# Math for Machine Learning [NO LABS]

## Learn the core topics of Machine Learning to open doors to data science and artificial intelligence.

*NEW!*

## Course Curriculum

- 0301 Classification (1:15)
- 0302 Linear Discriminant Analysis (0:44)
- 0303 The Posterior Probability Functions (3:42)
- 0304 Modelling the Posterior Probability Functions (7:13)
- 0305 Linear Discriminant Functions (5:32)
- 0306 Estimating the Linear Discriminant Functions (6:00)
- 0307 Classifying Data Points Using Linear Discriminant Functions (3:09)
- 0308 LDA Example 1 (13:52)
- 0309 LDA Example 2 (17:38)
- 0310 Summary Linear Discriminant Analysis (1:34)

- 0401 Logistic Regression (1:15)
- 0402 Logistic Regression Model of the Posterior Probability Function (3:02)
- 0403 Estimating the Posterior Probability Function (8:57)
- 0404 The Multivariate Newton-Raphson Method (9:14)
- 0405 Maximizing the Log-Likelihood Function (13:51)
- 0406 Logistic Regression Example (9:55)
- 0407 Summary Logistic Regression (1:21)

- 0501 Artificial Neural Networks (0:36)
- 0502 Neural Network Model of the Output Functions (12:59)
- 0503 Forward Propagation (0:51)
- 0504 Choosing Activation Functions (4:30)
- 0505 Estimating the Output Functions (2:17)
- 0506 Error Function for Regression (2:27)
- 0507 Error Function for Binary Classification (6:15)
- 0508 Error Function for Multiclass Classification (4:38)
- 0509 Minimizing the Error Function Using Gradient Descent (6:27)
- 0510 Backpropagation Equations (4:16)
- 0511 Summary of Backpropagation (1:27)
- 0512 Summary Artificial Neural Networks (1:47)

- 0601 Maximal Margin Classifier (2:29)
- 0602 Definitions of Separating Hyperplane and Margin (5:43)
- 0603 Proof 1 (6:42)
- 0604 Maximizing the Margin (3:36)
- 0605 Definition of Maximal Margin Classifier (1:01)
- 0606 Reformulating the Optimization Problem (7:37)
- 0607 Proof 2 (1:13)
- 0608 Proof 3 (4:52)
- 0609 Proof 4 (8:41)
- 0610 Proof 5 (5:10)
- 0611 Solving the Convex Optimization Problem (1:05)
- 0612 KKT Conditions (1:24)
- 0613 Primal and Dual Problems (1:24)
- 0614 Solving the Dual Problem (3:31)
- 0615 The Coefficients for the Maximal Margin Hyperplane (0:29)
- 0616 Classifying Test Points (1:50)
- 0617 The Support Vectors (0:58)
- 0618 Maximal Margin Classifier Example 1 (9:50)
- 0619 Maximal Margin Classifier Example 2 (11:41)
- 0620 Summary Maximal Margin Classifier (0:31)

- 0701 Support Vector Classifier (3:54)
- 0702 Slack Variables Points on Correct Side of Hyperplane (3:47)
- 0703 Slack Variables Points on Wrong Side of Hyperplane (1:37)
- 0704 Formulating the Optimization Problem (3:52)
- 0705 Definition of Support Vector Classifier (0:44)
- 0706A Convex Optimization Problem (1:46)
- 0707 Solving the Convex Optimization Problem (Soft Margin) (6:38)
- 0708 The Coefficients for the Soft Margin Hyperplane (2:09)
- 0709 Classifying Test Points (Soft Margin) (1:36)
- 0710 The Support Vectors (Soft Margin) (1:37)
- 0711 Support Vector Classifier Example 1 (14:53)
- 0712 Support Vector Classifier Example 2 (9:19)
- 0713 Summary Support Vector Classifier (0:41)

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.

In this course, we will cover the core concepts such as:

- Linear Regression
- Linear Discriminant Analysis
- Logistic Regression
- Artificial Neural Networks
- Support Vector Machines