AI & machine learning

What this course develops

Move from probability and linear models through trees, neural networks, representation learning, evaluation, and responsible deployment.

• Probability
• Classical ML
• Neural nets
• Deep learning
• Reinforcement learning
• AI systems

Scope

Math → models → training → evaluation

Public reading index

• Begin with the decision
• Operate the model as a system
• Separate fit from generalization
• Make launch a reversible claim
• Learn useful representations
• Learn from actions and delayed outcomes