| 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 |