From 5f361ae5feddf4652f6ae4563254dd4e8b1706a4 Mon Sep 17 00:00:00 2001 From: Scott Henderson Date: Tue, 28 Jan 2025 17:21:33 +0100 Subject: [PATCH] explicit time allocated to in-class practice. NOTE: is 20 min correct? --- JOSE_PAPER/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSE_PAPER/paper.md b/JOSE_PAPER/paper.md index a408911..8fc2756 100644 --- a/JOSE_PAPER/paper.md +++ b/JOSE_PAPER/paper.md @@ -188,7 +188,7 @@ The course is structured to provide a balanced and engaging learning experience, Weekly student participation includes presenting summaries of scientific papers or webinars, with an emphasis on selecting five presentations per week to encourage peer learning and collaborative discussions. The overall course is organized into three major pillars: 1/3 on data curation, 1/3 on classic machine learning methods, and 1/3 on deep learning techniques. Assignments, mostly tackled in groups, align with these pillars and culminate in a final group project that integrates all learned components. Additionally, an extra homework assignment helps assess individual learning outcomes, ensuring that students achieve a comprehensive understanding of the materials. -Students are provided ample opportunities to practice during class, fostering collaborative problem-solving and real-time feedback. The course is well-suited for remote delivery with its reliance on digital tools like Jupyter notebooks, GitHub, and cloud computing platforms. However, successful remote implementation requires additional teaching assistants (TAs) and breakout room support to address diverse student needs effectively. This interactive and flexible approach ensures that students are well-prepared to tackle complex geoscientific problems using modern machine learning techniques. +Students are provided at least 20 minutes to practice during class, fostering collaborative problem-solving and real-time feedback. The course is well-suited for remote delivery with its reliance on digital tools like Jupyter notebooks, GitHub, and cloud computing platforms. However, successful remote implementation requires additional teaching assistants (TAs) and breakout room support to address diverse student needs effectively. This interactive and flexible approach ensures that students are well-prepared to tackle complex geoscientific problems using modern machine learning techniques. # Teaching experience