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[FreeCoursesOnline.Me] [Stone River ELearning] Math For Machine Learning - [FCO]
TORRENT SUMMARY
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Author : Richard Han Publisher[b/]: Stone River eLearning [b]Release Date : May 2018 ISBN : 200000006A0200 Language : English Torrent Contains : 79 Files Course Source : https://www.oreilly.com/library/view/math-for-machine/200000006A0200/
Video Description
Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science Data Science Artificial Intelligence If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. You intend to pursue a masters degree or PhD, and machine learning is a required or recommended subject. Why you should choose this instructor: I earned my PhD in Mathematics from the University of California, Riverside. I have created many successful online math courses that students around the world have found invaluable—courses in linear algebra, discrete math, and calculus.
Table of Contents
• Course Promo • Introduction • Linear Regression • Linear Discriminant Analysis • Logistic Regression • Artificial Neural Networks • Maximal Margin Classifier • Support Vector Classifier • Support Vector Machine Classifier.
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FILE LIST
Filename
Size
01.Course Promo.mp4
4.7 MB
02.Course Introduction.mp4
5.2 MB
03.Linear Regression.mp4
11 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 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 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 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 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 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
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