bugs

morea/home.md

@@ -39,11 +39,11 @@ do. By design the course is open ended, where you have the option of
 going "ahead of lectures", so to speak, and I am always available to help
 you when you do so. Each module typically has:
 
-  * [Prerequisites](/prerequisites), describing skills you should have prior to starting the module.
-  * [Learning outcomes](/outcomes), describing the major goals for the module.
-  * [Readings and other online resources](/readings), providing background material.
-  * [Experiential learning activities](/experiences), providing a structured set of challenges enabling you to acquire mastery of material.
-  * [Assessments](/assessments), which help you determine if you have acquired mastery of the material.
+  * [Prerequisites](https://uhm-descartes.github.io/ee445/prerequisites), describing skills you should have prior to starting the module.
+  * [Learning outcomes](https://uhm-descartes.github.io/ee445/outcomes), describing the major goals for the module.
+  * [Readings and other online resources](https://uhm-descartes.github.io/ee445/readings), providing background material.
+  * [Experiential learning activities](https://uhm-descartes.github.io/ee445/experiences), providing a structured set of challenges enabling you to acquire mastery of material.
+  * [Assessments](https://uhm-descartes.github.io/ee445/assessments), which help you determine if you have acquired mastery of the material.
 
 ## About the instructor
 

morea/home.md~

@@ -0,0 +1,50 @@
+---
+title: "Home"
+morea_id: home
+morea_type: home
+published: true
+---
+
+## Welcome to EE 445, Spring 2023
+
+<div class="alert alert-danger" role="alert" markdown="1">
+
+  <i class="fa-solid fa-circle-exclamation fa-xl"></i> **Warning: material has not been migrated, you may find demo fillers instead of EE 445 material.**
+  <hr/>
+
+  The course material is transitioning into the Morea Framework. 
+
+  See the <a href="https://morea-framework.github.io">Morea Framework Project Site</a> for details.
+</div>
+
+EE 445 is an undergraduate level introduction to Machine Learning for
+Electrical and Computer Engineering students. It augments your base in
+probability and linear algebra (and to some extent related engineering
+concepts), and leverages this foundation to provide a comprehensive
+introduction to machine learning fundamentals.
+
+## Who should take this course
+
+This course is intended for undergraduates in ECE/Math, or graduate
+students in ICS, Economics, Math, and Business with a basic working
+knowledge of python, probability and linear algebra.
+
+## Pedagogy
+
+EE 445 is structured as a series of [modules](/modules), each taking
+approximately 1-2 weeks to complete. Each module has material we will
+cover in class (usually the harder or the more important parts), and
+will often have supplementary material that you are encouraged to
+do. By design the course is open ended, where you have the option of
+going "ahead of lectures", so to speak, and I am always available to help
+you when you do so. Each module typically has:
+
+  * [Prerequisites](/prerequisites), describing skills you should have prior to starting the module.
+  * [Learning outcomes](/outcomes), describing the major goals for the module.
+  * [Readings and other online resources](/readings), providing background material.
+  * [Experiential learning activities](/experiences), providing a structured set of challenges enabling you to acquire mastery of material.
+  * [Assessments](/assessments), which help you determine if you have acquired mastery of the material.
+
+## About the instructor
+
+[Narayana Prasad Santhanam](https://ee.hawaii.edu/faculty/profile?usr=63) is a Professor of Electrical and Computer Engineering at the University of Hawaii. My research interests are at the intersection of machine learning, information theory and statistics. A particular focus is on high dimensional and complex problems, that are not amenable to traditional statistical methods and guarantees.

morea/neural-networks/experience-auto.md

@@ -9,25 +9,25 @@ morea_labels:
 ---
 
 First review the section about eigenvalues and eigenspaces
-[here]. Recall that \({\mathbb R}^p\) represents the linear space of
-all vectors with \(p\) real coordinates. For a matrix \(n\times p\)
-matrix \(X\), one can use spectral decomposition of \(X^TX\)
-(respectively \(XX^T\)) to find an orthonormal basis for \({\mathbb
-R}^p\) (respectively \({\mathbb R}^n\)) using eigenvectors of \(X^TX\)
-(respectively \(XX^T\)), and therefore for the rows (respectively
-columns) of \(X\). Assume that \(n \ge p\), and let the eigenvalues
-of \(X^TX\) be \(\lambda_1\ge \lambda_2 \cdots \ge \lambda_p\), then
-the highest \(p\) eigenvalues of \(XX^T\) are also
-\(\lambda_1, \lambda_2 \upto \lambda_p\), while the remaining \(n-p\)
+[here]. Recall that \\({\mathbb R}^p\\) represents the linear space of
+all vectors with \\(p\\) real coordinates. For a matrix \\(n\times p\\)
+matrix \\(X\\), one can use spectral decomposition of \\(X^TX\\)
+(respectively \\(XX^T\\)) to find an orthonormal basis for \\({\mathbb
+R}^p\\) (respectively \\({\mathbb R}^n\\)) using eigenvectors of \\(X^TX\\)
+(respectively \\(XX^T\\)), and therefore for the rows (respectively
+columns) of \\(X\\). Assume that \\(n \ge p\\), and let the eigenvalues
+of \\(X^TX\\) be \\(\lambda_1\ge \lambda_2 \cdots \ge \lambda_p\\), then
+the highest \\(p\\) eigenvalues of \\(XX^T\\) are also
+\\(\lambda_1, \lambda_2 \upto \lambda_p\\), while the remaining \\(n-p\\)
 eigenvalues are all 0. 
 
 
-Let \(V\) (respectively \(U\)) be the matrix formed
+Let \\(V\\) (respectively \\(U\\)) be the matrix formed
 by placing as columns the orthonormal basis obtained by the
-eigendecomposition of \(X^TX\) (respectively \(XX^T\)).
-Let \(\Sigma\)
-be the \(n\times p\) matrix formed with the positive square roots of
-the eigenvalues of \(X^TX\) in all the diagonal locations.
+eigendecomposition of \\(X^TX\\) (respectively \\(XX^T\\)).
+Let \\(\Sigma\\)
+be the \\(n\times p\\) matrix formed with the positive square roots of
+the eigenvalues of \\(X^TX\\) in all the diagonal locations.
 The singular value decomposition observes that
 $$ X = U \Sigma V^T. $$