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)

Lecture 54 - Hypothesis Testing: Basic Concepts

Go to the Course Home or watch other lectures:

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