• 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 1: Introduction

Students who complete this unit will demonstrate that they can:

• Contrast contemporary ML-based numerical AI with classical rule-based symbolic AI
• Describe the overall approach of how modern AI learns from data
• Identify some of the major milestones in the history of AI.
• Implement basic numerical computing operations using PyTorch
• Implement a simple image classifier on a pre-wrangled dataset using fast.ai.

Preparation

• Read Deep Learning for Coders chapter 1 (open in Colab)
• Watch FastAI Course Lecture 1
• optional: Watch Lecture 1 of MIT 6.S191 Deep Learning

Contents

• Class Meeting: Introduction (Mon Jan 10)
• Class Meeting: History of AI (KVL) (Wed Jan 12)
• Discussion 1: Examples of AI (Wed Jan 12)
• Lab 1: Warmup (Fri Jan 14)

Due this Week