Module 1: Ethics and AI

This module covers ethical considerations and challenges in AI.

Learning Objectives

1. Name the generally accepted principles which ought to inform one's research (Beneficence, Justice, Respect for Persons, Respect for Law & Public Interest)
2. Compare and contrast the ethical frameworks of Consequentialism and Deontology
3. Apply ethical principles while collecting and curating data
4. Recognize the inherent complexity of ethical thinking and the need to balance competing interests

Module 2: Introduction to Python Workshop

The vast majority of hands-on artificial intelligence work is currently done using the Python programming language. As such, *some* understanding of Python, in particular, and computer programming, in general, is needed to be successful in applying AI. That said, it is truly astounding how much complex AI research can be accomplished with a few lines of code!

Module 3: Reproducibility with git and GitHub.com

AI is powered by code--Python code, run in Jupyter Notebooks in our case.

As a best practice, code should reside in a version control system. These systems allow collaboration, enforce good coding practices and foster collaboration and sharing. The most widely used version control system today is git. Git is free and open source.

In addition to using git, it is possible to use one of many online git hosting sites. For Practicum AI we use GitHub.com. There are other options, but GitHub is one of the leading online hosting companies and offers educational discounts for students and faculty.

This module uses GitHub for the content itself. To start the training, you need a free GitHub.com account. Please register at: https://github.com/.

Then click Accept this assignment and follow the directions.

Module 4: Deep Learning Foundations

This series introduces learners to the foundations of deep learning.