I am currently working at the MIT Media Lab on a project studying news around the world. New graduate students in the Media Lab take a pass/fail course that tours them through the various labs, and my lab (Civic Media) covers how to build software.
I had the opportunity to give a short talk on
- What is machine learning?
- How does machine learning go right?
- How does machine learning go wrong?
- How can machine learning be incorporated into software projects?
This was an ambitious amount to cover in 45 minutes, but I approached it with a tour of scikit-learn for “traditional” machine learning, spacy for NLP, and keras for deep learning. I used a Jupyter notebook with working demos instead of slides or board to emphasize how easy open source software makes implementing “working” machine learning. You can find this notebook here.
I also opened and concluded with a tiny working demo of a natural language processing app, which doubled as instructions for running the notebook, and references for students to use afterwards. This site can be found here.