Machine Learning 3.2 - Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA)
We will cover classification models in which we estimate the probability distributions for the classes. We can then compute the likelihood of each class for a new observation, and then assign the new observation to the class with the greatest likelihood. These maximum likelihood methods, such as the LDA and QDA methods you will see in this section, are often the best methods to use on data whose classes are well-approximated by standard probability distributions. This material complements pp. 138-149 of An Introduction to Statistical Learning (http://faculty.marshall.usc.edu/garet....
▶︎
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
▶︎
Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
▶︎
Linear discriminant analysis (LDA) - simply explained
▶︎
Expectation Maximization: how it works
▶︎
Introduction to Machine Learning - 06 - Linear discriminant analysis
▶︎
MLDL HW2) The Generative Models: Difference between LDA and QDA
▶︎
16. Learning: Support Vector Machines
▶︎
The EM Algorithm Clearly Explained (Expectation-Maximization Algorithm)
▶︎
ROC and AUC, Clearly Explained!
▶︎
Statistical Learning: 4.5 Discriminant Analysis
▶︎
Video 1: Introduction to Simple Linear Regression
▶︎
What is Linear Discriminant Analysis? | Comprehensive Tutorial | Supervised Learning | Data Science
▶︎
Gradient descent, how neural networks learn | Deep Learning Chapter 2
▶︎
688 S21 Lecture 10 - LDA and QDA
▶︎
6. Singular Value Decomposition (SVD)
▶︎
Stanford CS229 Machine Learning I Gaussian discriminant analysis, Naive Bayes I 2022 I Lecture 5
▶︎
Bayes theorem, the geometry of changing beliefs
▶︎
Linear Regression Analysis | Linear Regression in Python | Machine Learning Algorithms | Simplilearn
▶︎
Principal Component Analysis (PCA)
▶︎