NOC:Introduction to Machine Learning (Sponsored by Arihant)


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