# InfoCoBuild

## Introduction to Machine Learning

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)

 A Brief Introduction to Machine Learning

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

 References Introduction to Machine Learning Instructor: Dr. Balaraman Ravindran, Department of Computer Science and Engineering, IIT Madras. This course introduces some of the basic concepts of machine learning from a mathematically well motivated perspective.