Math for Machine Learning
Where to Watch Math for Machine Learning
72.
Summary: Support Vector Machine Classifier
2018-05-02
We review the support vector machine classifier.
Watch Math for Machine Learning Season 1 Episode 72 Now
71.
The Kernel Trick
2018-05-02
Students will learn how the support vector machine classifier works by using the kernel trick.
Watch Math for Machine Learning Season 1 Episode 71 Now
70.
Enlarging the Feature Space
2018-05-02
Students will see how the support vector machine basically works.
Watch Math for Machine Learning Season 1 Episode 70 Now
69.
Section 7: Support Vector Machine Classifier (First Lecture: Support Vector Machine Classifier)
2018-05-02
An introduction to the support vector machine classifier is provided. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 69 Now
68.
Summary: Support Vector Classifier
2018-05-02
We review the support vector classifier.
Watch Math for Machine Learning Season 1 Episode 68 Now
67.
Support Vector Classifier Example 2
2018-05-02
Students will see how the support vector classifier works in a second simple but specific example.
Watch Math for Machine Learning Season 1 Episode 67 Now
66.
Support Vector Classifier Example 1
2018-05-02
Students will see how the support vector classifier works in a simple but specific example.
Watch Math for Machine Learning Season 1 Episode 66 Now
65.
Classifying Test Points (Soft Margin)
2018-05-02
Students will learn how to classify test points using the support vector classifier.
Watch Math for Machine Learning Season 1 Episode 65 Now
64.
The Support Vectors (Soft Margin)
2018-05-02
We define the support vectors for the support vector classifier.
Watch Math for Machine Learning Season 1 Episode 64 Now
63.
The Coefficients for the Soft Margin Hyperplane
2018-05-02
Students will learn how to find the coefficients for the soft margin hyperplane.
Watch Math for Machine Learning Season 1 Episode 63 Now
62.
Solving the Convex Optimization Problem (Soft Margin)
2018-05-02
Students will learn how to solve the convex optimization problem using Lagrange multipliers.
Watch Math for Machine Learning Season 1 Episode 62 Now
61.
A Convex Optimization Problem
2018-05-02
We identify the optimization problem as a convex optimization problem.
Watch Math for Machine Learning Season 1 Episode 61 Now
60.
Definition of Support Vector Classifier
2018-05-02
We define the support vector classifier.
Watch Math for Machine Learning Season 1 Episode 60 Now
59.
Formulating the Optimization Problem
2018-05-02
We formulate the optimization problem for the support vector classifier.
Watch Math for Machine Learning Season 1 Episode 59 Now
58.
Slack Variables: Points on Wrong Side of Hyperplane
2018-05-02
We characterize points on the wrong side of the hyperplane using slack variables.
Watch Math for Machine Learning Season 1 Episode 58 Now
57.
Slack Variables: Points on Correct Side of Hyperplane
2018-05-02
We characterize points on the correct side of the hyperplane using slack variables.
Watch Math for Machine Learning Season 1 Episode 57 Now
56.
Section 6: Support Vector Classifier (First Lecture: Support Vector Classifier)
2018-05-02
An introduction to the support vector classifier is provided. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 56 Now
55.
Summary: Maximal Margin Classifier
2018-05-02
A summary of the maximal margin classifier is provided.
Watch Math for Machine Learning Season 1 Episode 55 Now
54.
Maximal Margin Classifier Example 2
2018-05-02
A second example of applying the maximal margin classifier is provided.
Watch Math for Machine Learning Season 1 Episode 54 Now
53.
Maximal Margin Classifier Example 1
2018-05-02
An example of applying the maximal margin classifier to solve a classification problem is provided.
Watch Math for Machine Learning Season 1 Episode 53 Now
52.
Classifying Test Points
2018-05-02
Students will learn how to classify test points.
Watch Math for Machine Learning Season 1 Episode 52 Now
51.
The Support Vectors
2018-05-02
We define what support vectors are.
Watch Math for Machine Learning Season 1 Episode 51 Now
50.
The Coefficients for the Maximal Margin Hyperplane
2018-05-02
Students will learn how to solve for the coefficients for the maximal margin hyperplane.
Watch Math for Machine Learning Season 1 Episode 50 Now
49.
Solving the Dual Problem
2018-05-02
In this lecture, we solve the dual problem.
Watch Math for Machine Learning Season 1 Episode 49 Now
48.
Primal and Dual Problems
2018-05-02
Students will learn what the primal and dual problems are.
Watch Math for Machine Learning Season 1 Episode 48 Now
47.
KKT Conditions
2018-05-02
Students will learn what the KKT conditions are.
Watch Math for Machine Learning Season 1 Episode 47 Now
46.
Solving the Convex Optimization Problem
2018-05-02
We introduce a strategy for solving the optimization problem.
Watch Math for Machine Learning Season 1 Episode 46 Now
45.
Proof 5 (Optional)
2018-05-02
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Watch Math for Machine Learning Season 1 Episode 45 Now
44.
Proof 4 (Optional)
2018-05-02
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Watch Math for Machine Learning Season 1 Episode 44 Now
43.
Proof 3 (Optional)
2018-05-02
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Watch Math for Machine Learning Season 1 Episode 43 Now
42.
Proof 2 (Optional)
2018-05-02
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Watch Math for Machine Learning Season 1 Episode 42 Now
41.
Reformulating the Optimization Problem
2018-05-02
In this lecture, we reformulate the maximization problem as a convex optimization problem.
Watch Math for Machine Learning Season 1 Episode 41 Now
40.
Definition of Maximal Margin Classifier
2018-05-02
The maximal margin classifier is defined.
Watch Math for Machine Learning Season 1 Episode 40 Now
39.
Maximizing the Margin
2018-05-02
in this lecture, we formulate a maximization problem.
Watch Math for Machine Learning Season 1 Episode 39 Now
38.
Proof 1 (Optional)
2018-05-02
This is a supplementary resource for the lecture titled Definitions of Separating Hyperplane and Margin.
Watch Math for Machine Learning Season 1 Episode 38 Now
37.
Definitions of Separating Hyperplane and Margin
2018-05-02
In this lecture, we provide definitions of separating hyperplane and margin.
Watch Math for Machine Learning Season 1 Episode 37 Now
36.
Section 5: Maximal Margin Classifier (First Lecture: Maximal Margin Classifier)
2018-05-02
An introduction to maximal margin classifier, support vector classifier, and support vector machine is provided. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 36 Now
35.
Summary: Artificial Neural Networks
2018-05-02
A summary of artificial neural networks is provided.
Watch Math for Machine Learning Season 1 Episode 35 Now
34.
Summary of Backpropagation
2018-05-02
A summary of backpropagation is provided.
Watch Math for Machine Learning Season 1 Episode 34 Now
33.
Backpropagation Equations
2018-05-02
Students will learn how the backpropagation equations are used to help find the gradient of the error function.
Watch Math for Machine Learning Season 1 Episode 33 Now
32.
Minimizing the Error Function Using Gradient Descent
2018-05-02
Students will learn how gradient descent is used to minimize the error function.
Watch Math for Machine Learning Season 1 Episode 32 Now
31.
Error Function for Multiclass Classification
2018-05-02
Students will learn which error function to use for multi-class classification problems.
Watch Math for Machine Learning Season 1 Episode 31 Now
30.
Error Function for Binary Classification
2018-05-02
Students will learn which error function to use for binary classification problems.
Watch Math for Machine Learning Season 1 Episode 30 Now
29.
Error Function for Regression
2018-05-02
Students will learn which error function to use for regression problems.
Watch Math for Machine Learning Season 1 Episode 29 Now
28.
Estimating the Output Functions
2018-05-02
We introduce a strategy for estimating the output functions.
Watch Math for Machine Learning Season 1 Episode 28 Now
27.
Choosing Activation Functions
2018-05-02
Students will learn which activation functions to choose for each type of problem.
Watch Math for Machine Learning Season 1 Episode 27 Now
26.
Forward Propagation
2018-05-02
The notion of forward propagation is discussed.
Watch Math for Machine Learning Season 1 Episode 26 Now
25.
Neural Network Model of the Output Functions
2018-05-02
In this lecture, we build a neural network model for the output functions using a neural network diagram.
Watch Math for Machine Learning Season 1 Episode 25 Now
24.
Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
2018-05-02
An introduction to artificial neural networks is provided. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 24 Now
23.
Summary: Logistic Regression
2018-05-02
A summary of logistic regression is provided.
Watch Math for Machine Learning Season 1 Episode 23 Now
22.
Example: Logistic Regression
2018-05-02
Students will learn how to apply logistic regression to solve a classification problem.
Watch Math for Machine Learning Season 1 Episode 22 Now
21.
Maximizing the Log-Likelihood Function
2018-05-02
In this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.
Watch Math for Machine Learning Season 1 Episode 21 Now
20.
The Multivariate Newton-Raphson Method
2018-05-02
Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.
Watch Math for Machine Learning Season 1 Episode 20 Now
19.
Estimating the Posterior Probability Function
2018-05-02
In this lecture, we introduce a strategy for estimating the posterior probability function.
Watch Math for Machine Learning Season 1 Episode 19 Now
18.
Logistic Regression Model of the Posterior Probability Function
2018-05-02
In this lecture, we model the posterior probability function.
Watch Math for Machine Learning Season 1 Episode 18 Now
17.
Section 3: Logistic Regression (First Lecture: Logistic Regression)
2018-05-02
The method of logistic regression is introduced. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 17 Now
16.
Summary: Linear Discriminant Analysis
2018-05-02
A summary of linear discriminant analysis is provided.
Watch Math for Machine Learning Season 1 Episode 16 Now
15.
LDA Example 2
2018-05-02
Another example of applying linear discriminant analysis is provided.
Watch Math for Machine Learning Season 1 Episode 15 Now
14.
LDA Example 1
2018-05-02
Students will see an example of applying linear discriminant analysis.
Watch Math for Machine Learning Season 1 Episode 14 Now
13.
Classifying Data Points Using Linear Discriminant Functions
2018-05-02
Students will learn how to classify data points using linear discriminant functions.
Watch Math for Machine Learning Season 1 Episode 13 Now
12.
Estimating the Linear Discriminant Functions
2018-05-02
In this lecture, we estimate the linear discriminant functions.
Watch Math for Machine Learning Season 1 Episode 12 Now
11.
Linear Discriminant Functions
2018-05-02
Students will learn what linear discriminant functions are.
Watch Math for Machine Learning Season 1 Episode 11 Now
10.
Modelling the Posterior Probability Functions
2018-05-02
In this lecture, we model the posterior probability functions.
Watch Math for Machine Learning Season 1 Episode 10 Now
9.
The Posterior Probability Functions
2018-05-02
In this lecture, we build a formula for the posterior probability.
Watch Math for Machine Learning Season 1 Episode 9 Now
8.
Linear Discriminant Analysis
2018-05-02
The method of linear discriminant analysis is introduced.
Watch Math for Machine Learning Season 1 Episode 8 Now
7.
Section 2: Linear Discriminant Analysis (First Lecture: Classification)
2018-05-02
Students will be introduced to classification problems. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 7 Now
6.
Summary: Linear Regression
2018-05-02
A summary of linear regression is provided.
Watch Math for Machine Learning Season 1 Episode 6 Now
5.
Example: Linear Regression
2018-05-02
An example of applying the least squares method is provided.
Watch Math for Machine Learning Season 1 Episode 5 Now
4.
Linear Algebra Solution to Least Squares Problem
2018-05-02
Students will learn about a linear algebra approach to solving the least squares problem.
Watch Math for Machine Learning Season 1 Episode 4 Now
3.
The Least Squares Method
2018-05-02
Students will learn how to apply the least squares method to solve the least squares problem.
Watch Math for Machine Learning Season 1 Episode 3 Now
2.
Section 1: Linear Regression (First Lecture: Linear Regression)
2018-05-02
Students will learn about the notion of residual sum of squares. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 2 Now
1.
Introduction Lecture (1 of 72 Lectures)
2018-05-02
An introduction to the course is provided. Practice problems available at onlinemathtraining.
Watch Math for Machine Learning Season 1 Episode 1 Now
Math for Machine Learning is a series categorized as a new series. Spanning 1 seasons with a total of 144 episodes, the show debuted on 2018. The series has earned a no reviews from both critics and viewers. The IMDb score stands at undefined.
How to Watch Math for Machine Learning
How can I watch Math for Machine Learning online? Math for Machine Learning is available on Richard Han with seasons and full episodes. You can also watch Math for Machine Learning on demand at Amazon online.
Genres
Channel
Richard Han
Cast
Richard Han
Stream shows like Math for Machine Learning
