4shared - Math for Machine Learning ng Hassan

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02:42

01 - Course Promo.mp4

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02:46

02 - Course Introduction.mp4

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07:33

03 - Linear Regression.mp4

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11:25

04 - The Least Squares Method.mp4

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12:50

05 - Linear Algebra Solution to Least Squares Problem.mp4

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04:05

06 - Example Linear Regression.mp4

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00:34

07 - Summary Linear Regression.mp4

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01:15

08 - Classification.mp4

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00:44

09 - Linear Discriminant Analysis.mp4

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03:42

10 - The Posterior Probability Functions.mp4

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07:13

11 - Modelling the Posterior Probability Functions.mp4

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05:32

12 - Linear Discriminant Functions.mp4

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13 - Estimating the Linear Discriminant Functions.mp4

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03:09

14 - Classifying Data Points Using Linear Discriminant Functions.mp4

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13:52

15 - LDA Example 1.mp4

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16 - LDA Example 2.mp4

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01:34

17 - Summary Linear Discriminant Analysis.mp4

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18 - Logistic Regression.mp4

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19 - Logistic Regression Model of the Posterior Probability Function.mp4

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20 - Estimating the Posterior Probability Function.mp4

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09:14

21 - The Multivariate Newton-Raphson Method.mp4

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13:51

22 - Maximizing the Log-Likelihood Function.mp4

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09:55

23 - Logistic Regression Example.mp4

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01:21

24 - Summary Logistic Regression.mp4

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00:36

25 - Artificial Neural Networks.mp4

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13:00

26 - Neural Network Model of the Output Functions.mp4

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27 - Forward Propagation.mp4

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04:30

28 - Choosing Activation Functions.mp4

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02:17

29 - Estimating the Output Functions.mp4

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02:27

30 - Error Function for Regression.mp4

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