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Math for Machine Learning

Name: Math for Machine Learning
Rating: 3.9 (85 reviews)

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

Created byRichard Han

Last updated 8/2020

English

What you'll learn

Refresh your machine learning knowledge.
Apply fundamental techniques of machine learning.
Gain a firm foundation in machine learning for furthering your career.
Learn a subject crucial for data science and artificial intelligence.

Course content

9 sections • 81 lectures • 5h 14m total length

Introduction2:46

Linear Regression7:32
Students will learn about the notion of residual sum of squares.
The Least Squares Method11:25
Students will learn how to apply the least squares method to solve the least squares problem.
Linear Algebra Solution to Least Squares Problem12:50
Students will learn about a linear algebra approach to solving the least squares problem.
Example: Linear Regression4:05
An example of applying the least squares method is provided.
Summary: Linear Regression0:34
A summary of linear regression is provided.
Problem Set: Linear Regression0:01
Practice Problems for Linear Regression are provided.
Solution Set: Linear Regression0:01
Solutions are provided for Problem Set: Linear Regression.

Classification1:15
Students will be introduced to classification problems.
Linear Discriminant Analysis0:44
The method of linear discriminant analysis is introduced.
The Posterior Probability Functions3:42
In this lecture, we build a formula for the posterior probability.
Modelling the Posterior Probability Functions7:13
In this lecture, we model the posterior probability functions.
Linear Discriminant Functions5:31
Students will learn what linear discriminant functions are.
Estimating the Linear Discriminant Functions6:00
In this lecture, we estimate the linear discriminant functions.
Classifying Data Points Using Linear Discriminant Functions3:09
Students will learn how to classify data points using linear discriminant functions.
LDA Example 113:51
Students will see an example of applying linear discriminant analysis.
LDA Example 217:38
Another example of applying linear discriminant analysis is provided.
Summary: Linear Discriminant Analysis1:34
A summary of linear discriminant analysis is provided.
Problem Set: Linear Discriminant Analysis0:01
Practice problems for Linear Discriminant Analysis are provided.
Solution Set: Linear Discriminant Analysis0:01
Solutions are provided for Problem Set: Linear Discriminant Analysis.

Logistic Regression1:15
The method of logistic regression is introduced.
Logistic Regression Model of the Posterior Probability Function3:02
In this lecture, we model the posterior probability function.
Estimating the Posterior Probability Function8:57
In this lecture, we introduce a strategy for estimating the posterior probability function.
The Multivariate Newton-Raphson Method9:14
Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.
Maximizing the Log-Likelihood Function13:51
In this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.
Example: Logistic Regression9:55
Students will learn how to apply logistic regression to solve a classification problem.
Summary: Logistic Regression1:21
A summary of logistic regression is provided.
Problem Set: Logistic Regression0:01
Practice problems for Logistic Regression are provided.
Solution Set: Logistic Regression0:01
Solutions are provided for Problem Set: Logistic Regression

Artificial Neural Networks0:35
An introduction to artificial neural networks is provided.
Neural Network Model of the Output Functions12:59
In this lecture, we build a neural network model for the output functions using a neural network diagram.
Forward Propagation0:51
The notion of forward propagation is discussed.
Choosing Activation Functions4:30
Students will learn which activation functions to choose for each type of problem.
Estimating the Output Functions2:17
We introduce a strategy for estimating the output functions.
Error Function for Regression2:27
Students will learn which error function to use for regression problems.
Error Function for Binary Classification6:16
Students will learn which error function to use for binary classification problems.
Error Function for Multi-class Classification4:38
Students will learn which error function to use for multi-class classification problems.
Minimizing the Error Function using Gradient Descent6:27
Students will learn how gradient descent is used to minimize the error function.
Backpropagation Equations4:16
Students will learn how the backpropagation equations are used to help find the gradient of the error function.
Summary of Backpropagation1:27
A summary of backpropagation is provided.
Summary: Artificial Neural Networks1:48
A summary of artificial neural networks is provided.
Problem Set: Artificial Neural Networks0:01
Practice problems for Artificial Neural Networks are provided.
Solution Set: Artificial Neural Networks0:01
Solutions are provided for Problem Set: Artificial Neural Networks

Maximal Margin Classifier2:29
An introduction to maximal margin classifier, support vector classifier, and support vector machine is provided.
Definitions of Separating Hyperplane and Margin5:43
In this lecture, we provide definitions of separating hyperplane and margin.
Maximizing the Margin3:36
in this lecture, we formulate a maximization problem.
Definition of Maximal Margin Classifier1:01
The maximal margin classifier is defined.
Reformulating the Optimization Problem7:36
In this lecture, we reformulate the maximization problem as a convex optimization problem.
Solving the Convex Optimization Problem1:05
We introduce a strategy for solving the optimization problem.
KKT Conditions1:24
Students will learn what the KKT conditions are.
Primal and Dual Problems1:24
Students will learn what the primal and dual problems are.
Solving the Dual Problem3:31
In this lecture, we solve the dual problem.
The Coefficients for the Maximal Margin Hyperplane0:29
Students will learn how to solve for the coefficients for the maximal margin hyperplane.
The Support Vectors0:58
We define what support vectors are.
Classifying Test Points1:50
Students will learn how to classify test points.
Maximal Margin Classifier Example 19:50
An example of applying the maximal margin classifier to solve a classification problem is provided.
Maximal Margin Classifier Example 211:41
A second example of applying the maximal margin classifier is provided.
Summary: Maximal Margin Classifier0:31
A summary of the maximal margin classifier is provided.
Problem Set: Maximal Margin Classifier0:01
Practice problems for Maximal Margin Classifier are provided.
Solution Set: Maximal Margin Classifier0:01
Solutions are provided for Problem Set: Maximal Margin Classifier

Support Vector Classifier3:54
An introduction to the support vector classifier is provided.
Slack Variables: Points on Correct Side of Hyperplane3:47
We characterize points on the correct side of the hyperplane using slack variables.
Slack Variables: Points on Wrong Side of Hyperplane1:37
We characterize points on the wrong side of the hyperplane using slack variables.
Formulating the Optimization Problem3:52
We formulate the optimization problem for the support vector classifier.
Definition of Support Vector Classifier0:44
We define the support vector classifier.
A Convex Optimization Problem1:46
We identify the optimization problem as a convex optimization problem.
Solving the Convex Optimization Problem (Soft Margin)6:37
Students will learn how to solve the convex optimization problem using Lagrange multipliers.
The Coefficients for the Soft Margin Hyperplane2:08
Students will learn how to find the coefficients for the soft margin hyperplane.
The Support Vectors (Soft Margin)1:37
We define the support vectors for the support vector classifier.
Classifying Test Points (Soft Margin)1:36
Students will learn how to classify test points using the support vector classifier.
Support Vector Classifier Example 114:53
Students will see how the support vector classifier works in a simple but specific example.
Support Vector Classifier Example 29:19
Students will see how the support vector classifier works in a second simple but specific example.
Summary: Support Vector Classifier0:41
We review the support vector classifier.
Problem Set: Support Vector Classifier0:01
Practice problems for Support Vector Classifier are provided.
Solution Set: Support Vector Classifier0:01
Solutions are provided for Problem Set: Support Vector Classifier

Support Vector Machine Classifier1:19
An introduction to the support vector machine classifier is provided.
Enlarging the Feature Space5:22
Students will see how the support vector machine basically works.
The Kernel Trick4:24
Students will learn how the support vector machine classifier works by using the kernel trick.
Summary: Support Vector Machine Classifier1:07
We review the support vector machine classifier.

Requirements

Linear algebra, probability, multivariable calculus

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.

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 CARE-FREE 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. I will also be AVAILABLE TO ANSWER ANY QUESTIONS you might have on the lecture material or any other questions you are struggling with.

Practice problems are provided for you, and detailed solutions are also provided to check your understanding.

30 day full refund if not satisfied.

Grab a cup of coffee and start listening to the first lecture. I, and your peers, are here to help. We're waiting for your insights and questions! Enroll now!

Who this course is for:

Working Professionals
Anyone interested in gaining mastery of machine learning
Data Scientists
AI professionals
Adult Learners
College Students

Math for Machine Learning

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Linear Regression7 lectures • 36min

Linear Discriminant Analysis12 lectures • 1hr 1min

Logistic Regression9 lectures • 48min

Artificial Neural Networks14 lectures • 49min

Maximal Margin Classifier17 lectures • 53min

Support Vector Classifier15 lectures • 53min

Support Vector Machine Classifier4 lectures • 12min

Concluding Letter2 lectures • 1min

Requirements

Description

Who this course is for: