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Math for Machine Learning

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

  1. Course Promo 00:02:42
  2. Introduction
    1. Course Introduction 00:02:46
  3. Linear Regression
    1. Linear Regression 00:07:33
    2. The Least Squares Method 00:11:25
    3. Linear Algebra Solution to Least Squares Problem 00:12:51
    4. Example Linear Regression 00:04:05
    5. Summary Linear Regression 00:00:34
  4. Linear Discriminant Analysis
    1. Classification 00:01:15
    2. Linear Discriminant Analysis 00:00:44
    3. The Posterior Probability Functions 00:03:43
    4. Modelling the Posterior Probability Functions 00:07:13
    5. Linear Discriminant Functions 00:05:32
    6. Estimating the Linear Discriminant Functions 00:06:00
    7. Classifying Data Points Using Linear Discriminant Functions 00:03:09
    8. LDA Example 1 00:13:52
    9. LDA Example 2 00:17:38
    10. Summary Linear Discriminant Analysis 00:01:34
  5. Logistic Regression
    1. Logistic Regression 00:01:16
    2. Logistic Regression Model of the Posterior Probability Function 00:03:02
    3. Estimating the Posterior Probability Function 00:08:57
    4. The Multivariate Newton-Raphson Method 00:09:14
    5. Maximizing the Log-Likelihood Function 00:13:52
    6. Logistic Regression Example 00:09:55
    7. Summary Logistic Regression 00:01:21
  6. Artificial Neural Networks
    1. Artificial Neural Networks 00:00:36
    2. Neural Network Model of the Output Functions 00:13:00
    3. Forward Propagation 00:00:51
    4. Choosing Activation Functions 00:04:30
    5. Estimating the Output Functions 00:02:17
    6. Error Function for Regression 00:02:27
    7. Error Function for Binary Classification 00:06:16
    8. Error Function for Multiclass Classification 00:04:38
    9. Minimizing the Error Function Using Gradient Descent 00:06:27
    10. Backpropagation Equations 00:04:17
    11. Summary of Backpropagation 00:01:27
    12. Summary Artificial Neural Networks 00:01:48
  7. Maximal Margin Classifier
    1. Maximal Margin Classifier 00:02:30
    2. Definitions of Separating Hyperplane and Margin 00:05:44
    3. Proof 1 00:06:43
    4. Maximizing the Margin 00:03:36
    5. Definition of Maximal Margin Classifier 00:01:02
    6. Reformulating the Optimization Problem 00:07:37
    7. Proof 2 00:01:14
    8. Proof 3 00:04:52
    9. Proof 4 00:08:41
    10. Proof 5 00:05:11
    11. Solving the Convex Optimization Problem 00:01:06
    12. KKT Conditions 00:01:25
    13. Primal and Dual Problems 00:01:25
    14. Solving the Dual Problem 00:03:31
    15. The Coefficients for the Maximal Margin Hyperplane 00:00:30
    16. The Support Vectors 00:00:58
    17. Classifying Test Points 00:01:51
    18. Maximal Margin Classifier Example 1 00:09:50
    19. Maximal Margin Classifier Example 2 00:11:41
    20. Summary Maximal Margin Classifier 00:00:31
  8. Support Vector Classifier
    1. Support Vector Classifier 00:03:54
    2. Slack Variables Points on Correct Side of Hyperplane 00:03:47
    3. Slack Variables Points on Wrong Side of Hyperplane 00:01:38
    4. Formulating the Optimization Problem 00:03:53
    5. Definition of Support Vector Classifier 00:00:44
    6. A Convex Optimization Problem 00:01:47
    7. Solving the Convex Optimization Problem (Soft Margin) 00:06:38
    8. The Coefficients for the Soft Margin Hyperplane 00:02:09
    9. Classifying Test Points (Soft Margin) 00:01:36
    10. The Support Vectors (Soft Margin) 00:01:37
    11. Support Vector Classifier Example 1 00:14:53
    12. Support Vector Classifier Example 2 00:09:20
    13. Summary Support Vector Classifier 00:00:42
  9. Support Vector Machine Classifier
    1. Support Vector Machine Classifier 00:01:20
    2. Enlarging the Feature Space 00:05:23
    3. The Kernel Trick 00:04:25
    4. Summary Support Vector Machine Classifier 00:01:08