What is the “A.I. ABCs”?
The A.I. ABCs is a workshop that surveys the many AI/ML tools available for analyzing structured data. The workshop is targeted at researchers with some programming experience but modest exposure to advanced math, stats, and computation.
Who is the intended audience?
This workshop is intended for researchers at all levels including students, post-docs, and principal investigators. Participants are expected to have some experience with the following tools:
- Python. Advanced knowledge is not required, but at a minimum students should be comfortable with Python syntax and familiar with NumPy and Pandas. Participants can learn these skills from resources on the preparation page.
- git/GitHub. Expertise with git is not required, but you must have git installed, and you should be familiar with basic git commands.
- Docker. Expertise with Docker is not required, but you must have Docker installed.
No advanced mathematical, statistical, or computational training is required for the course, which will focus on high-level explanations and intuitive demonstrations rather than theory. Some knowledge of linear algebra is helpful but not required.
Lesson Repository
The GitHub repository for the workshop can be used to run the demonstrations and tutorials.
About this Workshop
Around the turn of the millenium, computational methods began to gain real traction in academic work. This trend started in the sciences and engineering and gradually infiltrating most fields of study. Today, it is difficult to complete masters or doctoral degree in most academic subjects without engaging in computational methods of some kind. In short, the once niche methods of computer science proved to be so powerful that they came to occupy a critical role.
Similarly, artificial intelligence (AI) and machine learning (ML) have, since the early 2020s, increasingly become not just powerful computational techniques but one of the dominant classes of techniques used in many fields of study. Although many scholarly projects can still proceed without AI and ML, these norms are changing rapidly.
As basic computational methods spread through academia in the early 2000s, in part due to the speed with which the methods spread, most traditional academic departments were (and remain) slow to offer computational training to their students—even today, many non-computational departments do not offer a meaningful track for their students to learn scientific programming. As AI and ML inexorably make their way through contemporary academia, this same pattern is repeating: AI and ML are more and more important, but opportunities to learn about them, especially for those without deep training in computational theory, remain rare.
This workshop seeks to give researchers with some programming experience but limited exposure to advanced math, stats, and computation an opportunity to survey the many AI/ML tools available for analyzing structured data. To do this, the workshop will focus on high-level explanations of the tools that provide intuition without dwelling on theory. Many tools spanning a wide range of strategies will be demonstrated and discussed. The goal of the course is to get students past the initial struggles of learning a new field such that they feel confident finding and navigating the mant public tutorials and lessons that already exist in the AI/ML space.
Please see the Program for more information on topics that will be covered in the workshop. Please Register if you wish to attend.
