Add teaching

Author: RSTZZZ

_posts/teaching/2023-01-01-comp551-W23.md

@@ -19,7 +19,7 @@ tags:
 * **Course codes:** COMP 551 (Winter 2023)
 * **Instructors:** [Reihaneh Rabbany](http://www.reirab.com/)
 * **Location:** Stewart Biology Building S1/4 (lectures will be recorded)
-* **Time:** Tuesdays and Thursdays, 1:05 pm - 2:25 am
+* **Time:** Tuesdays and Thursdays, 1:05 pm - 2:25 pm
 * **Course Website:** [here](http://www.reirab.com/Teaching/AML23/index.html)
 
 # Overview

_posts/teaching/2024-09-01-comp551-F24.md

@@ -0,0 +1,27 @@
+---
+title:  Applied Machine Learning
+term: Winter 2025
+course_code: COMP 551
+author: Reihaneh Rabbany
+names: Reihaneh Rabbany
+categories: Teaching
+share: false
+last_modified_at: 2024-10-11T00:00:00
+layout: single
+toc: true
+toc_sticky: true
+toc_label: "Table of Contents"
+tags:
+  - Winter 2025
+  - Applied Machine Learning
+---
+
+* **Course codes:** COMP 551 (Winter 2025)
+* **Instructors:** [Reihaneh Rabbany](http://www.reirab.com/)
+* **Location:** Stewart Biology Building S1/4 (lectures will be recorded)
+* **Time:** Tuesdays and Thursdays, 1:05 pm - 2:25 am
+* **Course Website:** [here](http://www.reirab.com/Teaching/AML23/index.html)
+
+# Overview
+
+This course covers a selected set of topics in machine learning and data mining, with an emphasis on good methods and practices for deployment of real systems. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.

_posts/teaching/2024-09-01-comp599-F24.md

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+---
+title: Network Science
+term: Fall 2024
+course_code: COMP 599
+author: Reihaneh Rabbany
+names: Reihaneh Rabbany
+categories: Teaching
+share: false
+last_modified_at: 2024-10-11T00:00:00
+layout: single
+toc: true
+toc_sticky: true
+toc_label: "Table of Contents"
+tags:
+  - Fall 2024
+  - Network Science
+---
+
+* **Course codes:** COMP 599 (Fall 2024)
+* **Instructors:** [Reihaneh Rabbany](http://www.reirab.com/)
+
+
+# Overview
+
+This is on Network Science, Graph Mining and Graph Learning. Networks model relationships in complex systems, from hyperlinks between webpages, and co-authorships between research scholars to biological interactions between proteins and genes, and synaptic links between neurons. Network Science is an interdisciplinary research area involving researchers from Physics, Computer Science, Sociology, Math and Statistics, with applications in a wide range of domains including Biology, Medicine, Political Science, Marketing, Ecology, Criminology, etc. In this course, we will cover the basic concepts and techniques used in Network Science, review the state of the art techniques, and discuss the most recent developments.
+
+**Please note that about half of the class is paper discussions and student presentations.**

_posts/teaching/2025-01-01-comp551-W25.md

@@ -0,0 +1,27 @@
+---
+title:  Applied Machine Learning
+term: Winter 2025
+course_code: COMP 551
+author: Reihaneh Rabbany
+names: Reihaneh Rabbany
+categories: Teaching
+share: false
+last_modified_at: 2024-10-11T00:00:00
+layout: single
+toc: true
+toc_sticky: true
+toc_label: "Table of Contents"
+tags:
+  - Winter 2025
+  - Applied Machine Learning
+---
+
+* **Course codes:** COMP 551 (Winter 2025)
+* **Instructors:** [Reihaneh Rabbany](http://www.reirab.com/)
+* **Location:** Stewart Biology Building S1/4 (lectures will be recorded)
+* **Time:** Tuesdays and Thursdays, 1:05 pm - 2:25 pm
+* **Course Website:** [here](http://www.reirab.com/Teaching/AML23/index.html)
+
+# Overview
+
+This course covers a selected set of topics in machine learning and data mining, with an emphasis on good methods and practices for deployment of real systems. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, support vector machines, clustering, neural networks, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.