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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 60 - Boosting


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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