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Statistical Learning with Python

by Stanford Online · 108 videos

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20h 18m

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1.25×16h 14m 34s
1.5×13h 32m 8s
1.75×11h 36m 7s
2×10h 9m 6s
Average video11m 17s
Longest19m 9s
Shortest1m 48s
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Videos (108)

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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 I Introducing Jonathan - Third Edition of the Course I 2023 1:48 2023-12-05
4 Statistical Learning: 1.2 Examples and Framework 12:13 2022-10-07
5 Statistical Learning: 2.1 Introduction to Regression Models 11:42 2022-10-07
6 Statistical Learning: 2.2 Dimensionality and Structured Models 11:41 2022-10-07
7 Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff 10:05 2022-10-07
8 Statistical Learning: 2.4 Classification 15:38 2022-10-07
9 Statistical Learning: 2.Py Setting Up Python I 2023 5:12 2023-12-05
10 Statistical Learning: 2.Py Data Types, Arrays, and Basics I 2023 16:42 2023-12-05
11 Statistical Learning: 2.Py.3 Graphics I 2023 6:07 2023-12-05
12 Statistical Learning: 2.Py Indexing and Dataframes I 2023 10:20 2023-12-05
13 Statistical Learning: 3.1 Simple linear regression 13:02 2022-10-07
14 Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals 8:25 2022-10-07
15 Statistical Learning: 3.3 Multiple Linear Regression 15:38 2022-10-07
16 Statistical Learning: 3.4 Some important questions 14:52 2022-10-07
17 Statistical Learning: 3.5 Extensions of the Linear Model 14:17 2022-10-07
18 Statistical Learning: 3.Py Linear Regression and statsmodels Package I 2023 9:10 2023-12-05
19 Statistical Learning: 3.Py Multiple Linear Regression Package I 2023 2:26 2023-12-05
20 Statistical Learning: 3.Py Interactions, Qualitative Predictors and Other Details I 2023 6:33 2023-12-05
21 Statistical Learning: 4.1 Introduction to Classification Problems 10:26 2022-10-07
22 Statistical Learning: 4.2 Logistic Regression 9:08 2022-10-07
23 Statistical Learning: 4.3 Multivariate Logistic Regression 9:54 2022-10-07
24 Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass 7:29 2022-10-07
25 Statistical Learning: 4.5 Discriminant Analysis 7:13 2022-10-07
26 Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable) 7:38 2022-10-07
27 Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables) 17:43 2022-10-07
28 Statistical Learning: 4.8 Generalized Linear Models 9:35 2022-10-07
29 Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes 10:08 2022-10-07
30 Statistical Learning: 4.Py Logistic Regression I 2023 11:44 2023-12-05
31 Statistical Learning: 4.Py Linear Discriminant Analysis (LDA) I 2023 9:58 2023-12-05
32 Statistical Learning: 4.Py K-Nearest Neighbors (KNN) I 2023 7:06 2023-12-05
33 Statistical Learning: 5.1 Cross Validation 14:02 2022-10-07
34 Statistical Learning: 5.2 K-fold Cross Validation 13:34 2022-10-07
35 Statistical Learning: 5.3 Cross Validation the wrong and right way 10:08 2022-10-07
36 Statistical Learning: 5.4 The Bootstrap 11:30 2022-10-07
37 Statistical Learning: 5.5 More on the Bootstrap 14:36 2022-10-07
38 Statistical Learning: 5.Py Cross-Validation I 2023 10:00 2023-12-05
39 Statistical Learning: 5.Py Bootstrap I 2023 5:34 2023-12-05
40 Statistical Learning: 6.1 Introduction and Best Subset Selection 13:45 2022-10-07
41 Statistical Learning: 6.2 Stepwise Selection 12:27 2022-10-07
42 Statistical Learning: 6.3 Backward stepwise selection 5:27 2022-10-07
43 Statistical Learning: 6.4 Estimating test error 14:07 2022-10-07
44 Statistical Learning: 6.5 Validation and cross validation 8:44 2022-10-07
45 Statistical Learning: 6.6 Shrinkage methods and ridge regression 12:38 2022-10-07
46 Statistical Learning: 6.7 The Lasso 15:22 2022-10-07
47 Statistical Learning: 6.8 Tuning parameter selection 5:28 2022-10-07
48 Statistical Learning: 6.9 Dimension Reduction Methods 4:46 2022-10-07
49 Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares 15:49 2022-10-07
50 Statistical Learning: 6.Py Stepwise Regression I 2023 14:06 2023-12-05
51 Statistical Learning: 6.Py Ridge Regression and the Lasso I 2023 19:09 2023-12-05
52 Statistical Learning: 7.1 Polynomials and Step Functions 15:00 2022-10-07
53 Statistical Learning: 7.2 Piecewise Polynomials and Splines 13:14 2022-10-07
54 Statistical Learning: 7.3 Smoothing Splines 10:11 2022-10-07
55 Statistical Learning: 7.4 Generalized Additive Models and Local Regression 10:46 2022-10-07
56 Statistical Learning: 7.Py Polynomial Regressions and Step Functions I 2023 8:19 2023-12-05
57 Statistical Learning: 7.Py Splines I 2023 3:12 2023-12-05
58 Statistical Learning: 7.Py Generalized Additive Models (GAMs) I 2023 8:15 2023-12-05
59 Statistical Learning: 8.1 Tree based methods 14:38 2022-10-07
60 Statistical Learning: 8.2 More details on Trees 11:46 2022-10-07
61 Statistical Learning: 8.3 Classification Trees 11:01 2022-10-07
62 Statistical Learning: 8.4 Bagging 13:46 2022-10-07
63 Statistical Learning: 8.5 Boosting 12:03 2022-10-07
64 Statistical Learning: 8.6 Bayesian Additive Regression Trees 11:34 2022-10-07
65 Statistical Learning: 8.Py Tree-Based Methods I 2023 15:30 2023-12-05
66 Statistical Learning: 9.1 Optimal Separating Hyperplane 11:36 2022-10-07
67 Statistical Learning: 9.2.Support Vector Classifier 8:05 2022-10-07
68 Statistical Learning: 9.3 Feature Expansion and the SVM 15:05 2022-10-07
69 Statistical Learning: 9.4 Example and Comparison with Logistic Regression 14:48 2022-10-07
70 Statistical Learning: 9.Py Support Vector Machines I 2023 18:58 2023-12-05
71 Statistical Learning: 9.Py ROC Curves I 2023 4:45 2023-12-05
72 Statistical Learning: 10.1 Introduction to Neural Networks 15:31 2022-10-07
73 Statistical Learning: 10.2 Convolutional Neural Networks 17:09 2022-10-07
74 Statistical Learning: 10.3 Document Classification 7:47 2022-10-07
75 Statistical Learning: 10.4 Recurrent Neural Networks 14:45 2022-10-07
76 Statistical Learning: 10.5 Time Series Forecasting 16:51 2022-10-07
77 Statistical Learning: 10.6 Fitting Neural Networks 17:04 2022-10-07
78 Statistical Learning: 10.7 Interpolation and Double Descent 11:12 2022-10-07
79 Statistical Learning: 10.Py Single Layer Model: Hitters Data I 2023 17:12 2023-12-05
80 Statistical Learning: 10.Py Multilayer Model: MNIST Digit Data I 2023 7:49 2023-12-05
81 Statistical Learning: 10.Py Convolutional Neural Network: CIFAR Image Data I 2023 10:13 2023-12-05
82 Statistical Learning: 10.Py Document Classification and Recurrent Neural Networks I 2023 9:31 2023-12-05
83 Statistical Learning: 11.1 Introduction to Survival Data and Censoring 14:11 2022-10-07
84 Statistical Learning: 11.2 Proportional Hazards Model 14:32 2022-10-07
85 Statistical Learning: 11.3 Estimation of Cox Model with Examples 13:43 2022-10-07
86 Statistical Learning: 11.4 Model Evaluation and Further Topics 6:13 2022-10-07
87 Statistical Learning: 11.Py Cox Model: Brain Cancer Data I 2023 16:52 2023-12-05
88 Statistical Learning: 11.Py Cox Model: Publication Data I 2023 4:59 2023-12-05
89 Statistical Learning: 12.1 Principal Components 12:37 2022-10-07
90 Statistical Learning: 12.2 Higher order principal components 17:40 2022-10-07
91 Statistical Learning: 12.3 k means Clustering 17:18 2022-10-07
92 Statistical Learning: 12.4 Hierarchical Clustering 14:46 2022-10-07
93 Statistical Learning: 12.5 Matrix Completion 15:52 2022-10-07
94 Statistical Learning: 12.6 Breast Cancer Example 9:25 2022-10-07
95 Statistical Learning: 12.Py Principal Components I 2023 11:27 2023-12-05
96 Statistical Learning: 12.Py Clustering I 2023 11:22 2023-12-05
97 Statistical Learning: 12.Py Application: NCI60 Data I 2023 12:26 2023-12-05
98 Statistical Learning: 13.1 Introduction to Hypothesis Testing 14:31 2022-10-07
99 Statistical Learning: 13.1 Introduction to Hypothesis Testing II 10:18 2022-10-07
100 Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate 12:31 2022-10-07
101 Statistical Learning: 13.3 Bonferroni Method for Controlling FWER 6:32 2022-10-07
102 Statistical Learning: 13.4 Holm's Method for Controlling FWER 5:57 2022-10-07
103 Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method 11:14 2022-10-07
104 Statistical Learning: 13.6 Resampling Approaches 3:21 2022-10-07
105 Statistical Learning: 13.6 Resampling Approaches II 7:44 2022-10-07
106 Statistical Learning: 13.Py Multiple Testing I 2023 17:01 2023-12-05
107 Statistical Learning: 13.Py False Discovery Rate I 2023 6:09 2023-12-05
108 Statistical Learning: 13.Py Multiple Testing and Resampling I 2023 6:12 2023-12-05

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