From e8fd7daf5f1691bafcd589f2438c68acbd4e9577 Mon Sep 17 00:00:00 2001 From: Amanul Rahiman Shamshuddin Attar Date: Wed, 21 Jun 2023 11:23:21 -0500 Subject: [PATCH] Update README.md Hyperlink will be work as expected --- assignments/assignment1/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/assignments/assignment1/README.md b/assignments/assignment1/README.md index abaaf0a3..dd8ad185 100644 --- a/assignments/assignment1/README.md +++ b/assignments/assignment1/README.md @@ -122,7 +122,7 @@ For the first classifier, implement a `LogisticRegression` class similar to how ## 2.2 Linear Discriminant Analysis -The second model you will explore in this assignment is Linear Discriminant Analysis. Implement both a `fit` and `predict` method following the details [https://dillhoffaj.utasites.cloud/posts/linear_discriminant_analysis](described here.) +The second model you will explore in this assignment is Linear Discriminant Analysis. Implement both a `fit` and `predict` method following the details [https://dillhoffaj.utasites.cloud/posts/linear_discriminant_analysis] (described here.) The parameter update equations were derived via Maximum Likelihood Estimation and can be estimated directly from the data. You do not need to create a covariance matrix for each class. Instead, use a shared covariance matrix which is computed as