• 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

ML Systems

Key questions

• 375:
  • What are the inputs to and outputs of AI systems?
  • What abstractions do systems provide, and how can they compose? (ML APIs)
  • How do we evaluate ML solutions?
• 376:
  • How do we evaluate language models?
  • Can I run an LLM on my laptop? Can I train one?

Key objectives

After this course, I will be able to:

• 375:
  • APIs and systems
    • I can create a computational notebook that includes code, execution results, section headings, and formatted textual explanations.
    • I can write code that loads data, preprocesses it, and feeds it to a supervised ML model using a sklearn-style fit-predict API.
    • I can select appropriate loss functions and metrics for a given task (and thus choose an appropriate model type/structure). Specifically, I can distinguish between regression and classification problems, even if classification targets happen to be encoded as numbers.
    • I can integrate an ML model into a larger application.
  • Experimentation and Evaluation
    • I can design, run, and analyze empirical experiments to quantify the impact of hyperparameter changes on model performance.
    • I can make and interpret plots of relevant evaluation metrics.
    • I can identify hyperparameters that can be adjusted to improve the performance of a model.
• 376:
  • I can solve basic language and reasoning tasks using commercially deployed LLM APIs.
  • I can apply and critically analyze evaluation strategies for generative models.
  • I can describe the overall process of training a state-of-the-art dialogue LLM such as Llama or OLMo.
  • I can analyze the computational requirements of training and inference of generative AI systems.

Learning Sequence

• I’ve trained a classifier (using code already provided)
  • basic image clf notebook
• I’ve called a LLM API
  • llm library
• I’ve designed a prompt for an LLM
• I’ve used a high-level API to run a neural network model
• I’ve generated:
  • An array of class probabilities
  • A chatbot response
  • A next-token prediction
  • An image
• I’ve computed the similarity between two embeddings and compared that with another pairwise similarity.
• I’ve used the sklearn (fit-predict) API
• I’ve evaluated the performance of a regression model and a classification model using appropriate metrics.

Materials

• Slides

Contents