[Stone River eLearning] Math for Machine Learning - [FCO]

Files:

• 01.Course Promo.mp4 (4.7 MB)
• 02.Course Introduction.mp4 (5.2 MB)
• 03.Linear Regression.mp4 (11.0 MB)
• 04.The Least Squares Method.mp4 (17.5 MB)
• 05.Linear Algebra Solution to Least Squares Problem.mp4 (17.9 MB)
• 06.Example Linear Regression.mp4 (6.0 MB)
• 07.Summary Linear Regression.mp4 (1.6 MB)
• 08.Classification.mp4 (1.7 MB)
• 09.Linear Discriminant Analysis.mp4 (967.1 KB)
• 10.The Posterior Probability Functions.mp4 (5.3 MB)
• 11.Modelling the Posterior Probability Functions.mp4 (11.3 MB)
• 12.Linear Discriminant Functions.mp4 (8.4 MB)
• 13.Estimating the Linear Discriminant Functions.mp4 (8.6 MB)
• 14.Classifying Data Points Using Linear Discriminant Functions.mp4 (4.9 MB)
• 15.LDA Example 1.mp4 (20.2 MB)
• 16.LDA Example 2.mp4 (26.9 MB)
• 17.Summary Linear Discriminant Analysis.mp4 (4.8 MB)
• 18.Logistic Regression.mp4 (1.6 MB)
• 19.Logistic Regression Model of the Posterior Probability Function.mp4 (4.2 MB)
• 20.Estimating the Posterior Probability Function.mp4 (12.9 MB)
• 21.The Multivariate Newton-Raphson Method.mp4 (16.6 MB)
• 22.Maximizing the Log-Likelihood Function.mp4 (21.4 MB)
• 23.Logistic Regression Example.mp4 (14.3 MB)
• 24.Summary Logistic Regression.mp4 (3.7 MB)
• 25.Artificial Neural Networks.mp4 (778.6 KB)
• 26.Neural Network Model of the Output Functions.mp4 (18.8 MB)
• 27.Forward Propagation.mp4 (1.6 MB)
• 28.Choosing Activation Functions.mp4 (5.9 MB)
• 29.Estimating the Output Functions.mp4 (3.0 MB)
• 30.Error Function for Regression.mp4 (3.3 MB)
• 31.Error Function for Binary Classification.mp4 (8.1 MB)
• 32.Error Function for Multiclass Classification.mp4 (6.3 MB)
• 33.Minimizing the Error Function Using Gradient Descent.mp4 (9.2 MB)
• 34.Backpropagation Equations.mp4 (6.1 MB)
• 35.Summary of Backpropagation.mp4 (2.3 MB)
• 36.Summary Artificial Neural Networks.mp4 (5.0 MB)
• 37.Maximal Margin Classifier.mp4 (3.1 MB)
• 38.Definitions of Separating Hyperplane and Margin.mp4 (8.4 MB)
• 39.Proof 1.mp4 (10.8 MB)
• 40.Maximizing the Margin.mp4 (5.3 MB)
• 41.Definition of Maximal Margin Classifier.mp4 (1.5 MB)
• 42.Reformulating the Optimization Problem.mp4 (12.2 MB)
• 43.Proof 2.mp4 (1.8 MB)
• 44.Proof 3.mp4 (7.3 MB)
• 45.Proof 4.mp4 (13.0 MB)
• 46.Proof 5.mp4 (8.1 MB)
• 47.Solving the Convex Optimization Problem.mp4 (1.7 MB)
• 48.KKT Conditions.mp4 (2.7 MB)
• 49.Primal and Dual Problems.mp4 (2.1 MB)
• 50.Solving the Dual Problem.mp4 (4.8 MB)
• 51.The Coefficients for the Maximal Margin Hyperplane.mp4 (677.0 KB)
• 52.The Support Vectors.mp4 (1.3 MB)
• 53.Classifying Test Points.mp4 (2.5 MB)
• 54.Maximal Margin Classifier Example 1.mp4 (14.4 MB)
• 55.Maximal Margin Classifier Example 2.mp4 (16.7 MB)
• 56.Summary Maximal Margin Classifier.mp4 (1.6 MB)
• 57.Support Vector Classifier.mp4 (5.3 MB)
• 58.Slack Variables Points on Correct Side of Hyperplane.mp4 (5.5 MB)
• 59.Slack Variables Points on Wrong Side of Hyperplane.mp4 (2.2 MB)
• 60.Formulating the Optimization Problem.mp4 (5.5 MB)
• 61.Definition of Support Vector Classifier.mp4 (1.2 MB)
• 62.A Convex Optimization Problem.mp4 (3.3 MB)
• 63.Solving the Convex Optimization Problem (Soft Margin).mp4 (9.4 MB)
• 64.The Coefficients for the Soft Margin Hyperplane.mp4 (2.9 MB)
• 65.Classifying Test Points (Soft Margin).mp4 (2.4 MB)
• 66.The Support Vectors (Soft Margin).mp4 (2.3 MB)
• 67.Support Vector Classifier Example 1.mp4 (22.1 MB)
• 68.Support Vector Classifier Example 2.mp4 (14.3 MB)
• 69.Summary Support Vector Classifier.mp4 (1.9 MB)
• 70.Support Vector Machine Classifier.mp4 (1.7 MB)
• 71.Enlarging the Feature Space.mp4 (8.2 MB)
• 72.The Kernel Trick.mp4 (6.9 MB)
• 73.Summary Support Vector Machine Classifier.mp4 (3.4 MB)
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• How you can help Team-FTU.txt (0.3 KB)
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