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دورة تعلم الآلة من NPTEL 2016 | تقديم بلالارامان رافيندران
Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees

Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees

دورة تعلم الآلة من NPTEL 2016 | تقديم بلالارامان رافيندران

الدرس السابق الدرس التالي

محتوى الكورس

جميع الدروس
88 درس
01:59 Introduction to Machine Learning
15:28 Week 1 - Lecture 1 - Introduction to Machine Learning
24:36 Week 1 Lecture 2 - Supervised Learning
08:52 Week 1 Lecture 3 - Unsupervised Learning
08:46 Week 1 Lecture 4 - Reinforcement Learning
41:05 Week 2 Lecture 5 - Statistical Decision Theory - Regression
19:21 Week 2 Lecture 6 - Statistical Decision Theory - Classification
17:07 Week 2 Lecture 7 - Bias - Variance
23:40 Week 2 Lecture 8 - Linear Regression
29:52 Week 2 Lecture 9 - Multivariate Regression
15:49 Week 3 Lecture 10 Subset Selection 1
23:44 Week 3 Lecture 11 Subset Selection 2
14:53 Week 3 Lecture 12 Shrinkage Methods
14:28 Week 3 Lecture 13 Principal Components Regression
14:35 Week 3 Lecture 14 Partial Least Squares
24:10 Week 3 Lecture 15 Linear Classification
38:17 Week 3 Lecture 16 Logistic Regression
16:22 Week 3 Lecture 17 Linear Discriminant Analysis 1
20:59 Week 3 Lecture 18 Linear Discriminant Analysis 2
25:00 Week 3 Lecture 19 Linear Discriminant Analysis 3
32:01 Week 4 Lecture 20 Perceptron Learning
15:47 Week 4 Lecture 21 SVM - Formulation
19:27 Week 4 Lecture 22 SVM - Interpretation & Analysis
30:56 Week 4 Lecture 23 SVMs for Linearly Non Separable Data
17:50 Week 4 Lecture 24 SVM Kernels
14:42 Week 4 Lecture 25 SVM - Hinge Loss Formulation
29:44 Week 5 Lecture 26 ANN I - Early Models
25:14 Week 5 Lecture 27 ANN II - Backprogpogation I
21:30 Week 5 Lecture 28 ANN III - Backpropogation II
31:13 Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
14:27 MAXIMUM LIKELIHOOD ESTIMATE
13:08 Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
21:54 Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
18:08 Week 6 Lecture 33 Decision Trees - Introduction
30:57 Week 6 Lecture 34 Regression Trees
23:37 Week 6 Lecture 35 Stopping Criteria & Pruning
17:38 Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
13:46 Week 6 Lecture 37 Decision Trees - Categorical Attributes
16:27 Week 6 Lecture 38 Decision Trees - Multiway Splits
21:32 Week 6 Lecture 39 Decision Trees - Missing Values, Imputation & Surrogate Splits
13:08 Week 6 Lecture 40 Decision Trees - Instability, Smoothness & Repeated Subtrees
19:59 Week 6 Lecture 41 Decision Trees - Example
17:46 Week 6 Lecture 42 Evaluation Measures 1
17:17 Week 6 Lecture 43 Bootstrapping & Cross Validation
20:55 Week 6 Lecture 44 - 2 Class Evaluation Measures
26:03 Week 6 Lecture 45 - The ROC Curve
11:19 Week 6 Lecture 46 - Minimum Description Length & Exploratory Analysis
19:52 Week 7 Lecture 47 - Introduction to Hypothesis Testing
27:34 Week 7 Lecture 48 - Basic Concepts
28:55 Week 7 Lecture 49 - Hypothesis Testing II - Sampling Distributions & The Z test
16:38 Introduction to Machine Learning
16:52 Week 1 - Lecture 1 - Introduction to Machine Learning
14:03 Week 1 Lecture 2 - Supervised Learning
31:14 Week 1 Lecture 3 - Unsupervised Learning
35:52 Week 1 Lecture 4 - Reinforcement Learning
40:24 Week 2 Lecture 5 - Statistical Decision Theory - Regression
05:47 Week 2 Lecture 6 - Statistical Decision Theory - Classification
29:04 Week 2 Lecture 7 - Bias - Variance
40:01 Week 2 Lecture 8 - Linear Regression
37:55 Week 2 Lecture 9 - Multivariate Regression
27:39 Week 3 Lecture 10 Subset Selection 1
10:32 Week 3 Lecture 11 Subset Selection 2
32:33 Week 3 Lecture 12 Shrinkage Methods
16:37 Week 3 Lecture 13 Principal Components Regression
16:18 Week 3 Lecture 14 Partial Least Squares
51:23 Week 3 Lecture 15 Linear Classification
15:21 Week 3 Lecture 16 Logistic Regression
33:37 Week 3 Lecture 17 Linear Discriminant Analysis 1
21:11 Week 3 Lecture 18 Linear Discriminant Analysis 2
20:01 Week 3 Lecture 19 Linear Discriminant Analysis 3
17:51 Week 4 Lecture 20 Perceptron Learning
44:06 Week 4 Lecture 21 SVM - Formulation
32:25 Week 4 Lecture 22 SVM - Interpretation & Analysis
38:20 Week 4 Lecture 23 SVMs for Linearly Non Separable Data
04:33 Week 4 Lecture 24 SVM Kernels
41:27 Week 4 Lecture 25 SVM - Hinge Loss Formulation
27:22 Week 5 Lecture 26 ANN I - Early Models
22:51 Week 5 Lecture 27 ANN II - Backprogpogation I
27:41 Week 5 Lecture 28 ANN III - Backpropogation II
39:07 Week 5 Lecture 29 ANN IV - Initialization, Training & Validation
12:57 MAXIMUM LIKELIHOOD ESTIMATE
20:02 Week 5 Lecture 31 Parameter Estimation II - Priors & MAP
35:51 Week 5 Lecture 32 Parameter Estimation III - Bayesian Estimation
25:21 Week 6 Lecture 33 Decision Trees - Introduction
20:17 Week 6 Lecture 34 Regression Trees
21:40 Week 6 Lecture 35 Stopping Criteria & Pruning
28:52 Week 6 Lecture 36 Decision Trees for Classification - Loss Functions
23:02 Week 6 Lecture 37 Decision Trees - Categorical Attributes