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Statistical Learning with R

by Stanford Online · 104 videos

Total watch time

20h 28m

at 1× speed · exactly 20 hours, 28 minutes, 12 seconds at 1×

1×20h 28m 12s
1.25×16h 22m 34s
1.5×13h 38m 48s
1.75×11h 41m 50s
2×10h 14m 6s
Average video11m 49s
Longest29m 34s
Shortest2m 19s
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Videos (104)

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1 Statistical Learning: 1.1 Opening Remarks 18:19 2022-10-07
2 Statistical Learning: 8 Years Later (Second Edition of the Course) 2:19 2022-10-07
3 Statistical Learning: 1.2 Examples and Framework 12:13 2022-10-07
4 Statistical Learning: 2.1 Introduction to Regression Models 11:42 2022-10-07
5 Statistical Learning: 2.2 Dimensionality and Structured Models 11:41 2022-10-07
6 Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff 10:05 2022-10-07
7 Statistical Learning: 2.4 Classification 15:38 2022-10-07
8 Statistical Learning: 2.R Introduction to R 14:13 2022-10-07
9 Statistical Learning: 3.1 Simple linear regression 13:02 2022-10-07
10 Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals 8:25 2022-10-07
11 Statistical Learning: 3.3 Multiple Linear Regression 15:38 2022-10-07
12 Statistical Learning: 3.4 Some important questions 14:52 2022-10-07
13 Statistical Learning: 3.5 Extensions of the Linear Model 14:17 2022-10-07
14 Statistical Learning: 3.R Regression in R 22:11 2022-10-07
15 Statistical Learning: 4.1 Introduction to Classification Problems 10:26 2022-10-07
16 Statistical Learning: 4.2 Logistic Regression 9:08 2022-10-07
17 Statistical Learning: 4.3 Multivariate Logistic Regression 9:54 2022-10-07
18 Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass 7:29 2022-10-07
19 Statistical Learning: 4.5 Discriminant Analysis 7:13 2022-10-07
20 Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable) 7:38 2022-10-07
21 Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables) 17:43 2022-10-07
22 Statistical Learning: 4.8 Generalized Linear Models 9:35 2022-10-07
23 Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes 10:08 2022-10-07
24 Statistical Learning: 4.R.1 Logistic Regression 10:15 2022-10-07
25 Statistical Learning: 4.R.2 Linear Discriminant Analysis 8:23 2022-10-07
26 Statistical Learning: 4.R.3 Nearest Neighbor Classification 5:02 2022-10-07
27 Statistical Learning: 5.1 Cross Validation 14:02 2022-10-07
28 Statistical Learning: 5.2 K-fold Cross Validation 13:34 2022-10-07
29 Statistical Learning: 5.3 Cross Validation the wrong and right way 10:08 2022-10-07
30 Statistical Learning: 5.4 The Bootstrap 11:30 2022-10-07
31 Statistical Learning: 5.5 More on the Bootstrap 14:36 2022-10-07
32 Statistical Learning: 5.R.1 Cross Validation 11:22 2022-10-07
33 Statistical Learning: 5.R.2 Bootstrap 7:41 2022-10-07
34 Statistical Learning: 6.1 Introduction and Best Subset Selection 13:45 2022-10-07
35 Statistical Learning: 6.2 Stepwise Selection 12:27 2022-10-07
36 Statistical Learning: 6.3 Backward stepwise selection 5:27 2022-10-07
37 Statistical Learning: 6.4 Estimating test error 14:07 2022-10-07
38 Statistical Learning: 6.5 Validation and cross validation 8:44 2022-10-07
39 Statistical Learning: 6.6 Shrinkage methods and ridge regression 12:38 2022-10-07
40 Statistical Learning: 6.7 The Lasso 15:22 2022-10-07
41 Statistical Learning: 6.8 Tuning parameter selection 5:28 2022-10-07
42 Statistical Learning: 6.9 Dimension Reduction Methods 4:46 2022-10-07
43 Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares 15:49 2022-10-07
44 Statistical Learning: 6.R.1 Markdown in RStudio and Best Subset Regression 10:37 2022-10-07
45 Statistical Learning: 6.R.2 Forward Stepwise Regression 10:33 2022-10-07
46 Statistical Learning: 6.R.3 Model Selection and Cross-Validation 5:33 2022-10-07
47 Statistical Learning: 6.R.4 Ridge Regression and Lasso 16:35 2022-10-07
48 Statistical Learning: 7.1 Polynomials and Step Functions 15:00 2022-10-07
49 Statistical Learning: 7.2 Piecewise Polynomials and Splines 13:14 2022-10-07
50 Statistical Learning: 7.3 Smoothing Splines 10:11 2022-10-07
51 Statistical Learning: 7.4 Generalized Additive Models and Local Regression 10:46 2022-10-07
52 Statistical Learning: 7.R.1 Polynomials in GLMs 21:12 2022-10-07
53 Statistical Learning: 7.R.2 Splines and GAMs 12:16 2022-10-07
54 Statistical Learning: 8.1 Tree based methods 14:38 2022-10-07
55 Statistical Learning: 8.2 More details on Trees 11:46 2022-10-07
56 Statistical Learning: 8.3 Classification Trees 11:01 2022-10-07
57 Statistical Learning: 8.4 Bagging 13:46 2022-10-07
58 Statistical Learning: 8.5 Boosting 12:03 2022-10-07
59 Statistical Learning: 8.6 Bayesian Additive Regression Trees 11:34 2022-10-07
60 Statistical Learning: 8.R.1 Fitting Trees 10:14 2022-10-07
61 Statistical Learning: 8.R.2 Random Forests and Boosting 15:36 2022-10-07
62 Statistical Learning: 9.1 Optimal Separating Hyperplane 11:36 2022-10-07
63 Statistical Learning: 9.2.Support Vector Classifier 8:05 2022-10-07
64 Statistical Learning: 9.3 Feature Expansion and the SVM 15:05 2022-10-07
65 Statistical Learning: 9.4 Example and Comparison with Logistic Regression 14:48 2022-10-07
66 Statistical Learning: 9.R.1 Support Vector Classifier 10:14 2022-10-07
67 Statistical Learning: 9.R.2 Nonlinear Support Vector Machine 7:55 2022-10-07
68 Statistical Learning: 10.1 Introduction to Neural Networks 15:31 2022-10-07
69 Statistical Learning: 10.2 Convolutional Neural Networks 17:09 2022-10-07
70 Statistical Learning: 10.3 Document Classification 7:47 2022-10-07
71 Statistical Learning: 10.4 Recurrent Neural Networks 14:45 2022-10-07
72 Statistical Learning: 10.5 Time Series Forecasting 16:51 2022-10-07
73 Statistical Learning: 10.6 Fitting Neural Networks 17:04 2022-10-07
74 Statistical Learning: 10.7 Interpolation and Double Descent 11:12 2022-10-07
75 Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data 29:34 2022-10-07
76 Statistical Learning: 10.R.2 Convolutional Neural Networks in R 13:20 2022-10-07
77 Statistical Learning: 10.R.3 Document Classification 8:28 2022-10-07
78 Statistical Learning: 10.R.4 Recurrent Neural Networks 15:05 2022-10-07
79 Statistical Learning: 11.1 Introduction to Survival Data and Censoring 14:11 2022-10-07
80 Statistical Learning: 11.2 Proportional Hazards Model 14:32 2022-10-07
81 Statistical Learning: 11.3 Estimation of Cox Model with Examples 13:43 2022-10-07
82 Statistical Learning: 11.4 Model Evaluation and Further Topics 6:13 2022-10-07
83 Statistical Learning: 11.R.1 Survival Curves Brain Cancer Data 12:19 2022-10-07
84 Statistical Learning: 11.R.2 Cox Models I Publication Data 4:21 2022-10-07
85 Statistical Learning: 11.R.3 Cox Models II Call Center Data 7:58 2022-10-07
86 Statistical Learning: 12.1 Principal Components 12:37 2022-10-07
87 Statistical Learning: 12.2 Higher order principal components 17:40 2022-10-07
88 Statistical Learning: 12.3 k means Clustering 17:18 2022-10-07
89 Statistical Learning: 12.4 Hierarchical Clustering 14:46 2022-10-07
90 Statistical Learning: 12.5 Matrix Completion 15:52 2022-10-07
91 Statistical Learning: 12.6 Breast Cancer Example 9:25 2022-10-07
92 Statistical Learning: 12.R.1 Principal Components 6:29 2022-10-07
93 Statistical Learning: 12.R.2 K means Clustering 6:32 2022-10-07
94 Statistical Learning: 12.R.3 Hierarchical Clustering 6:34 2022-10-07
95 Statistical Learning: 13.1 Introduction to Hypothesis Testing 14:31 2022-10-07
96 Statistical Learning: 13.1 Introduction to Hypothesis Testing II 10:18 2022-10-07
97 Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate 12:31 2022-10-07
98 Statistical Learning: 13.3 Bonferroni Method for Controlling FWER 6:32 2022-10-07
99 Statistical Learning: 13.4 Holm's Method for Controlling FWER 5:57 2022-10-07
100 Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method 11:14 2022-10-07
101 Statistical Learning: 13.6 Resampling Approaches 3:21 2022-10-07
102 Statistical Learning: 13.6 Resampling Approaches II 7:44 2022-10-07
103 Statistical Learning: 13.R.1 Bonferroni and Holm 11:45 2022-10-07
104 Statistical Learning: 13.R.1 Bonferroni and Holm II 13:50 2022-10-07

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