• 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

Unit 3: ML Fundamentals

Students who complete this unit will demonstrate that they can:

Fundamentals of Machine Learning

• Describe the basic structure of a machine learning model.
• Describe the overall approach of Stochastic Gradient Descent: how does it use information from a batch of data to improve its performance on that and other data?
• Describe the parameters of a linear layer and how they are used to compute its output.
• Identify the following loss functions: Mean Squared Error and Mean Absolute Error.

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