• Unit 1: Introduction
• Unit 2: Supervised Learning
• Unit 3: ML Fundamentals
• Unit 4: Neural Models
• Unit 5: Embeddings
• Unit 6: Generalization
• Unit 7: Vision and Perspectives
• 376 Unit 1: Introduction to Generative Modeling
• 376 Unit 2: Language Modeling
• 376 Unit 3: Architectures
• Neural Computation
• 376 Unit 4: Generation and Prompt Engineering
• ML Systems
• 376 Unit 5: Multimodal Models and Diffusion
• 376 Unit 6: Miscellaneous Topics
• Learning Machines
• Context and Implications
• Review

Learning Machines

Key questions

• 375:
  • How can systems improve from experience?
  • What can be learned from data vs interaction?
  • How can we evaluate learning: does it generalize?
• 376:
  • How can we learn without labeled data? (self-supervised learning)
  • How do foundation models learn generalizable patterns from massive datasets?
  • How can generative agents learn to improve their behavior from feedback?

Key objectives

After this course, I will be able to:

• 375:
  • Learning Theory
    • I can explain how different ML approaches (supervised, unsupervised/self-supervised, reinforcement) learn.
    • I can explain how stochastic gradient descent uses data to improve performance.
    • I can describe the relationship between loss functions and metrics.
  • Implementation and Debugging
    • I apply data validation techniques without having to be reminded to do so.
    • I can diagnose problems in model training, such as overfitting or underfitting, from metrics.
    • I can implement a basic training loop in PyTorch (also in ML Engineering)
• 376:
  • I can explain how next-token prediction implicitly encodes many other linguistic and reasoning tasks.
  • I can explain how supervised tuning and feedback tuning can improve the performance and reliability of a model.

Materials

• Slides

Contents