Machine Learning 82

• Lecture1.1
  What is Machine Learning? Quick Introduction
• Lecture1.2
  What is Supervised Learning ?
• Lecture1.3
  What is Unsupervised Learning ?
• Lecture1.4
  What is Reinforcement Learning ?
• Lecture1.5
  Probability Theory: Bayes’ Rule with Example
• Lecture1.6
  Probability Theory: Random variables, Expectations
• Lecture1.7
  Linear Algebra: Vector Spaces, Subspaces, Orthogonal Matrices, Quadratic Form
• Lecture1.8
  Linear Algebra: Eigenvalues, Eigenvectors, Diagonlization, SVD & Matrix Calculus
• Lecture1.9
  Statistical Decision Theory: Regression
• Lecture1.10
  Statistical Decision Theory: Classification
• Lecture1.11
  Bias-Variance
• Lecture1.12
  Linear Regression
• Lecture1.13
  Multivariate Regression
• Lecture1.14
  Dimensionality Reduction: Subset Selection (1/2)
• Lecture1.15
  Dimensionality Reduction: Subset Selection (2/2)
• Lecture1.16
  Dimensionality Reduction: Shrinkage Methods
• Lecture1.17
  Principal Components Regression
• Lecture1.18
  Partial Least Squares (PLS) Regression | Dimension Reduction
• Lecture1.19
  Linear Classification
• Lecture1.20
  Logistic Regression | Classification
• Lecture1.21
  Linear Discriminant Analysis: Classification (1/3)
• Lecture1.22
  Linear Discriminant Analysis: Classification (2/3)
• Lecture1.23
  Linear Discriminant Analysis: Classification (3/3)
• Lecture1.24
  Weka Tutorial For Beginners
• Lecture1.25
  Optimization: Problems & Algorithms
• Lecture1.26
  Perceptron Learning | Classification
• Lecture1.27
  Support Vector Machine (SVM) Formulation
• Lecture1.28
  Interpretation & Analysis | Support Vector Machine
• Lecture1.29
  Support Vector Machine for Linearly Non Separable Data
• Lecture1.30
  Support Vector Machine Kernels
• Lecture1.31
  Hinge Loss Formulation in Support Vector Machine
• Lecture1.32
  Artificial Neural Networks: Early Models
• Lecture1.33
  Artificial Neural Networks: Backpropogation 01
• Lecture1.34
  Artificial Neural Networks: Backpropogation 02
• Lecture1.35
  ANNs: Initialization, Training & Validation
• Lecture1.36
  Maximum Likelihood Estimate | Parameter Estimation
• Lecture1.37
  Priors & MAP Estimate | Parameter Estimation
• Lecture1.38
  Bayesian Parameter Estimation
• Lecture1.39
  Decision Trees: Introduction
• Lecture1.40
  Regression Trees | Decision Trees
• Lecture1.41
  Stopping Criteria & Pruning | Decision Trees
• Lecture1.42
  Loss Functions for Classification | Decision Trees
• Lecture1.43
  Categorical Attributes | Decision Trees
• Lecture1.44
  Multiway Splits | Decision Trees
• Lecture1.45
  Missing Values, Imputation & Surrogate Splits
• Lecture1.46
  Instability, Smoothness & Repeated Subtrees
• Lecture1.47
  Decision Trees Tutorial
• Lecture1.48
  Evaluation Measures
• Lecture1.49
  Bootstrapping & Cross Validation
• Lecture1.50
  2 Class Evaluation Measures
• Lecture1.51
  ROC Curve
• Lecture1.52
  Minimum Description Length & Exploratory Analysis
• Lecture1.53
  Hypothesis Testing: An Introduction
• Lecture1.54
  Hypothesis Testing: Basic Concepts
• Lecture1.55
  Sampling Distributions and the Z Test
• Lecture1.56
  Student’s t-test | Hypothesis Testing
• Lecture1.57
  Two Sample & Paired Sample t-tests | Hypothesis Testing
• Lecture1.58
  Confidence Intervals | Hypothesis Testing
• Lecture1.59
  Bagging, Committee Machines & Stacking | Ensemble Methods
• Lecture1.60
  Boosting | Ensemble Methods
• Lecture1.61
  Gradient Boosting | Ensemble Methods
• Lecture1.62
  Random Forest | Ensemble Methods
• Lecture1.63
  Naive Bayes | Graphical Models
• Lecture1.64
  Bayesian Networks | Graphical Models
• Lecture1.65
  Undirected Graphical Models: Introduction
• Lecture1.66
  Undirected Graphical Models: Potential Functions
• Lecture1.67
  Hidden Markov Models: Graphical Models
• Lecture1.68
  Variable Elimination | Graphical Models
• Lecture1.69
  Belief Propagation | Graphical Models
• Lecture1.70
  Partitional Clustering
• Lecture1.71
  Hierarchical Clustering
• Lecture1.72
  Threshold Graphs | Clustering
• Lecture1.73
  BIRCH Algorithm | Clustering
• Lecture1.74
  CURE Algorithm | Clustering
• Lecture1.75
  Density Based Clustering
• Lecture1.76
  Gaussian Mixture Model
• Lecture1.77
  Expectation Maximization Algorithm with Example
• Lecture1.78
  Expectation Maximization Algorithm (Part 02)
• Lecture1.79
  Spectral Clustering Algorithm Explained
• Lecture1.80
  Computational Learning Theory
• Lecture1.81
  Frequent Itemset Mining
• Lecture1.82
  Apriori Property | Frequent Itemset Mining
• Lecture1.83
  Reinforcement Learning: Introduction
• Lecture1.84
  RL Framework, Temporal Difference (TD) Learning
• Lecture1.85
  Solution Methods & Applications | Reinforcement Learning
• Lecture1.86
  Multiclass Classification