Math For Machine Learning

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This course is designed for those interested to learn mathematical concepts that are used in data science, computer science and artificial intelligence.

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About This Courseintermediate

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 master’s 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, I cover the core concepts such as:

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

After taking this course, you will feel carefree and confident. I will break it all down into bite-sized no-brainer chunks. I explain each definition and go through each example step by step so that you understand each topic clearly. Practice problems are provided for you, and detailed solutions are also provided to check your understanding.

Our Promise to You

By the end of this course, you will have learned about mathematical concepts that are used in data science, computer science and artificial intelligence.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about math for machine learning.

Course Curriculum

Course Sections

Introduction Lecture

Linear Regression

The Least Squares Method

Linear Algebra Solution To Least Squares Problem

Example: Linear Regression

Summary: Linear Regression

Problem Set: Linear Regression

Solution Set: Linear Regression


Linear Discriminant Analysis

The Posterior Probability Functions

Modelling The Posterior Probability Functions

Linear Discriminant Functions

Estimating The Linear Discriminant Functions

Classifying Data Points Using Linear Discriminant Functions

LDA Example 1

LDA Example 2

Summary: Linear Discriminant Analysis

Problem Set: Linear Discriminant Analysis

Solution Set: Linear Discriminant Analysis

Logistic Regression

Logistic Regression Model Of The Posterior Probability Function

Estimating The Posterior Probability Function

The Multivariate Newton-Raphson Method

Maximizing The Log-Likelihood Function

Example: Logistic Regression

Summary: Logistic Regression

Problem Set: Logistic Regression

Solution Set: Logistic Regression

Artificial Neural Networks

Neural Network Model Of The Output Functions

Forward Propagation

Choosing Activation Functions

Estimating The Output Functions

Error Function For Regression

Error Function For Binary Classification

Error Function For Multi-class Classification

Minimizing The Error Function Using Gradient Descent

Backpropagation Equations

Summary Of Backpropagation

Summary: Artificial Neural Networks

Problem Set: Artificial Neural Networks

Solution Set: Artificial Neural Networks

Maximal Margin Classifier

Definitions Of Separating Hyperplane And Margin

Maximizing the Margin

Definition Of Maximal Margin Classifier

Reformulating The Optimization Problem

Solving The Convex Optimization Problem

KKT Conditions

Primal And Dual Problems

Solving The Dual Problem

The Coefficients For The Maximal Margin Hyperplane

The Support Vectors

Classifying Test Points

Maximal Margin Classifier Example 1

Maximal Margin Classifier Example 2

Summary: Maximal Margin Classifier

Problem Set: Maximal Margin Classifier

Solution Set: Maximal Margin Classifier

Support Vector Classifier

Slack Variables: Points On Correct Side Of Hyperplane

Slack Variables: Points On Wrong Side Of Hyperplane

Formulating The Optimization Problem

Definition Of Support Vector Classifier

A Convex Optimization Problem

Solving The Convex Optimization Problem (Soft Margin)

The Coefficients For The Soft Margin Hyperplane

The Support Vectors (Soft Margin)

Classifying Test Points (Soft Margin)

Support Vector Classifier Example 1

Support Vector Classifier Example 2

Summary: Support Vector Classifier

Problem Set: Support Vector Classifier

Solution Set: Support Vector Classifier

Support Vector Machine Classifier

Enlarging The Feature Space

The Kernel Trick

Summary: Support Vector Machine Classifier

Concluding Letter


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About The Instructor


My name is Richard Han. I earned my PhD in Mathematics from the University of California, Riverside and am a successful online course creator.