This is an introductory course that will give you the ability to learn ML autonomously.
- apply probability theory to the description of ML algorithms
- describe the various frameworks of algorithmic learning (exact, PAC, agnostic)
- analyze the bias-variance tradeoff in light of the information bottleneck principle and double-descent phenomena
- explain how unsupervised, supervised and reinforcement learning algorithms work