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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
Students who complete this unit will demonstrate that they can:
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
Preparation 2
(draft!)
The content may not be revised for this year. If you really want to see it, click the link above.
Homework 1: Train and evaluate a classifier on your own images
(draft!)
The content may not be revised for this year. If you really want to see it, click the link above.