• 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 2: Supervised Learning

Supervised Learning

• Contrast a training set and validation set; explain appropriate uses of both
• Use a decision tree for regression tasks
• Explain underfitting and overfitting
• Implement basic numerical computing operations using industry-standard array computing libraries

Contents