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Week 3 Lecture 18 Linear Discriminant Analysis 2
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Classroom Contents
Machine Learning
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- 1 Introduction to Machine Learning
- 2 Week 1 - Lecture 1 - Introduction to Machine Learning
- 3 Week 1 Lecture 2 - Supervised Learning
- 4 Week 1 Lecture 3 - Unsupervised Learning
- 5 Week 1 Lecture 4 - Reinforcement Learning
- 6 Week 2 Lecture 5 - Statistical Decision Theory - Regression
- 7 Week 2 Lecture 6 - Statistical Decision Theory - Classification
- 8 Week 2 Lecture 7 - Bias - Variance
- 9 Week 2 Lecture 8 - Linear Regression
- 10 Week 2 Lecture 9 - Multivariate Regression
- 11 Week 3 Lecture 10 Subset Selection 1
- 12 Week 3 Lecture 11 Subset Selection 2
- 13 Week 3 Lecture 12 Shrinkage Methods
- 14 Week 3 Lecture 13 Principal Components Regression
- 15 Week 3 Lecture 14 Partial Least Squares
- 16 Week 3 Lecture 15 Linear Classification
- 17 Week 3 Lecture 16 Logistic Regression
- 18 Week 3 Lecture 17 Linear Discriminant Analysis 1
- 19 Week 3 Lecture 18 Linear Discriminant Analysis 2
- 20 Week 3 Lecture 19 Linear Discriminant Analysis 3
- 21 Week 4 Lecture 20 Perceptron Learning
- 22 Week 4 Lecture 21 SVM - Formulation
- 23 Week 4 Lecture 22 SVM - Interpretation & Analysis
- 24 Week 4 Lecture 23 SVMs for Linearly Non Separable Data
- 25 Week 4 Lecture 24 SVM Kernels
- 26 Week 4 Lecture 25 SVM - Hinge Loss Formulation
- 27 Week 5 Lecture 26 ANN I - Early Models
- 28 Week 5 Lecture 27 ANN II - Backprogpogation I
- 29 Week 5 Lecture 28 ANN III - Backpropogation II
- 30 Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
- 31 MAXIMUM LIKELIHOOD ESTIMATE
- 32 Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
- 33 Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
- 34 Week 6 Lecture 33 Decision Trees - Introduction
- 35 Week 6 Lecture 34 Regression Trees
- 36 Week 6 Lecture 35 Stopping Criteria & Pruning
- 37 Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
- 38 Week 6 Lecture 37 Decision Trees - Categorical Attributes
- 39 Week 6 Lecture 38 Decision Trees - Multiway Splits
- 40 Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
- 41 Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
- 42 Week 6 Lecture 41 Decision Trees - Example
- 43 Week 6 Lecture 42 Evaluation Measures 1
- 44 Week 6 Lecture 43 Bootstrapping & Cross Validation
- 45 Week 6 Lecture 44 - 2 Class Evaluation Measures
- 46 Week 6 Lecture 45 - The ROC Curve
- 47 Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
- 48 Week 7 Lecture 47 - Introduction to Hypothesis Testing
- 49 Week 7 Lecture 48 - Basic Concepts
- 50 Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
- 51 Week 7 Lecture 50 -STUDENT'S T-TEST
- 52 Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests
- 53 Week 7 Lecture 52 - Confidence Intervals
- 54 Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking
- 55 Week 8 Lecture 54 - Boosting
- 56 Week 8 Lecture 55 - Gradient Boosting
- 57 Week 8 Lecture 56 - Random Forests
- 58 Week 8 Lecture 57 - Naive Bayes
- 59 Week 9 Lecture 58 Bayesian Networks
- 60 Week 9 Lecture 59 Undirected Graphical Models - Introduction
- 61 Week 8 Lecture 60 Undirected Graphical Models - Potential Functions
- 62 Week 9 Lecture 61 Hidden Markov Models
- 63 Week 9 Lecture 62 Variable Elimination
- 64 Week 9 Lecture 63 Belief Propagation
- 65 Lecture 64 Multi-class Classification
- 66 Week 10 Lecture 65 Partional Clustering
- 67 Week 10 Lecture 66 Hierarchical Clustering
- 68 Week 10 Lecture 67 Threshold Graphs
- 69 Week 10 Lecture 68 The BIRCH Algorithm
- 70 Week 10 Lecture 69 The CURE Algorithm
- 71 Week 10 Lecture 70 Density Based Clustering
- 72 Week 11 Lecture 71 Gaussian Mixture Models
- 73 Week 11 Lecture 72 Expectation Maximization
- 74 Week 11 Lecture 73 Expectation Maximization Continued
- 75 Lecture 76 Spectral Clustering
- 76 The Apriori Property
- 77 Frequent Itemset Mining
- 78 Lecture 79 Learning Theory
- 79 Lecture 80 Introduction to Reinforcement Learning
- 80 Lecture 81 - RL Framework and TD Learning
- 81 Lecture 82 Solution Methods & Applications
- 82 Week 6 Decision Trees Tutorial
- 83 Week 4 Tutorial 4 - Optimization
- 84 Week 3 Weka Tutorial
- 85 Week 2 Tutorial 2 - Linear Algebra (2)
- 86 Week 2 Tutorial 2 - Linear Algebra (1)
- 87 Week 1 Tutorial 1 - Probability Basics (2)
- 88 Week 1 Tutorial 1 - Probability Basics (1)
