NPTEL : NOC:Introduction to Machine Learning (Sponsored by Arihant) (Computer Science and Engineering)

Co-ordinators : Dr. Balaraman Ravindran


Lecture 1 - A brief introduction to machine learning

Lecture 2 - Supervised Learning

Lecture 3 - Unsupervised Learning

Lecture 4 - Reinforcement Learning

Lecture 5 - Probability Basics - 1

Lecture 6 - Probability Basics - 2

Lecture 7 - Linear Algebra - 1

Lecture 8 - Linear Algebra - 2

Lecture 9 - Statistical Decision Theory - Regression

Lecture 10 - Statistical Decision Theory - Classification

Lecture 11 - Bias-Variance

Lecture 12 - Linear Regression

Lecture 13 - Multivariate Regression

Lecture 14 - Subset Selection 1

Lecture 15 - Subset Selection 2

Lecture 16 - Shrinkage Methods

Lecture 17 - Principal Components Regression

Lecture 18 - Partial Least Squares

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 - Optimization

Lecture 25 - Perceptron Learning

Lecture 26 - SVM - Formulation

Lecture 27 - SVM - Interpretation & Analysis

Lecture 28 - SVMs for Linearly Non Separable Data

Lecture 29 - SVM Kernels

Lecture 30 - SVM - Hinge Loss Formulation

Lecture 31 - Weka Tutorial

Lecture 32 - Early Models

Lecture 33 - Backpropogation - I

Lecture 34 - Backpropogation - II

Lecture 35 - Initialization, Training and Validation

Lecture 36 - Maximum Likelihood Estimate

Lecture 37 - Priors and MAP Estimate

Lecture 38 - Bayesian Parameter Estimation

Lecture 39 - Introduction

Lecture 40 - Regression Trees

Lecture 41 - Stopping Criteria and Pruning

Lecture 42 - Loss Functions for Classification

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 - Tutorial

Lecture 48 - 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

Lecture 53 - Introduction to Hypothesis Testing

Lecture 54 - Basic Concepts

Lecture 55 - Sampling Distributions and the Z Test

Lecture 56 - Student's t-test

Lecture 57 - The Two Sample and Paired Sample t-tests

Lecture 58 - Confidence Intervals

Lecture 59 - Bagging, Committee Machines and Stacking

Lecture 60 - Boosting

Lecture 61 - Gradient Boosting

Lecture 62 - Random Forest

Lecture 63 - Naive Bayes

Lecture 64 - Bayesian Networks

Lecture 65 - Undirected Graphical Models - Introduction

Lecture 66 - Undirected Graphical Models - Potential Functions

Lecture 67 - Hidden Markov Models

Lecture 68 - Variable Elimination

Lecture 69 - Belief Propagation

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

Lecture 76 - Gaussian Mixture Models

Lecture 77 - Expectation Maximization

Lecture 78 - Expectation Maximization (Continued...)

Lecture 79 - Spectral Clustering

Lecture 80 - Learning Theory

Lecture 81 - Frequent Itemset Mining

Lecture 82 - The Apriori Property

Lecture 83 - Introduction to Reinforcement Learning

Lecture 84 - RL Framework and TD Learning

Lecture 85 - Solution Methods and Applications

Lecture 86 - Multi-class Classification