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Instructor
Richard Han, Instructor - Math for Machine Learning: Open Doors to Great Careers

Richard Han

Earned his Ph.D in Mathematics from the University of California, Riverside & has created many successful online math courses like: linear algebra, discrete math, and calculus

Instructor: Richard Han

Learn the core topics of Machine Learning for Data Science and AI.

  • Learn the core topics of Machine Learning for Data Science and AI.
  • This course will put you in a better position to pursue a Masters or Ph.D degree in Machine Learning and Data Science.
  • Instructor earned his Ph.D in Mathematics from the University of California, Riverside & has created many successful math courses.

Course Description

In this course, we cover core topics such as: • Linear Regression • Linear Discriminant Analysis • Logistic Regression • Artificial Neural Networks • Support Vector Machines

What am I going to get from this course?

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.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Linear algebra, multivariable calculus, probability.
Any computational software.  E.g. Mathematica.

Who should take this course? Who should not?

Anyone interested in gaining mastery of machine learning.
Data scientists.
AI professionals.

Curriculum

Module 1: Introduction

Lecture 1 Introduction

Module 2: Linear Regression

Lecture 2 Linear Regression

Students will learn about the notion of residual sum of squares.

Lecture 3 The Least Squares Method

Students will learn how to apply the least squares method to solve the least squares problem.

Lecture 4 Linear Algebra Solution to Least Squares Problem

Students will learn about a linear algebra approach to solving the least squares problem.

Lecture 5 Example: Linear Regression

An example of applying the least squares method is provided.

Lecture 6 Summary: Linear Regression

A summary of linear regression is provided.

Lecture 7 Problem Set: Linear Regression

Practice Problems for Linear Regression are provided.

Lecture 8 Solution Set: Linear Regression

Solutions are provided for Problem Set: Linear Regression.

Module 3: Linear Discriminant Analysis

Lecture 9 Classification

Students will be introduced to classification problems.

Lecture 10 Linear Discriminant Analysis

The method of linear discriminant analysis is introduced.

Lecture 11 The Posterior Probability Functions

In this lecture, we build a formula for the posterior probability.

Lecture 12 Modelling the Posterior Probability Functions

In this lecture, we model the posterior probability functions.

Lecture 13 Linear Discriminant Functions

Students will learn what linear discriminant functions are.

Lecture 14 Estimating the Linear Discriminant Functions

In this lecture, we estimate the linear discriminant functions.

Lecture 15 Classifying Data Points Using Linear Discriminant Functions

Students will learn how to classify data points using linear discriminant functions.

Lecture 16 LDA Example 1

Students will see an example of applying linear discriminant analysis.

Lecture 17 LDA Example 2

Another example of applying linear discriminant analysis is provided.

Lecture 18 Summary: Linear Discriminant Analysis

A summary of linear discriminant analysis is provided.

Lecture 19 Problem Set: Linear Discriminant Analysis

Practice problems for Linear Discriminant Analysis are provided.

Lecture 20 Solution Set: Linear Discriminant Analysis

Solutions are provided for Problem Set: Linear Discriminant Analysis.

Module 4: Logistic Regression

Lecture 21 Logistic Regression

The method of logistic regression is introduced.

Lecture 22 Logistic Regression Model of the Posterior Probability Function

In this lecture, we model the posterior probability function.

Lecture 23 Estimating the Posterior Probability Function

In this lecture, we introduce a strategy for estimating the posterior probability function.

Lecture 24 The Multivariate Newton-Raphson Method

Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.

Lecture 25 Maximizing the Log-Likelihood Function

In this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.

Lecture 26 Example: Logistic Regression

Students will learn how to apply logistic regression to solve a classification problem.

Lecture 27 Summary: Logistic Regression

A summary of logistic regression is provided.

Lecture 28 Problem Set: Logistic Regression

Practice problems for Logistic Regression are provided.

Lecture 29 Solution Set: Logistic Regression

Solutions are provided for Problem Set: Logistic Regression

Module 5: Artificial Neural Networks

Lecture 30 Artificial Neural Networks

An introduction to artificial neural networks is provided.

Lecture 31 Neural Network Model of the Output Functions

In this lecture, we build a neural network model for the output functions using a neural network diagram.

Lecture 32 Forward Propagation

The notion of forward propagation is discussed.

Lecture 33 Choosing Activation Functions

Students will learn which activation functions to choose for each type of problem.

Lecture 34 Estimating the Output Functions

We introduce a strategy for estimating the output functions.

Lecture 35 Error Function for Regression

Students will learn which error function to use for regression problems.

Lecture 36 Error Function for Binary Classification

Students will learn which error function to use for binary classification problems.

Lecture 37 Error Function for Multi-class Classification

Students will learn which error function to use for multi-class classification problems.

Lecture 38 Minimizing the Error Function Using Gradient Descent

Students will learn how gradient descent is used to minimize the error function.

Lecture 39 Backpropagation Equations

Students will learn how the backpropagation equations are used to help find the gradient of the error function.

Lecture 40 Summary of Backpropagation

A summary of backpropagation is provided.

Lecture 41 Summary: Artificial Neural Networks

A summary of artificial neural networks is provided.

Lecture 42 Problem Set: Artificial Neural Networks

Practice problems for Artificial Neural Networks are provided.

Lecture 43 Solution Set: Artificial Neural Networks

Solutions are provided for Problem Set: Artificial Neural Networks

Module 6: Maximal Margin Classifier

Lecture 44 Maximal Margin Classifier

An introduction to maximal margin classifier, support vector classifier, and support vector machine is provided.

Lecture 45 Definitions of Separating Hyperplane and Margin

In this lecture, we provide definitions of separating hyperplane and margin.

Lecture 46 Maximizing the Margin

in this lecture, we formulate a maximization problem.

Lecture 47 Definition of Maximal Margin Classifier

The maximal margin classifier is defined.

Lecture 48 Reformulating the Optimization Problem

In this lecture, we reformulate the maximization problem as a convex optimization problem.

Lecture 49 Solving the Convex Optimization Problem

We introduce a strategy for solving the optimization problem.

Lecture 50 KKT Conditions

Students will learn what the KKT conditions are.

Lecture 51 Primal and Dual Problems

Students will learn what the primal and dual problems are.

Lecture 52 Solving the Dual Problem

In this lecture, we solve the dual problem.

Lecture 53 The Coefficients for the Maximal Margin Hyperplane

Students will learn how to solve for the coefficients for the maximal margin hyperplane.

Lecture 54 The Support Vectors

We define what support vectors are.

Lecture 55 Classifying Test Points

Students will learn how to classify test points.

Lecture 56 Maximal Margin Classifier Example 1

An example of applying the maximal margin classifier to solve a classification problem is provided.

Lecture 57 Maximal Margin Classifier Example 2

A second example of applying the maximal margin classifier is provided.

Lecture 58 Summary: Maximal Margin Classifier

A summary of the maximal margin classifier is provided.

Lecture 59 Problem Set: Maximal Margin Classifier

Practice problems for Maximal Margin Classifier are provided.

Lecture 60 Solution Set: Maximal Margin Classifier

Solutions are provided for Problem Set: Maximal Margin Classifier

Module 7: Support Vector Classifier

Lecture 61 Support Vector Classifier

An introduction to the support vector classifier is provided.

Lecture 62 Slack Variables: Points on Correct Side of Hyperplane

We characterize points on the correct side of the hyperplane using slack variables.

Lecture 63 Slack Variables: Points on Wrong Side of Hyperplane

We characterize points on the wrong side of the hyperplane using slack variables.

Lecture 64 Formulating the Optimization Problem

We formulate the optimization problem for the support vector classifier.

Lecture 65 Definition of Support Vector Classifier

We define the support vector classifier.

Lecture 66 A Convex Optimization Problem

We identify the optimization problem as a convex optimization problem.

Lecture 67 Solving the Convex Optimization Problem (Soft Margin)

Students will learn how to solve the convex optimization problem using Lagrange multipliers.

Lecture 68 The Coefficients for the Soft Margin Hyperplane

Students will learn how to find the coefficients for the soft margin hyperplane.

Lecture 69 The Support Vectors (Soft Margin)

We define the support vectors for the support vector classifier.

Lecture 70 Classifying Test Points (Soft Margin)

Students will learn how to classify test points using the support vector classifier.

Lecture 71 Support Vector Classifier Example 1

Students will see how the support vector classifier works in a simple but specific example.

Lecture 72 Support Vector Classifier Example 2

Students will see how the support vector classifier works in a second simple but specific example.

Lecture 73 Summary: Support Vector Classifier

We review the support vector classifier.

Lecture 74 Problem Set: Support Vector Classifier

Practice problems for Support Vector Classifier are provided.

Lecture 75 Solution Set: Support Vector Classifier

Solutions are provided for Problem Set: Support Vector Classifier

Module 8: Support Vector Machine Classifier

Lecture 76 Support Vector Machine Classifier

An introduction to the support vector machine classifier is provided.

Lecture 77 Enlarging the Feature Space

Students will see how the support vector machine basically works.

Lecture 78 The Kernel Trick

Students will learn how the support vector machine classifier works by using the kernel trick.

Lecture 79 Summary: Support Vector Machine Classifier

We review the support vector machine classifier.

Module 9: Conclusion

Lecture 80 Concluding Letter
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