Week 6 Lecture 42 Evaluation Measures 1
Machine Learning- Balaraman Ravindran
Week 6 Lecture 42 Evaluation Measures 1
17:46
Week 3 Lecture 19 Linear Discriminant Analysis 3
Machine Learning- Balaraman Ravindran
Week 3 Lecture 19 Linear Discriminant Analysis 3
25:00
Week 3 Lecture 10 Subset Selection 1
Machine Learning- Balaraman Ravindran
Week 3 Lecture 10 Subset Selection 1
15:49
Week 5 Lecture 27 ANN II - Backprogpogation I
Machine Learning- Balaraman Ravindran
Week 5 Lecture 27 ANN II - Backprogpogation I
25:14
Week 1 Lecture 2 - Supervised Learning
Machine Learning- Balaraman Ravindran
Week 1 Lecture 2 - Supervised Learning
24:36
Week 4 Lecture 24 SVM Kernels
Machine Learning- Balaraman Ravindran
Week 4 Lecture 24 SVM Kernels
17:50
Week 3 Weka Tutorial
Machine Learning- Balaraman Ravindran
Week 3 Weka Tutorial
25:21
Week 3 Lecture 18 Linear Discriminant Analysis 2
Machine Learning- Balaraman Ravindran
Week 3 Lecture 18 Linear Discriminant Analysis 2
20:59
Week 4 Lecture 23 SVMs for Linearly Non Separable Data
Machine Learning- Balaraman Ravindran
Week 4 Lecture 23 SVMs for Linearly Non Separable Data
30:56
Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
Machine Learning- Balaraman Ravindran
Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
21:54
Week 3 Lecture 14 Partial Least Squares
Machine Learning- Balaraman Ravindran
Week 3 Lecture 14 Partial Least Squares
14:35
Week 6 Lecture 37 Decision Trees - Categorical Attributes
Machine Learning- Balaraman Ravindran
Week 6 Lecture 37 Decision Trees - Categorical Attributes
13:46
Week 5 Lecture 28 ANN III - Backpropogation II
Machine Learning- Balaraman Ravindran
Week 5 Lecture 28 ANN III - Backpropogation II
21:30
Week 4 Lecture 25 SVM - Hinge Loss Formulation
Machine Learning- Balaraman Ravindran
Week 4 Lecture 25 SVM - Hinge Loss Formulation
14:42
Week 1 Lecture 4 - Reinforcement Learning
Machine Learning- Balaraman Ravindran
Week 1 Lecture 4 - Reinforcement Learning
8:46
Week 4 Lecture 22 SVM - Interpretation & Analysis
Machine Learning- Balaraman Ravindran
Week 4 Lecture 22 SVM - Interpretation & Analysis
19:27
Week 6 Lecture 34 Regression Trees
Machine Learning- Balaraman Ravindran
Week 6 Lecture 34 Regression Trees
30:57
Week 3 Lecture 13 Principal Components Regression
Machine Learning- Balaraman Ravindran
Week 3 Lecture 13 Principal Components Regression
14:28
Week 1 Lecture 3 - Unsupervised Learning
Machine Learning- Balaraman Ravindran
Week 1 Lecture 3 - Unsupervised Learning
8:52
Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
Machine Learning- Balaraman Ravindran
Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
17:38
Week 3 Lecture 12 Shrinkage Methods
Machine Learning- Balaraman Ravindran
Week 3 Lecture 12 Shrinkage Methods
14:53
Week 2 Lecture 6 - Statistical Decision Theory - Classification
Machine Learning- Balaraman Ravindran
Week 2 Lecture 6 - Statistical Decision Theory - Classification
19:21
Week 3 Lecture 17 Linear Discriminant Analysis 1
Machine Learning- Balaraman Ravindran
Week 3 Lecture 17 Linear Discriminant Analysis 1
16:22
Week 2 Lecture 7 - Bias - Variance
Machine Learning- Balaraman Ravindran
Week 2 Lecture 7 - Bias - Variance
17:07
Week 2 Lecture 8 - Linear Regression
Machine Learning- Balaraman Ravindran
Week 2 Lecture 8 - Linear Regression
23:40
Week 4 Lecture 20 Perceptron Learning
Machine Learning- Balaraman Ravindran
Week 4 Lecture 20 Perceptron Learning
32:01
Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
Machine Learning- Balaraman Ravindran
Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
31:13
Week 3 Lecture 15 Linear Classification
Machine Learning- Balaraman Ravindran
Week 3 Lecture 15 Linear Classification
24:10
Week 3 Lecture 11 Subset Selection 2
Machine Learning- Balaraman Ravindran
Week 3 Lecture 11 Subset Selection 2
23:44
Week 3 Lecture 16 Logistic Regression
Machine Learning- Balaraman Ravindran
Week 3 Lecture 16 Logistic Regression
38:17
Week 6 Lecture 38 Decision Trees - Multiway Splits
Machine Learning- Balaraman Ravindran
Week 6 Lecture 38 Decision Trees - Multiway Splits
16:27
Week 4 Lecture 21 SVM - Formulation
Machine Learning- Balaraman Ravindran
Week 4 Lecture 21 SVM - Formulation
15:47
Week 2 Lecture 9 - Multivariate Regression
Machine Learning- Balaraman Ravindran
Week 2 Lecture 9 - Multivariate Regression
29:52
Week 5 Lecture 26 ANN I - Early Models
Machine Learning- Balaraman Ravindran
Week 5 Lecture 26 ANN I - Early Models
29:44
Week 2 Lecture 5 - Statistical Decision Theory - Regression
Machine Learning- Balaraman Ravindran
Week 2 Lecture 5 - Statistical Decision Theory - Regression
41:05
Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
Machine Learning- Balaraman Ravindran
Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
13:08
MAXIMUM LIKELIHOOD ESTIMATE
Machine Learning- Balaraman Ravindran
MAXIMUM LIKELIHOOD ESTIMATE
14:27
Week 6 Lecture 33 Decision Trees - Introduction
Machine Learning- Balaraman Ravindran
Week 6 Lecture 33 Decision Trees - Introduction
18:08
Week 6 Lecture 35 Stopping Criteria & Pruning
Machine Learning- Balaraman Ravindran
Week 6 Lecture 35 Stopping Criteria & Pruning
23:37
Week 10 Lecture 69 The CURE Algorithm
Machine Learning- Balaraman Ravindran
Week 10 Lecture 69 The CURE Algorithm
20:01
Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
Machine Learning- Balaraman Ravindran
Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
11:19
Week 10 Lecture 67 Threshold Graphs
Machine Learning- Balaraman Ravindran
Week 10 Lecture 67 Threshold Graphs
33:37
Week 6 Decision Trees Tutorial
Machine Learning- Balaraman Ravindran
Week 6 Decision Trees Tutorial
20:02
Week 7 Lecture 52 - Confidence Intervals
Machine Learning- Balaraman Ravindran
Week 7 Lecture 52 - Confidence Intervals
14:03
Week 9 Lecture 59 Undirected Graphical Models - Introduction
Machine Learning- Balaraman Ravindran
Week 9 Lecture 59 Undirected Graphical Models - Introduction
37:55
Week 6 Lecture 41 Decision Trees - Example
Machine Learning- Balaraman Ravindran
Week 6 Lecture 41 Decision Trees - Example
19:59
Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
Machine Learning- Balaraman Ravindran
Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
13:08
Week 11 Lecture 72 Expectation Maximization
Machine Learning- Balaraman Ravindran
Week 11 Lecture 72 Expectation Maximization
32:25
Lecture 81 - RL Framework and TD Learning
Machine Learning- Balaraman Ravindran
Lecture 81 - RL Framework and TD Learning
39:07
The Apriori Property
Machine Learning- Balaraman Ravindran
The Apriori Property
41:27
Week 9 Lecture 63 Belief Propagation
Machine Learning- Balaraman Ravindran
Week 9 Lecture 63 Belief Propagation
16:37
Lecture 64 Multi-class Classification
Machine Learning- Balaraman Ravindran
Lecture 64 Multi-class Classification
16:18
Week 9 Lecture 58 Bayesian Networks
Machine Learning- Balaraman Ravindran
Week 9 Lecture 58 Bayesian Networks
40:01
Week 7 Lecture 48 - Basic Concepts
Machine Learning- Balaraman Ravindran
Week 7 Lecture 48 - Basic Concepts
27:34
Week 7 Lecture 50 -STUDENT'S T-TEST
Machine Learning- Balaraman Ravindran
Week 7 Lecture 50 -STUDENT'S T-TEST
16:38
Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking
Machine Learning- Balaraman Ravindran
Week 8 Lecture 53 - Ensemble Methods - Bagging, Committee Machines and Stacking
31:14
Week 9 Lecture 62 Variable Elimination
Machine Learning- Balaraman Ravindran
Week 9 Lecture 62 Variable Elimination
32:33
Week 10 Lecture 70 Density Based Clustering
Machine Learning- Balaraman Ravindran
Week 10 Lecture 70 Density Based Clustering
17:51
Week 6 Lecture 44 - 2 Class Evaluation Measures
Machine Learning- Balaraman Ravindran
Week 6 Lecture 44 - 2 Class Evaluation Measures
20:55
Lecture 79 Learning Theory
Machine Learning- Balaraman Ravindran
Lecture 79 Learning Theory
1:22:51
Week 10 Lecture 65 Partional Clustering
Machine Learning- Balaraman Ravindran
Week 10 Lecture 65 Partional Clustering
51:23
Lecture 80 Introduction to Reinforcement Learning
Machine Learning- Balaraman Ravindran
Lecture 80 Introduction to Reinforcement Learning
27:41
Week 8 Lecture 57 - Naive Bayes
Machine Learning- Balaraman Ravindran
Week 8 Lecture 57 - Naive Bayes
29:04
Week 8 Lecture 56 - Random Forests
Machine Learning- Balaraman Ravindran
Week 8 Lecture 56 - Random Forests
5:47
Week 6 Lecture 43 Bootstrapping & Cross Validation
Machine Learning- Balaraman Ravindran
Week 6 Lecture 43 Bootstrapping & Cross Validation
17:17
Week 6 Lecture 45 - The ROC Curve
Machine Learning- Balaraman Ravindran
Week 6 Lecture 45 - The ROC Curve
26:03
Lecture 76 Spectral Clustering
Machine Learning- Balaraman Ravindran
Lecture 76 Spectral Clustering
1:04:33
Week 11 Lecture 71 Gaussian Mixture Models
Machine Learning- Balaraman Ravindran
Week 11 Lecture 71 Gaussian Mixture Models
44:06
Lecture 82 Solution Methods & Applications
Machine Learning- Balaraman Ravindran
Lecture 82 Solution Methods & Applications
12:57
Week 11 Lecture 73 Expectation Maximization Continued
Machine Learning- Balaraman Ravindran
Week 11 Lecture 73 Expectation Maximization Continued
38:20
Week 8 Lecture 54 - Boosting
Machine Learning- Balaraman Ravindran
Week 8 Lecture 54 - Boosting
35:52
Week 7 Lecture 47 - Introduction to Hypothesis Testing
Machine Learning- Balaraman Ravindran
Week 7 Lecture 47 - Introduction to Hypothesis Testing
19:52
Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
Machine Learning- Balaraman Ravindran
Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
28:55
Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests
Machine Learning- Balaraman Ravindran
Week 7 Lecture 51 - Hypothesis Testing IV - The Two Sample and Paired Sample t-tests
16:52
Week 10 Lecture 68 The BIRCH Algorithm
Machine Learning- Balaraman Ravindran
Week 10 Lecture 68 The BIRCH Algorithm
21:11
Week 8 Lecture 55 - Gradient Boosting
Machine Learning- Balaraman Ravindran
Week 8 Lecture 55 - Gradient Boosting
40:24
Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
Machine Learning- Balaraman Ravindran
Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
21:32
Frequent Itemset Mining
Machine Learning- Balaraman Ravindran
Frequent Itemset Mining
27:22
Week 8 Lecture 60 Undirected Graphical Models - Potential Functions
Machine Learning- Balaraman Ravindran
Week 8 Lecture 60 Undirected Graphical Models - Potential Functions
27:39
Week 9 Lecture 61 Hidden Markov Models
Machine Learning- Balaraman Ravindran
Week 9 Lecture 61 Hidden Markov Models
10:32
Week 10 Lecture 66 Hierarchical Clustering
Machine Learning- Balaraman Ravindran
Week 10 Lecture 66 Hierarchical Clustering
15:21
Week 1 Tutorial 1 - Probability Basics (2)
Machine Learning- Balaraman Ravindran
Week 1 Tutorial 1 - Probability Basics (2)
28:52
Week 1 Tutorial 1 - Probability Basics (1)
Machine Learning- Balaraman Ravindran
Week 1 Tutorial 1 - Probability Basics (1)
23:02
Week 2 Tutorial 2 - Linear Algebra (1)
Machine Learning- Balaraman Ravindran
Week 2 Tutorial 2 - Linear Algebra (1)
21:40
Week 2 Tutorial 2 - Linear Algebra (2)
Machine Learning- Balaraman Ravindran
Week 2 Tutorial 2 - Linear Algebra (2)
20:17
Introduction to Machine Learning
Machine Learning- Balaraman Ravindran
Introduction to Machine Learning
1:59
Week 4 Tutorial 4 - Optimization
Machine Learning- Balaraman Ravindran
Week 4 Tutorial 4 - Optimization
35:51
Week 1 - Lecture 1 - Introduction to Machine Learning
Machine Learning- Balaraman Ravindran
Week 1 - Lecture 1 - Introduction to Machine Learning
15:28