2019 Syllabus and Course Schedule

All course materials and instructions for running Jupyter notebooks can be found on the class GitHub.

EventDateDescriptionMaterials and Assignments
Unit 1: The Basics
Week 1 4/2 Lecture Topics:
    1. Survey of AI for good applications
    2. Gender bias in word vectors
    3. Linear, logistic models
    4. Machine learning basics
Handouts
Week 2 4/9 Guest lecture: Chris Piech Handouts
Unit 2: Introduction to Neural Networks
Week 3 4/16 Lecture Topics:
    1. Training an accidentally sexist model
    2. Neural networks
    3. Backpropagation and stochastic gradient descent
Handouts
Week 4 4/23 Guest lecture: Stefano Ermon
Unit 3: Deep Dive into Neural Networks
Week 5 4/30 Lecture Topics:
    1. Predicting COMPAS recidivism scores
    2. Deep neural networks
    3. Keras
Handouts
Week 6 5/7 Guest lecture: Margaret Mitchell Handouts
Unit 4: Convolutional Neural Networks
Week 7 5/14 Lecture Topics:
    1. Computer vision applications for social good
    2. Convolutional neural networks
Handouts
Week 8 5/21 Guest lecture: Timnit Gebru Handouts
Unit 5: Recurrent Neural Networks
Week 9 5/28 Lecture Topics:
    1. NLP applications for social good
    2. Recurrent neural networks
Handouts
Week 10 6/4 Guest lecture: Robert Munro