unlimited
    • Lecture 52 – Minimum Description Length and Exploratory Analysis Unlimited
    • Lecture 53 – Introduction to Hypothesis Testing Unlimited
    • Lecture 54 – Hypothesis Testing: Basic Concepts Unlimited
    • Lecture 55 – Sampling Distributions and the Z Test Unlimited
    • Lecture 56 – Student’s T-Test Unlimited
    • Lecture 57 – The Two Samples and Paired Sample T-Tests Unlimited
    • Lecture 58 – Confidence Intervals Unlimited
    • Lecture 59 – Bagging, Committee Machines and Stacking Unlimited
    • Lecture 60 – Boosting Unlimited
    • Lecture 61 – Gradient Boosting Unlimited
    • Lecture 62 – Random Forests Unlimited
    • Lecture 63 – Naive Bayes Unlimited
    • Lecture 64 – Bayesian Networks Unlimited
    • Lecture 65 – Undirected Graphical Methods: Introduction and Factorization Unlimited
    • Lecture 66 – Undirected Graphical Methods: Potential Functions Unlimited
    • Lecture 67 – Hidden Markov Models Unlimited
    • Lecture 68 – Variable Elimination Unlimited
    • Lecture 69 – Belief Propagation Unlimited
    • Lecture 70 – Partitional Clustering Unlimited
    • Lecture 71 – Hierarchical Clustering Unlimited
    • Lecture 72 – Threshold Graphs Unlimited
    • Lecture 73 – The BIRCH Algorithm Unlimited
    • Lecture 74 – The CURE Algorithm Unlimited
    • Lecture 75 – Density based Clustering Unlimited
    • Lecture 76 – Gaussian Mixture Models Unlimited
    • Lecture 77 – Expectation Maximization Unlimited
    • Lecture 78 – Expectation Maximization (cont.) Unlimited
    • Lecture 79 – Spectral Clustering Unlimited
    • Lecture 80 – Learning Theory Unlimited
    • Lecture 81 – Frequent Itemset Mining Unlimited
    • Lecture 82 – The Apriori Property Unlimited
    • Lecture 83 – Introduction to Reinforcement Learning Unlimited
    • Lecture 84 – RL Framework and TD Learning Unlimited
    • Lecture 85 – Solution Methods and Applications Unlimited
    • Lecture 86 – Multi-class Classification Unlimited

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