# Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course

arXiv: https://arxiv.org/abs/2107.13671 · HTML: https://mlsanity.com/paper/2107.13671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Students Overfit to Overfitting](https://mlsanity.com/paper/2209.03032/) (2022)

## Abstract (arXiv)

Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources. While conventional lectures provide students with important information and knowledge, we also believe that additional project-based learning components can motivate students to engage in topics more deeply. In addition to incorporating project-based learning in our courses, we aim to develop project-based learning components aligned with real-world tasks, including experimental design and execution, report writing, oral presentation, and peer-reviewing. This paper describes the organization of our project-based machine learning courses with a particular emphasis on the class project components and shares our resources with instructors who would like to include similar elements in their courses.