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Statistical Learning Course – Regression, Classification & Model Selection
Statistical Learning: 10.4 Recurrent Neural Networks

Statistical Learning: 10.4 Recurrent Neural Networks

Statistical Learning Course – Regression, Classification & Model Selection

محتوى الكورس

جميع الدروس
104 درس
18:19 Statistical Learning: 1.1 Opening Remarks
02:19 Statistical Learning: 8 Years Later (Second Edition of the Course)
12:13 Statistical Learning: 1.2 Examples and Framework
11:42 Statistical Learning: 2.1 Introduction to Regression Models
11:41 Statistical Learning: 2.2 Dimensionality and Structured Models
10:05 Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
15:38 Statistical Learning: 2.4 Classification
14:13 Statistical Learning: 2.R Introduction to R
13:02 Statistical Learning: 3.1 Simple linear regression
08:25 Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
15:38 Statistical Learning: 3.3 Multiple Linear Regression
14:52 Statistical Learning: 3.4 Some important questions
14:17 Statistical Learning: 3.5 Extensions of the Linear Model
22:11 Statistical Learning: 3.R Regression in R
10:26 Statistical Learning: 4.1 Introduction to Classification Problems
09:08 Statistical Learning: 4.2 Logistic Regression
09:54 Statistical Learning: 4.3 Multivariate Logistic Regression
07:29 Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
07:13 Statistical Learning: 4.5 Discriminant Analysis
07:38 Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
17:43 Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
09:35 Statistical Learning: 4.8 Generalized Linear Models
10:08 Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
10:15 Statistical Learning: 4.R.1 Logistic Regression
08:23 Statistical Learning: 4.R.2 Linear Discriminant Analysis
05:02 Statistical Learning: 4.R.3 Nearest Neighbor Classification
14:02 Statistical Learning: 5.1 Cross Validation
13:34 Statistical Learning: 5.2 K-fold Cross Validation
10:08 Statistical Learning: 5.3 Cross Validation the wrong and right way
11:30 Statistical Learning: 5.4 The Bootstrap
14:36 Statistical Learning: 5.5 More on the Bootstrap
11:22 Statistical Learning: 5.R.1 Cross Validation
07:41 Statistical Learning: 5.R.2 Bootstrap
13:45 Statistical Learning: 6.1 Introduction and Best Subset Selection
12:27 Statistical Learning: 6.2 Stepwise Selection
05:27 Statistical Learning: 6.3 Backward stepwise selection
14:07 Statistical Learning: 6.4 Estimating test error
08:44 Statistical Learning: 6.5 Validation and cross validation
12:38 Statistical Learning: 6.6 Shrinkage methods and ridge regression
15:22 Statistical Learning: 6.7 The Lasso
05:28 Statistical Learning: 6.8 Tuning parameter selection
04:46 Statistical Learning: 6.9 Dimension Reduction Methods
15:49 Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
10:37 Statistical Learning: 6.R.1 Markdown in RStudio and Best Subset Regression
10:33 Statistical Learning: 6.R.2 Forward Stepwise Regression
05:33 Statistical Learning: 6.R.3 Model Selection and Cross-Validation
16:35 Statistical Learning: 6.R.4 Ridge Regression and Lasso
15:00 Statistical Learning: 7.1 Polynomials and Step Functions
13:14 Statistical Learning: 7.2 Piecewise Polynomials and Splines
10:11 Statistical Learning: 7.3 Smoothing Splines
10:46 Statistical Learning: 7.4 Generalized Additive Models and Local Regression
21:12 Statistical Learning: 7.R.1 Polynomials in GLMs
12:16 Statistical Learning: 7.R.2 Splines and GAMs
14:38 Statistical Learning: 8.1 Tree based methods
11:46 Statistical Learning: 8.2 More details on Trees
11:01 Statistical Learning: 8.3 Classification Trees
13:46 Statistical Learning: 8.4 Bagging
12:03 Statistical Learning: 8.5 Boosting
11:34 Statistical Learning: 8.6 Bayesian Additive Regression Trees
10:14 Statistical Learning: 8.R.1 Fitting Trees
15:36 Statistical Learning: 8.R.2 Random Forests and Boosting
11:36 Statistical Learning: 9.1 Optimal Separating Hyperplane
08:05 Statistical Learning: 9.2.Support Vector Classifier
15:05 Statistical Learning: 9.3 Feature Expansion and the SVM
14:48 Statistical Learning: 9.4 Example and Comparison with Logistic Regression
10:14 Statistical Learning: 9.R.1 Support Vector Classifier
07:55 Statistical Learning: 9.R.2 Nonlinear Support Vector Machine
15:31 Statistical Learning: 10.1 Introduction to Neural Networks
17:09 Statistical Learning: 10.2 Convolutional Neural Networks
07:47 Statistical Learning: 10.3 Document Classification
14:45 Statistical Learning: 10.4 Recurrent Neural Networks
16:51 Statistical Learning: 10.5 Time Series Forecasting
17:04 Statistical Learning: 10.6 Fitting Neural Networks
11:12 Statistical Learning: 10.7 Interpolation and Double Descent
29:34 Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data
13:20 Statistical Learning: 10.R.2 Convolutional Neural Networks in R
08:28 Statistical Learning: 10.R.3 Document Classification
15:05 Statistical Learning: 10.R.4 Recurrent Neural Networks
14:11 Statistical Learning: 11.1 Introduction to Survival Data and Censoring
14:32 Statistical Learning: 11.2 Proportional Hazards Model
13:43 Statistical Learning: 11.3 Estimation of Cox Model with Examples
06:13 Statistical Learning: 11.4 Model Evaluation and Further Topics
12:19 Statistical Learning: 11.R.1 Survival Curves Brain Cancer Data
04:21 Statistical Learning: 11.R.2 Cox Models I Publication Data
07:58 Statistical Learning: 11.R.3 Cox Models II Call Center Data
12:37 Statistical Learning: 12.1 Principal Components
17:40 Statistical Learning: 12.2 Higher order principal components
17:18 Statistical Learning: 12.3 k means Clustering
14:46 Statistical Learning: 12.4 Hierarchical Clustering
15:52 Statistical Learning: 12.5 Matrix Completion
09:25 Statistical Learning: 12.6 Breast Cancer Example
06:29 Statistical Learning: 12.R.1 Principal Components
06:32 Statistical Learning: 12.R.2 K means Clustering
06:34 Statistical Learning: 12.R.3 Hierarchical Clustering
14:31 Statistical Learning: 13.1 Introduction to Hypothesis Testing
10:18 Statistical Learning: 13.1 Introduction to Hypothesis Testing II
12:31 Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
06:32 Statistical Learning: 13.3 Bonferroni Method for Controlling FWER
05:57 Statistical Learning: 13.4 Holm's Method for Controlling FWER
11:14 Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method
03:21 Statistical Learning: 13.6 Resampling Approaches
07:44 Statistical Learning: 13.6 Resampling Approaches II
11:45 Statistical Learning: 13.R.1 Bonferroni and Holm
13:50 Statistical Learning: 13.R.1 Bonferroni and Holm II