**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 |