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)