• Unit 1: Introduction
• Unit 2: Data
• Unit 3: Data and Ethics
• Unit 4: Models
• Unit 5: Learning and Classification
• Unit 6: Recap and Regularization
• Unit 7: Learned Representations (Embeddings)
• Unit 8: NLP Intro
• Unit 9: NLP Modeling
• Unit 10: Transformers
• Unit 11: Generation
• Unit 12: Review, Reinforcement Learning
• Unit 13: Human-Centered AI

Unit 3: Data and Ethics

Students who complete this unit will demonstrate that they can:

• Identify ethical issues pertaining to the collection and use of data in AI systems
• Explain examples of social impacts of AI systems in wide use today
• Explain examples of biases in AI systems.
• Explain the importance of evaluating image classifiers on unseen data.

Preparation

• Finish reading Deep Learning for Coders chapter 3 (open in Colab). Complete the prep quiz in Moodle.
  • Recommended: watch FastAI Course Lecture 5 on this material.
• Next week’s chapter is dense, so I highly recommend you get a head start on Preparation 4.

Note: chapter 4 material has been delayed by a week. To keep links working, I haven’t renamed this unit in the URL.

Contents

• Monday class: AI Questions, Lab (Mon Jan 24)
• Discussion 3: Facial Recognition (Wed Jan 26)
• Wednesday class: Discussion, Model Evaluation, Loss Functions (Wed Jan 26)
• Week 3 Feedback (Thu Jan 27)
• Week 3 Q&A (Thu Jan 27)
• Friday class: Evaluation (Fri Jan 28)
• Homework 3: Generalization (Fri Jan 28)

Due this Week