**Math for Machine Learning**

English | MP4 | AVC 1280Ã—720 | AAC 44KHz 2ch | 5h 43m | 504 MB

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 invaluableâ€”courses in linear algebra, discrete math, and calculus.

01 Course Promo

02 Course Introduction

03 Linear Regression

04 The Least Squares Method

05 Linear Algebra Solution to Least Squares Problem

06 Example Linear Regression

07 Summary Linear Regression

08 Classification

09 Linear Discriminant Analysis

10 The Posterior Probability Functions

11 Modelling the Posterior Probability Functions

12 Linear Discriminant Functions

13 Estimating the Linear Discriminant Functions

14 Classifying Data Points Using Linear Discriminant Functions

15 LDA Example 1

16 LDA Example 2

17 Summary Linear Discriminant Analysis

18 Logistic Regression

19 Logistic Regression Model of the Posterior Probability Function

20 Estimating the Posterior Probability Function

21 The Multivariate Newton-Raphson Method

22 Maximizing the Log-Likelihood Function

23 Logistic Regression Example

24 Summary Logistic Regression

25 Artificial Neural Networks

26 Neural Network Model of the Output Functions

27 Forward Propagation

28 Choosing Activation Functions

29 Estimating the Output Functions

30 Error Function for Regression

31 Error Function for Binary Classification

32 Error Function for Multiclass Classification

33 Minimizing the Error Function Using Gradient Descent

34 Backpropagation Equations

35 Summary of Backpropagation

36 Summary Artificial Neural Networks

37 Maximal Margin Classifier

38 Definitions of Separating Hyperplane and Margin

39 Proof 1

40 Maximizing the Margin

41 Definition of Maximal Margin Classifier

42 Reformulating the Optimization Problem

43 Proof 2

44 Proof 3

45 Proof 4

46 Proof 5

47 Solving the Convex Optimization Problem

48 KKT Conditions

49 Primal and Dual Problems

50 Solving the Dual Problem

51 The Coefficients for the Maximal Margin Hyperplane

52 The Support Vectors

53 Classifying Test Points

54 Maximal Margin Classifier Example 1

55 Maximal Margin Classifier Example 2

56 Summary Maximal Margin Classifier

57 Support Vector Classifier

58 Slack Variables Points on Correct Side of Hyperplane

59 Slack Variables Points on Wrong Side of Hyperplane

60 Formulating the Optimization Problem

61 Definition of Support Vector Classifier

62 A Convex Optimization Problem

63 Solving the Convex Optimization Problem (Soft Margin)

64 The Coefficients for the Soft Margin Hyperplane

65 Classifying Test Points (Soft Margin)

66 The Support Vectors (Soft Margin)

67 Support Vector Classifier Example 1

68 Support Vector Classifier Example 2

69 Summary Support Vector Classifier

70 Support Vector Machine Classifier

71 Enlarging the Feature Space

72 The Kernel Trick

73 Summary Support Vector Machine Classifier

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