72 (na) mga episode
1. Introduction Lecture (1 of 72 Lectures)
1. Introduction Lecture (1 of 72 Lectures)
An introduction to the course is provided. Practice problems available at onlinemathtraining.
2. Section 1: Linear Regression (First Lecture: Linear Regression)
2. Section 1: Linear Regression (First Lecture: Linear Regression)
Students will learn about the notion of residual sum of squares. Practice problems available at onlinemathtraining.
3. The Least Squares Method
3. The Least Squares Method
Students will learn how to apply the least squares method to solve the least squares problem.
4. Linear Algebra Solution to Least Squares Problem
4. Linear Algebra Solution to Least Squares Problem
Students will learn about a linear algebra approach to solving the least squares problem.
5. Example: Linear Regression
5. Example: Linear Regression
An example of applying the least squares method is provided.
6. Summary: Linear Regression
6. Summary: Linear Regression
A summary of linear regression is provided.
7. Section 2: Linear Discriminant Analysis (First Lecture: Classification)
7. Section 2: Linear Discriminant Analysis (First Lecture: Classification)
Students will be introduced to classification problems. Practice problems available at onlinemathtraining.
8. Linear Discriminant Analysis
8. Linear Discriminant Analysis
The method of linear discriminant analysis is introduced.
9. The Posterior Probability Functions
9. The Posterior Probability Functions
In this lecture, we build a formula for the posterior probability.
10. Modelling the Posterior Probability Functions
10. Modelling the Posterior Probability Functions
In this lecture, we model the posterior probability functions.
11. Linear Discriminant Functions
11. Linear Discriminant Functions
Students will learn what linear discriminant functions are.
12. Estimating the Linear Discriminant Functions
12. Estimating the Linear Discriminant Functions
In this lecture, we estimate the linear discriminant functions.
13. Classifying Data Points Using Linear Discriminant Functions
13. Classifying Data Points Using Linear Discriminant Functions
Students will learn how to classify data points using linear discriminant functions.
14. LDA Example 1
14. LDA Example 1
Students will see an example of applying linear discriminant analysis.
15. LDA Example 2
15. LDA Example 2
Another example of applying linear discriminant analysis is provided.
16. Summary: Linear Discriminant Analysis
16. Summary: Linear Discriminant Analysis
A summary of linear discriminant analysis is provided.
17. Section 3: Logistic Regression (First Lecture: Logistic Regression)
17. Section 3: Logistic Regression (First Lecture: Logistic Regression)
The method of logistic regression is introduced. Practice problems available at onlinemathtraining.
18. Logistic Regression Model of the Posterior Probability Function
18. Logistic Regression Model of the Posterior Probability Function
In this lecture, we model the posterior probability function.
19. Estimating the Posterior Probability Function
19. Estimating the Posterior Probability Function
In this lecture, we introduce a strategy for estimating the posterior probability function.
20. The Multivariate Newton-Raphson Method
20. The Multivariate Newton-Raphson Method
Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.
21. Maximizing the Log-Likelihood Function
21. 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.
22. Example: Logistic Regression
22. Example: Logistic Regression
Students will learn how to apply logistic regression to solve a classification problem.
23. Summary: Logistic Regression
23. Summary: Logistic Regression
A summary of logistic regression is provided.
24. Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
24. Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
An introduction to artificial neural networks is provided. Practice problems available at onlinemathtraining.
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Math for Machine Learning
20181 season
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